NBER WORKING PAPER SERIES

ON THE RISE OF FINTECHS – CREDIT SCORING USING DIGITAL FOOTPRINTS

Tobias Berg

Valentin Burg

Ana Gombovi 

Manju Puri

Working Paper 24551

http://www.nber.org/papers/w24551

NATIONAL BUREAU OF ECONOMIC RESEARCH

1050 Massachusetts Avenue

Cambridge, MA 02138

April 2018, Revised July 2018

We wish to thank Frank Ecker, Falko Fecht, Christine Laudenbach, Laurence van Lent, Kelly 

Shue (discussant), Sascha Steffen, as well as participants of the 2018 RFS FinTech Conference, 

the 2018 Swiss Winter Conference on Financial Intermediation, and research seminars at Duke 

University, FDIC, and Frankfurt School of Finance & Management for valuable comments and 

suggestions. This work was supported by a grant from FIRM (Frankfurt Institute for Risk 

Management and Regulation). The views expressed herein are those of the authors and do not 

necessarily reflect the views of the National Bureau of Economic Research.

NBER working papers are circulated for discussion and comment purposes. They have not been 

peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies 

official NBER publications.

© 2018 by Tobias Berg, Valentin Burg, Ana Gombovi , and Manju Puri. All rights reserved. 

Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission 

provided that full credit, including © notice, is given to the source.
On the Rise of FinTechs – Credit Scoring using Digital Footprints

Tobias Berg, Valentin Burg, Ana Gombovi , and Manju Puri

NBER Working Paper No. 24551

April 2018, Revised July 2018

JEL No. D12,G20,O33

ABSTRACT

We analyze the information content of the digital footprint – information that people leave online 

simply by accessing or registering on a website – for predicting consumer default. Using more 

than 250,000 observations, we show that even simple, easily accessible variables from the digital 

footprint equal or exceed the information content of credit bureau scores. Furthermore, the 

discriminatory power for unscorable customers is very similar to that of scorable customers. Our 

results have potentially wide implications for financial intermediaries’ business models, for 

access to credit for the unbanked, and for the behavior of consumers, firms, and regulators in the 

digital sphere.

Tobias Berg

Frankfurt School of Finance & Management

t.berg@fs.de

Valentin Burg

Humboldt University Berlin

valentin.burg@gmail.com

Ana Gombovi 

Frankfurt School of Finance & Management, 

a.gombovic@fs.de

Manju Puri

Fuqua School of Business

Duke University

100 Fuqua Drive

Durham, NC 27708-0120

and FDIC

and also NBER

mpuri@duke.edu
2 

1. Introduction 

The growth of the internet leaves a trace of simple, easily accessible information about almost 

every individual worldwide – a trace that we label “digital footprint”. Even without writing text about 

oneself, uploading financial information, or providing friendship or social network data, the simple act of 

accessing or registering on a webpage leaves valuable information. As a simple example, every website can 

effortlessly track whether a customer is using an iOS or an Android device; or track whether a customer 

comes to the website via a search engine or a click on a paid ad. In this project, we seek to understand 

whether the digital footprint helps augment information traditionally considered to be important for default 

prediction and whether it can be used for the prediction of consumer payment behavior and defaults. 

Understanding the importance of digital footprints for consumer lending is of significant 

importance. A key reason for the existence of financial intermediaries is their superior ability to access and 

process information relevant for screening and monitoring of borrowers.1

 If digital footprints yield 

significant information on predicting defaults then FinTechs – with their superior ability to access and 

process digital footprints – can threaten the information advantage of financial intermediaries and thereby 

challenge financial intermediaries’ business models.2

 

In this paper, we analyze the importance of simple, easily accessible digital footprint variables for 

default prediction using a comprehensive and unique data set covering approximately 250,000 observations 

from an E-Commerce company located in Germany. Judging the creditworthiness of its customers is 

important because goods are shipped first and paid later. The use of digital footprints in similar settings is 

growing around the world.3

 Our data set contains a set of ten digital footprint variables: the device type (for 

 

1

 See in particular Diamond (1984), Boot (1999), and Boot and Thakor (2000) for an overview of the role of banks in 

overcoming information asymmetries and Berger, Miller, Petersen, Rajan, and Stein (2005) for empirical evidence. 

2

 The digital footprint can also be used by financial intermediaries themselves, but to the extent that it proxies for 

current relationship-specific information it reduces the gap between traditional banks and those firms more prone to 

technology innovation.

3

 In China, Alibaba’s Sesame Credit uses social credit scores from AntFinancial and goods are also shipped first and 

paid later (see https://www.economist.com/news/finance-and-economics/21710292-chinas-consumer-credit-ratingculture-evolving-fastand-unconventionally-just). Other FinTechs that have publicly announced using digital 

footprints for lending decisions include ZestFinance and Earnest in the U.S., Kreditech in various emerging markets, 

and Rapid Finance, CreditEase, and Yongqianbao in China (see https://www.nytimes.com/2015/01/19/technology/ 

banking-start-ups-adopt-new-tools-for-lending.html and https://www.forbes.com/sites/rebeccafeng/2017/ 

07/25/chinese-fintechs-use-big-data-to-give-credit-scores-to-the-unscorable/#45b0e6ed410a). 
3 

example, tablet or mobile), the operating system (for example, iOS or Android), the channel through which 

a customer comes to the website (for example, search engine or price comparison site), a do not track 

dummy equal to one if a customer uses settings that do not allow tracking device, operating system and 

channel information, the time of day of the purchase (for example, morning, afternoon, evening, or night), 

the email service provider (for example, gmail or yahoo), two pieces of information about the email address 

chosen by the user (includes first and/or last name and includes a number), a lower case dummy if a user 

consistently uses lower case when writing, and a dummy for a typing error when entering the email 

address. In addition to these digital footprint variables, our data set also contains a credit score from a 

private credit bureau. We are therefore able to assess the discriminatory ability of the digital footprint 

variables both separately, vis-à-vis the credit bureau score, and jointly with the credit bureau score. 

Our results suggest that even the simple, easily accessible variables from the digital footprint proxy 

for income, character and reputation and are highly valuable for default prediction. For example, the 

difference in default rates between customers using iOS (Apple) and Android (for example, Samsung) is 

equivalent to the difference in default rates between a median credit score and the 80th percentile of the 

credit score. Bertrand and Kamenica (2017) document that owning an iOS device is one of the best 

predictors for being in the top quartile of the income distribution. Our results are therefore consistent with 

the device type being an easily accessible proxy for otherwise hard to collect income data. 

Variables that proxy for character and reputation are also significantly related to future payment 

behavior. For example, customers coming from a price comparison website are almost half as likely to 

default as customers being directed to the website by search engine ads, consistent with marketing research 

documenting the importance of personality traits for impulse shopping.4

 Belenzon, Chatterji, and Daley 

(2017) and Guzman and Stern (2016) have documented an eponymous-entrepreneurs-effect, implying that 

whether a firm is named after their founders matters for subsequent performance. Consistent with their 

results, customers having their names in the email address are 30% less likely to default. 

 

4

 See for example Rook (1987), Wells, Parboteeah, and Valacich (2011), and Turkyilmaz, Erdem, and Uslu (2015). 
4 

We provide a more formal analysis of the discriminatory power of digital footprint variables by 

constructing receiver operating characteristics and determining the area under the curve (AUC). The AUC 

is a simple and widely used metric for judging the discriminatory power of credit scores (see for example 

Stein, 2007; Altman, Sabato, and Wilson, 2010; Iyer, Khwaja, Luttmer, and Shue, 2016; Vallee and Zeng, 

2018). The AUC ranges from 50% (purely random prediction) to 100% (perfect prediction) and is closely 

related to the Gini coefficient (Gini= 2*AUC–1). The AUC corresponds to the probability of correctly 

identifying the good case if faced with one random good and one random bad case (Hanley and McNeil, 

1982). Following Iyer, Khwaja, Luttmer, and Shue (2016), an AUC of 60% is generally considered 

desirable in information-scarce environments, while AUCs of 70% or greater are the goal in informationrich environments. 

The AUC using the credit bureau score alone is 68.3% in our data set, comparable to the 66.6% 

AUC using the credit bureau score alone documented in a consumer loan sample of a large German bank 

(Berg, Puri, and Rocholl, 2017), as well as the 66.5% AUC using the credit bureau score alone in a loan 

sample of 296 German savings banks (Puri, Rocholl, and Steffen, 2017). As a comparison, Iyer, Khwaja, 

Luttmer, and Shue (2016) report an AUC of 62.5% in a U.S. peer-to-peer lending data set using a credit 

bureau score only. Similarly, in an own analysis we find an AUC of 59.8% using U.S. credit scores from 

Lending Club. This suggests that the score provided to us by a German credit bureau clearly possesses 

discriminatory power and we use the credit bureau score related AUC of 68.3% as a benchmark for the 

digital footprint variables in our analysis.5

 

Interestingly, a model that uses only the digital footprint variables equals or exceeds the 

information content of the credit bureau score: the AUC of the model using digital footprint variables is 

69.6%, higher than the AUC of the model using only the credit bureau score (68.3%). This is remarkable 

because our data set only contains digital footprint variables that are easily accessible for any firm 

conducting business in the digital sphere. Our results are also robust to a large set of robustness tests. In 

particular, we show that digital footprint variables are not simply proxies for time or region fixed effects 

 

5

 Note that the German credit bureau may use some information which U.S. bureaus are legally prohibited to use 

under the Equal Credit Opportunity Act. Examples include gender, age, current and previous addresses. 
5 

and results are robust to various default definitions and sample splits. We also provide out-of-sample tests 

for all of our results which yield very similar magnitudes. Furthermore, we show that digital footprints 

today can forecast future changes in the credit score. This provides indirect evidence that the predictive 

power of digital footprints is not limited to short-term loans originated online, but that digital footprints 

matter for predicting creditworthiness for more traditional loan products as well. 

In the next step, we analyze whether the digital footprint complements or substitutes for 

information from the credit bureau. We find that the digital footprint complements rather than substitutes 

for credit bureau information. The correlation between a score based on the digital footprint variables and 

the credit bureau score is only approximately 10%. As a consequence, the discriminatory power of a model 

using both the credit bureau score and the digital footprint variables significantly exceeds the 

discriminatory power of models that only use the credit bureau score or only use the digital footprint 

variables. This suggests that a lender that uses information from both sources (credit bureau score + digital 

footprint) can make superior lending decisions. The AUC of the combined model (credit bureau score + 

digital footprint) is 73.6% and therefore 5.3 percentage points higher than that of a model using only the 

credit bureau score. This improvement is very similar to the 5.7 percentage points AUC improvement 

reported in Iyer, Khwaja, Luttmer, and Shue (2016) who compare the AUC using the Experian credit score 

to the AUC in a setting where, in addition to the credit score, lenders have access to a large set of borrower 

financial information as well as access to non-standard information (characteristics of the listing text, group 

and friend endorsements as well as borrower choice variables such as listing duration and listing category). 

It is also sizeable relative to the improvement in the AUC by +8.8 percentage points in a consumer loan 

sample of a large German bank (Berg, Puri, and Rocholl, 2017) and the improvement in the AUC by +11.9 

percentage points in a loan sample of 296 German savings banks (Puri, Rocholl, and Steffen, 2017), where 

the AUC using the credit bureau score is compared to the AUC using the entire bank-internal information 

set, including account data, credit history, as well as socio-demographic data and income information. 

Taken together, this evidence suggests that a few variables from the digital footprint can (partially) 

substitute for variables that are otherwise more expensive to collect, otherwise take significantly more 
6 

effort to provide and process, or might only be available to a few lenders with specific access to particular 

types of information. 

Furthermore, digital footprints can facilitate access to credit when credit bureau scores do not exist, 

thereby fostering financial inclusion and lowering inequality (Japelli and Pagano, 1993; Djankov, McLiesh, 

and Shleifer, 2007; Beck, Demirguc-Kunt, and Honohan, 2009; and Brown, Jappelli and Pagano, 2009). 

We therefore analyze customers for whom no credit bureau score is available, i.e., customers whose credit 

history is insufficient to calculate a credit bureau score, which we label “unscorable customers”. We find 

that the discriminatory power of the digital footprint for unscorable customers matches the discriminatory 

power for scorable customers (72.2% versus 69.6% in-sample, 68.8% versus 68.3% out-of-sample). These 

results suggest that digital footprints have the potential to boost financial inclusion to parts of the currently 

two billion working-age adults worldwide that lack access to services in the formal financial sector. 

In the last section, we discuss implications of our findings for the behavior of consumers, firms and 

regulators. Consumers might plausibly change their behavior if digital footprints are widely used for 

lending decisions (Lucas (1976)). Some of the digital footprint variables are clearly costly to manipulate 

(such as buying the newest smart device or signing up for a paid email account) while others require a 

customer to change her intrinsic habits (such as impulse shopping or making typing mistakes). However, 

more importantly, such a change in behavior can lead to a situation where consumers fear to express their 

individual personality online. A wider implication of our findings is therefore that the use of digital 

footprints has a considerable impact on everyday life, with consumers constantly considering their digital 

footprints which are so far usually left without any further thought. Firms and regulators are equally likely 

to react to an increased use of digital footprints. As an example, firms associated with low creditworthiness 

products may object to the use of digital footprints and may conceal the digital footprint of their products. 

Regulators are likely to watch closely whether digital footprints proxy for variables that are legally 

prohibited to be used for credit scoring. 

 Our paper relates to the literature on the role of financial intermediaries in mitigating information 

asymmetries (Diamond, 1984; Petersen and Rajan, 1994, Boot, 1999; Boot and Thakor, 2000; Berger, 
7 

Miller, Petersen, Rajan, and Stein, 2005). The prior literature has established the importance of credit 

history and account data to assess borrower risk (Mester, Nakamura, and Renault, 2007; Norden and 

Weber, 2010; Puri, Rocholl, and Steffen, 2017), thereby giving rise to an informational advantage for those 

financial intermediaries with access to borrowers’ credit history and account data. More recently, the 

literature has explored the usefulness of data beyond the credit bureau score and bank-internal relationshipspecific data for default prediction. These data sources include soft information in peer-to-peer lending 

(Iyer, Khwaja, Luttmer, and Shue, 2016), friendships and social networks (Hildebrandt, Rocholl, and Puri, 

2017; Lin, Prabhala, and Viswanathan, 2013), text-based analysis of applicants listings (Gao, Lin, and Sias, 

2017; Dorfleitner et al., 2016), and signaling and screening via contract terms (reserve interest rates in 

Kawai, Onishi, and Uetake 2016; maturity choice in Hertzberg, Liberman, and Paravisini, 2017). 

Our paper differs from these papers, in that the information we are looking at is provided simply by 

accessing or registering on the website, not by furnishing any information – hard or soft – about the 

applicant. We show that even simple, easily accessible variables from the digital footprint provide valuable 

information for default prediction that helps to significantly improve traditional credit scores. Our variables 

stand out in terms of their ease of collection: almost every firm operating in the digital sphere can 

effortlessly track the digital footprint we use. Unlike the papers cited above, the processing and 

interpretation of these variables does not require human ingenuity, nor does it require effort on the side of 

the applicant (such as uploading financial information or inputting a text description about oneself), nor 

does it require the availability of friendship or social network data. Simply accessing or registering on the 

website is adequate. Our results imply that barriers to entry in financial intermediation might be lower in a 

digital world, and easily accessible digital footprints can (partially) substitute for variables that need to be 

collected with considerable effort in a non-digital world. As a consequence, the digital footprint can also 

be used to process applications faster than traditional lenders (see Fuster et al. (2018) for an analysis of 

process time of FinTech lenders versus traditional lenders). A credit score based on the digital footprint 

should therefore serve as a benchmark for other models that use more elaborate sources of information that 

might either be more costly to collect or only accessible to a selected group of intermediaries. 
8 

The rest of the paper is structured as follows. Section 2 provides an overview about the institutional 

setup and data. Section 3 provides empirical results. Section 4 discusses further implications of our 

findings. Section 5 concludes. 

2. Institutional setup, descriptive statistics, and the digital footprint 

2.1 Institutional setup 

We access data about 270,399 purchases from an E-commerce company selling furniture in 

Germany (similar to “Wayfair” in the U.S.) between October 2015 and December 2016. Before purchasing 

an item, a customer needs to register using his or her name, address and email. Judging the creditworthiness 

of its customers is important because goods are shipped first and paid later.6

 The claims in our data set are 

therefore akin to a short-term consumer loan. 

The company uses information from two private credit bureaus to decide whether customers have a 

sufficient creditworthiness. The first credit bureau provides basic information such as whether the customer 

exists and whether the customer is currently or has been recently in bankruptcy. This score is used to 

screen out customers with fraudulent data as well as customers with clearly negative information.7

 The 

second credit bureau score draws upon credit history data from various banks (credit card debt and loans 

outstanding, past payment behavior, number of bank accounts and credit cards), sociodemographic data, as 

well as payment behavior data sourced from retail sales firms, telecommunication companies, and utilities. 

This credit bureau score is requested for purchases exceeding € 100 and we consequently restrict our data 

set to purchases for which the company requested a credit bureau score.8 We label those customers for 

whom a credit bureau exists “scorable customers”. 

 

6

 Customers can choose to pay upfront instead of paying after shipment of the products. Customers paying upfront 

are not included in our data set. Paying after shipment, so called “deferred payment”, is by far the dominant payment 

type: more than 80% of customers choose to pay after shipment if this method is offered to a customer. 

7

 The firm switched the credit bureau that provides this basic information in July 2016. Results are very similar preJuly-2016 and post-July-2016. 

8

 The company requests the credit bureau score if the customer’s shopping cart amount exceeds € 100, even when the 

customer ultimately purchases a smaller amount. 
9 

The E-commerce company uses the credit bureau score together with the digital footprint 

(discussed further below) to screen out borrowers with a predicted default rate exceeding 10 percent. 

Restricting our data set to orders exceeding € 100 and excluding customers with a very low 

creditworthiness has the benefit of making our data set more comparable to a typical credit card, bank loan 

or peer-to-peer lending data set. 

After the purchase, the items are sent to the customer together with an invoice. The customer has 

14 days to pay the invoice. If the customer does not pay on time, three reminders (one per email, two per 

email and letter) are sent out. A customer who does not pay after three reminders is in default and the claim 

is transferred to a debt collection agency, on average 3.5 months from the order date. 

2.2 Descriptive statistics 

Our data set comprises 270,399 purchases between October 2015 and December 2016. The credit 

bureau score is available for 254,808 observations (94% of the sample) and unavailable for 15,591 

observations (6% of the sample). Non-existence is due to customers being unscorable, i.e., not having a 

sufficient credit history that would allow the credit bureau to calculate a credit score. In the following and 

throughout the entire paper, we distinguish between scorable and unscorable borrowers, i.e. those with and 

without a credit bureau score. As shown in Figure 1a, the purchases are distributed roughly even over time 

with slight increases in orders during October and November, as typical for the dark season. Table 2 

provides descriptive statistics for both subsamples, variable descriptions are in Table 1. 

[Table 1 and Table 2, Figure 1a, Figure 1b, Figure 2] 

In the sample with credit bureau scores, the average purchase volume is EUR 318 (approximately 

USD 350) and the mean customer age is 45.06 years. On average, 0.9% of customers default on their 

payment. Our default definition comprises claims that have been transferred to a debt collection agency.9

The credit bureau score ranges from 0 (worst) to 100 (best). It is highly skewed with 99% of the 

 

9

 The average time between the order date and the date a claim is transferred to the debt collection agency is 103 

days in our sample, i.e., approximately 3.5 months. 
10 

observations ranging between 90 and 100. The average credit bureau score is 98.11, the median is 98.86. 

Figure 2 provides the distribution of credit bureau scores together with (smoothed) default rates. The 

average credit bureau score of 98.11 corresponds to a default rate of approximately 1% and default rates 

grow exponentially when credit bureau scores decrease, with a credit bureau of 95 corresponding to a 2% 

default rate and a credit bureau of 90 corresponding to a 5% default rate. Note that default rates are not 

annualized but constitute default rates over a shorter window of approximately 3.5 months. 

Descriptive statistics for the sample without credit bureau score are similar with respect to order 

amount and gender, with age being somewhat lower (consistent with the idea that it takes time to build up a 

credit history) and default rates being significantly higher (2.5%). 

2.3 Representativeness of data set 

 Our data set is largely representative of the geographic distribution of the German population 

overall. As can be seen from Figure 1b, the share of observations in our sample closely follows the 

population share for all the 16 German states. Furthermore, the mean customer age is 45.06 years, 

comparable both to the mean age of 43.77 in the German population as well as to the mean age of 45.24 

reported by Berg, Puri, and Rocholl (2017) in a sample of more than 200,000 consumer loans at a large 

German private bank. Our sample is restricted to customers of legal age (18 years and older) and less than 

5% of the customers are older than 70. The age distribution in our sample therefore resembles the age 

distribution of the German population aged 18-70: the interquartile range of the German population aged 

18-70 ranges from 31-56, compared to an interquartile range of 34-54 in our sample. 

The average default rate in our sample is 1.0% (0.9% for scorable customers, 2.5% for unscorable 

customers). As discussed above, these default rates constitute default rates over a window of approximately 

4 months, implying a scaled-up annualized default rate of 3.0%. We compare our default rate to other 

studies in Appendix Table A.1. Berg, Puri, and Rocholl (2017) report an average default rate of 2.5% in a 

sample of more than 200,000 consumer loans at a large German private bank; the major German credit 

bureau reports an average default rate of 2.4% (2015) and 2.2% (2016) in a sample of more than 17 million 
11 

consumer loans, and the two largest German banks report probability of default estimates of 1.5% 

(Deutsche Bank) and 2.0% (Commerzbank) across their entire retail lending portfolio. Default rates 

reported by Puri, Rocholl, and Steffen (2017) in a sample of German savings banks are somehow lower. 

Taken together, this evidence suggests that default rates in our sample are largely representative of a typical 

consumer loan sample in Germany. Charge-off rates on consumer loans in the U.S. across all commercial 

banks as reported by the Federal Reserve were approximately 2% in 2015/2016, implying a comparable 

default rate to our sample. Default rates reported in some U.S. peer-to-peer lending studies are higher (up to 

10% per annum). However, the studies with the highest default rates were conducted using loans originated 

in 2007/2008 at the height of the financial crisis. More recent studies report default rates that are 

comparable to our default rates on an annualized basis (for example, Hertzberg, Liberman, and Paravisini, 

(2016) report a 4.2% annualized default rate in a sample of Lending Club loans originated in 2012/2013). 

2.4 Digital footprint 

In addition to the credit bureau score described above, the company collects a “digital footprint” 

for each customer. All digital footprint variables are simple, easily accessible variables that every firm 

operating in the digital sphere can collect at almost no cost. The list of all digital footprint variables is 

reported in Table 1. 

The digital footprint comprises easily accessible pieces of information known to be a proxy for the 

economic status of a person, for instance the device type (desktop, tablet, mobile) and operating system (for 

example, Windows, iOS, Android). As documented by Bertrand and Kamenica (2017), owning an iOS 

device is one of the best predictors for being in the top quartile of the income distribution. Furthermore, the 

distinct features of most commonly used email providers in Germany (for example Gmx, Web, T-Online, 

Gmail, Yahoo, or Hotmail) also allow us to infer information about the customer’s economic status. Gmx, 

Web, and T-online are common email hosts in Germany which are partly or fully paid. In particular, Tonline is a large internet service provider and is known to serve a more affluent clientele, given that it 

offers internet, telephone, and television plans and in-person customer support. A customer obtains a T-
12 

online email address only if she purchased a T-online package. Yahoo and Hotmail, in contrast, are fully 

free and mostly outdated services. Thus, based on these simple variables, the digital footprint provides 

easily accessible proxies of a person’s economic status absent of private information and hard-to-collect 

income data. 

Second, the digital footprint provides simple variables known to proxy for character, such as the 

channel through which the customer has visited the homepage of the firm. Examples for the channel 

include paid clicks (mainly through paid ads on google or by being retargeted by ads on other websites 

according to preferences revealed by prior searches), direct (a customer directly entering the URL of the Ecommerce company in her browser), affiliate (customers coming from an affiliate site that links to the Ecommerce company’s webpage such as a price comparison site), and organic (a customer coming via the 

non-paid results list of a search engine). Information about a person’s character (such as her self-control) is 

also reasonably assumed to be revealed by the time of day at which the customer makes the purchase (for 

instance, we find that customers purchasing between noon and 6 pm are approximately half as likely do 

default as customers purchasing from midnight to 6am). 

Finally, corporate research documents that firms being named after their owners have a superior 

performance. This so called eponymous effect is mainly driven via a reputation channel (Belenzon, 

Chatterji, and Daley, 2017). We find it reasonable to extend this finding to the choice of email addresses. A 

testable prediction from this prior literature is that eponymous customers – those who include their first 

and/or last names in their email address – are less likely to default. In contrast to eponymous customers, 

those arguably less concerned with including their name but instead include numbers or type errors in their 

email address default more frequently.10 The digital footprint provides this type of simple information that 

can serve as a proxy for reputation in the form of four dummies, as to whether the last and/or first name is 

part of the email address, whether the email address contains a number, whether the email contains an 

 

10 Approximately 10-15% of defaults are identified as fraud cases. Compared to non-fraud defaults, fraud cases have 

a higher incidence of numbers in their email address. This is consistent with anecdotal evidence suggesting that 

fraudsters create a large number of email addresses and do so in a way that uses a string combined with consecutive 

numbers. 
13 

error, as well as whether the customer types either the name or shipping address using lower case on the 

homepage.11 

Note that some of the variables discussed above are likely to proxy for several characteristics. For 

example, iOS devices are a predictor of economic status (Bertrand and Kamenica, 2017), but might also 

proxy for character (for example, status-seeking users might be more likely to buy an iOS device). It is not 

our target to point to exactly one single channel that can explain why digital footprints variables can predict 

default. Rather we want to highlight existing research that provides guidance as to why we can expect these 

variables to matter for default prediction. 

3. Empirical results 

3.1 Univariate results 

We provide univariate results for the sample of customers with a credit bureau score in Table 3. 

[Table 3] 

As expected, the credit bureau score clearly exhibits discriminatory ability: the default rate in the 

lowest credit score quintile is 2.12%, more than twice the average default rate of 0.94% and five times the 

default rate in the highest credit score quintile (0.39%).12

 

Interestingly, the univariate results indicate discriminatory ability for the digital footprint variables 

as well. The footprint variables that proxy for income and wealth reveal significant differences in payment 

behavior. For example, orders from mobile phones (default rate 2.14%) are three times as likely to default 

as orders from desktops (default rate 0.74%) and two-and-a-half times as likely to default as orders from 

 

11 Kreditech is an example of a German company already using simple typography variables, such as the lack of 

capital letters, to evaluate credit risk but also detect possible fraud and online impersonations (see BBVA (2017): 

The digital footprint: a tool to increase and improve lending, accessed via https://www.bbva.com/en/digital-footprinttool-increase-improve-lending/). 

12 Using credit bureau scores for Lending Club loans over the same period we find that the default rate increases only 

by a factor of 2.5 from the highest to the lowest credit bureau score quintile, suggesting our credit bureau score has 

more discriminatory power in our data set than the credit bureau score in the Lending Club data set, which we will 

confirm later using AUCs. 
14 

tablets (default rate 0.91%). Orders from the Android operating systems (default rate 1.79%) are almost 

twice as likely to default as orders from iOS systems (1.07%) – consistent with the idea that consumers 

purchasing an iPhone are usually more affluent than consumers purchasing other smartphones. As 

expected, customers from a premium internet service (T-online, a service that mainly sells to affluent 

customers at higher prices but with better service) are significantly less likely to default (0.51% versus the 

unconditional average of 0.94%). Customers from shrinking platforms like Hotmail (an old Microsoft 

service) and Yahoo exhibit default rates of 1.45% and 1.96%, almost twice the unconditional average. 

Information on character is also significantly related to default rates. Customers arriving on the 

homepage through paid ads (either clicking on paid google ads or being retargeted after prior google 

searches) exhibit the largest default rate (1.11%). One possible interpretation is that ads, in particular ads 

that are shown multiple times on various websites to a customer, seduce customers to buy products they 

potentially cannot afford. Customers being targeted via affiliate links, e.g. price comparison sites, and 

customers directly entering the URL of the E-commerce company in their browser exhibit lower-thanaverage default rates (0.64% and 0.84%). Finally, customers ordering during the night have a default rate of 

1.97%, approximately two times the unconditional average. 

There are only few customers who make typing mistakes while inputting their email addresses 

(roughly 1% of all orders), but these customers are much more likely to default (5.09% versus the 

unconditional mean of 0.94%). Customers with numbers in their email-addresses default more frequently, 

which is plausible given that fraud cases also have a higher incidence of numbers in their email address.13

Customers who use only lower case when typing their name and shipping address are more than twice as 

likely to default as those writing names and addresses with first capital letters. Interestingly, we find that 

eponymous customers who use their first and/or last name in their email address are less likely to default. 

Thus information on reputation also shows significant power for predicting default rates. These findings are 

 

13 Approximately 10-15% of defaults are identified as fraud cases. Compared to non-fraud defaults, fraud cases have 

a higher incidence of numbers in their email address. This is consistent with anecdotal evidence suggesting that 

fraudsters create a large number of email addresses and do so in a way that uses a string combined with consecutive 

numbers. 
15 

consistent with recent findings by Belenzon, Chatterji, and Daley (2017) who show that eponymous firms 

perform better, supporting the reputational explanation of their findings. 

3.2 Measures of association between variables, Combination of digital footprint variables 

In the next step, we report measures of association between the credit bureau score and the digital footprint 

variables in order to assess whether the digital footprint variables are correlated with the credit bureau score 

and among each other, or whether they provide independent information. As most of the digital footprint 

variables are categorical variables, standard measures for ordinal variables (for example, Pearson’s 

correlation or Spearman rank correlation) are not feasible. We therefore report Cramér’s V, which provides 

a measure of association between categorical variables that is bounded in the interval [0,1], with 0 denoting 

no association and 1 denoting perfect association. To allow calculation of Cramér’s V, we transform the 

continuous variables (credit bureau score and Check-Out Time) into categories by forming quintiles by 

credit bureau score and categorizing the check-out time into morning, afternoon, evening, and night. Table 

4 reports the results. 

[Table 4] 

Interestingly, the Cramér’s V between the credit bureau score and the digital footprint variables is 

economically small, with values ranging between 0.01 and 0.07. This suggests that digital footprint 

variables act as complements rather than substitutes for credit bureau scores – a claim we will analyze more 

formally below in a multivariate regression setup. 

The association between the variables Device Type and Operating System is high. This is not 

surprising, for example most desktop computers run on Windows and most tablets on iOS or Android. To 

avoid multicollinearity, we therefore simply use the most frequent combinations from these two categories 

in our multivariate regressions below.14 The check-out time has some association with device 

 

14 The most frequent combinations are Windows and Macintosh for desktop computers, Android and iOS for tablets, 

and Android and iOS for mobile phones. See Table A.3 in the Appendix for descriptive statistics. 
16 

type/operating system. Mobile phones are used relatively more frequently than desktops and tablets for late 

night shopping, and desktops are used relatively more in the afternoon. All other combinations of digital 

footprint variables have a Cramér’s V of less than 0.25. 

 The fact that many of the digital footprint variables provide mutually independent information 

suggests that a combination of digital footprint variables is significantly more powerful in predicting 

default than single variables. We illustrate this idea in Figure 3. Figure 3 depicts default rates using the 

variables “Operating system” and “Email host” separately as well as in combination. The sample is 

restricted to customers with a credit bureau score. 

[Figure 3] 

Among the categories from these two variables, T-online users have the lowest default rate 

(0.51%), while Yahoo users have the highest default rate (1.96%). As a reference point, we list deciles by 

credit bureau score at the bottom of Figure 3. The default rate of T-online users of 0.51% is approximately 

equal to the default rate in the 7th decile of credit bureau scores, while the default rate of Yahoo users 

(1.96%) is between the 1st and 2nd decile of credit bureau scores. When combining information from both 

variables (“Operating system” and “Email host”), default rates are even more dispersed.15 We observe the 

lowest default rate for Mac-users with a T-online email address. The default rate for this combination is 

0.36%, which is lower than the average default rate in the 1st decile of credit bureau scores. On the other 

extreme, Android users with a Yahoo email address have an average default rate of 4.30%, significantly 

higher than the 2.69% default rate in the highest decile of credit bureau scores. These results suggest that 

even two simple variables from the digital footprint allow categorizing customers into default bins that 

match or exceed the variation in default rates from credit bureau score deciles. 

3.3 Multivariate results: Digital footprint and default 

 

15 The following results are not driven by small sample sizes, i.e., all categories reported in Figure 3 have at least 

1,000 observations. 
17 

Table 5 provides multivariate regression results of a default dummy on the credit bureau score and 

digital footprint variables. We use a logistic regression and report the Area-Under-Curve (AUC) for every 

specification. The AUC is a simple and widely used metric for judging the discriminatory power of credit 

scores (see for example Stein, 2007; Altman, Sabato, and Wilson, 2010; Iyer, Khwaja, Luttmer, and Shue, 

2016). The AUC ranges from 50% (purely random prediction) to 100% (perfect prediction). Following 

Iyer, Khwaja, Luttmer, and Shue (2016), an AUC of 60% is generally considered desirable in informationscarce environments, while AUCs of 70% or greater are the goal in information-rich environments. We 

also plot the Receiver Operating Characteristic that is used to calculate the AUC in Figure 4. 

[Table 5 and Figure 4] 

Column (1) of Table 5 reports results using the (continuous) credit bureau score as an independent 

variable. As expected and consistent with Figure 2, the credit bureau score is a highly significant predictor 

of default, with higher credit bureau scores being associated with lower default rates. The AUC using only 

the credit bureau score is 68.3% and is significantly different from chance (AUC of 50%). This result is 

comparable to the 66.6% AUC using the credit bureau score alone documented in a consumer loan sample 

of a large German bank (Berg, Puri, and Rocholl, 2017) and the 66.5% AUC using the credit bureau score 

alone in a loan sample of 296 German savings banks (Puri, Rocholl, and Steffen, 2017). This result is 

higher than the AUC of 62.5% reported by Iyer, Khwaja, Luttmer, and Shue (2016) in a U.S. peer-to-peer 

lending data set using the Experian credit score only and the AUC of 59.8% we compute for comparison 

using credit bureau scores for Lending Club loans. This suggests that the credit score provided to us by a 

German credit bureau clearly possesses discriminatory power and we use the AUC of 68.3% as a 

benchmark for the digital footprint variables in the following. 

Column (2) reports results for the digital footprint, column (3) uses both the credit bureau score 

and the digital footprint variables, and column (4) adds age and month and region fixed effects. For 

categorical variables, all coefficients need to be interpreted relative to the baseline level. We always choose 

the most popular category in a variable as the baseline level. We report AUCs in the bottom rows of Table 
18 

5 and also test for differences in AUCs using the methodology by DeLong, DeLong, and Clarke-Pearson 

(1988). 

Interestingly, digital footprint variables have an AUC of 69.6% – which is higher than the AUC of 

the credit bureau score.16 These results suggest that even simple, easily accessible variables from the digital 

footprint are as useful in predicting defaults as the credit bureau score. We focus on the economic and 

statistical significance of the variables in column (2) in the following discussion. 

The variables “Email error”, “Mobile/Android”, and the “Night” dummy have the highest 

economic significance. The variable “Email error” is a simple dummy variable that is equal to one in only a 

few cases, and thus allows categorizing a small portion of customers as being high risk. Customers with an 

Email Error have an odds ratio of defaulting which is exp(1.66)=5.25 times higher than customers without 

an Email Error. Given that default rates are rather small, default probabilities p and odds ratios (p/(1-p)) are 

very similar, implying that customers with an Email Error default approximately 5.25 times more frequent 

than customers without Email Error. 

Android users default more frequently than the baseline category, consistent with the univariate 

results and consistent with the fact that consumer purchasing an iPhones are usually more affluent than 

consumers purchasing other smartphones. Customers purchasing at night (midnight-6am) also default more 

frequently than customers purchasing at other times of the day, suggesting that purchases made during a 

time when many people might be asleep are fundamentally different from daytime purchases. 

In column (3) of Table 5, we complement the digital footprint variables with the credit bureau 

score. Both the coefficient on the credit bureau score as well as the coefficients on the digital footprint 

variables barely change compared to columns (1) and (2). This suggests that the digital footprint variables 

complement rather than substitute for the information content of the credit bureau score. As a consequence, 

the AUC of the combined model using both the digital footprint variables and the credit bureau score 

 

16 Note that in Table 5 we report only the 6 largest categories for email providers even though we use the largest 18 

categories in the regression (all email providers with at least 1000 observations). In a regression using only the 6 

reported email hosts, the AUC of the digital footprint decreases by 0.9PP, still being higher than the AUC using 

credit bureau score alone. 
19 

(73.6%) is significantly higher than the AUC of each of the stand-alone models (68.3% for the credit 

bureau score and 69.6% for the digital footprint variables).17 

In column (4) of Table 5, we add time and region fixed effects and control for age. Results remain 

almost unchanged, suggesting that neither the credit bureau score nor the digital footprint act as simple 

proxies for different regions, different sub-periods, or different age. While older people are expectedly less 

likely to default, consistent with the idea that it takes time to build up a credit history, coefficients for the 

credit bureau score and the digital footprint remain very similar.18 Only the coefficient for users of the 

premium service T-online, which is known to serve more affluent and older customers, decreases slightly 

in economic significance (from -0.35 in column (3) to -0.27 in column (4)).

Figure 5 provides a more detailed look at the correlation between the credit bureau score and the 

digital footprint. Using the results from column (2) of Table 5, we construct a default prediction using only 

the digital footprint variables for each observation in our sample. For each observation, Figure 5 then 

depicts the percentile using the credit bureau score as well as the percentile using the digital footprint score. 

As an example, if a customer has a very good credit bureau score (=low default probability) and a very low 

default probability by the digital footprint as well, then it would end up in the upper right-hand corner of 

Figure 5. A customer with a low credit bureau score (=high default probability) and a very high default 

probability by the digital footprint as well would end up in the lower left-hand corner. Observations where 

credit bureau score and digital footprint have opposing predictions end up in the upper left-hand corner or 

the lower right-hand corner. Figure 4 clearly shows that the correlation between credit bureau score and 

digital footprint is very low (R2

 of 1.0%, implying a correlation of approximately 10%). These results 

confirm our prior observation that the digital footprint acts as a complement, rather than a substitute, of the 

credit bureau score. 

[Figure 5] 

 

17 Please note that AUCs generated by two independent variables cannot be simply summed up because the AUC of 

an uninformative variable is already 50%. 

18 The coefficient on log(age) in Column (4) of Table (5) is -0.33 (significant at the 1% level), suggesting that a 

doubling in age reduces defaults by approximately one third. 
20 

3.4 Unscorable customers and access to finance 

The lack of access to financial services affects around two billion working-age adults worldwide 

and is seen as one of the main drivers of inequality.19 Particularly in developing countries, the inability of 

the unbanked population to participate in financial services is often caused by a lack of information 

infrastructure, such as credit bureau scores. Many countries have therefore already started leveraging 

digital technologies to promote financial inclusion.20 As digital footprint variables are available also for 

customers without a credit score, analyzing borrowers’ digital behavior may present an opportunity to 

boost financial inclusion – particularly in developing economies, where a large share of the population does 

not have access to banking services and therefore to traditional credit sources.21 

We test whether the digital footprint can present an opportunity to facilitate access to finance for 

customers who do not have a credit bureau score, which we label unscorable customers in our analysis. The 

average default rate of unscorable customers in our sample is 2.49% (see Table 6), thereby clearly 

exceeding the default rate for scorable customers of 0.94% (see Table 2). This is not surprising, given that 

unscorable customers are customers without credit record where uncertainty about repayment is likely to be 

higher. Interestingly, the discriminatory power of the digital footprint – as measured by the AUC – is 

broadly similar for unscorable customers than for scorable customers (72.2% versus 69.6%), see Table 7 

and Figure 6. Adding time and region fixed effects also does not affect our results (Column (3) of Table 7). 

[Table 6, Table 7 and Figure 6] 

These results suggest that digital footprints may help to overcome information asymmetries 

between lenders and borrowers when standard credit bureau information is not available. We clearly have 

 

19 The World Bank Group identifies financial inclusion as a key enabler of reducing poverty and boosting prosperity 

and promotes new use of data and digital technology as an opportunity for expanding access to financial services. 

See e.g. http://www.worldbank.org/en/news/video/2016/03/10/2-billion-number-of-adults-worldwide-withoutaccess-to-formal-financial-services, http://www.worldbank.org/en/topic/financialinclusion

20 Initiatives include the G20 High-Level Principles for Digital Financial Inclusion available via 

https://www.gpfi.org/sites/default/files/G20%20High%20Level%20Principles%20for%20Digital%20Financial%20I

nclusion.pdf 

21 See BBVA (2017), suggesting analyzing borrowers’ online behavior can help financial inclusion particularly in 

emerging economies, available via https://www.bbva.com/en/digital-footprint-tool-increase-improve-lending/
21 

to be cautious in interpolating these results from a developed country to unscorable customers in emerging 

markets. Still, recent activity in the FinTech industry suggests this is an avenue that FinTechs aim to take. 

Motivated by a dramatic increase in the availability of digital footprints in developing economies, new 

FinTech players have emerged that use digital footprints to challenge traditional banking options and 

develop innovative financing solutions.22 These FinTechs have the vision to give billions of unbanked 

people access to credit when credit bureaus scores do not exist, thereby fostering financial inclusion and 

lowering inequality (see Japelli and Pagano, 1993; Djankov, McLiesh, and Shleifer, 2007; Beck, 

Demirguc-Kunt, and Honohan, 2009; and Brown, Jappelli and Pagano, 2009 for the link between 

availability of credit scores, access to credit and inequality). 

3.5 Out-of-sample tests 

Table 5 (scorable customers) and Table 6 (unscorable customers) were estimated in-sample which 

may overstate discriminatory power due to overfitting. We therefore provide out-of-sample tests using 

Nx2-fold cross validation in Table 8. Nx2-fold cross validation is a common method to evaluate out-ofsample performance of an estimator (see for example Dietterich, 1998 for a general discussion of crossvalidation techniques). We thereby randomly divide the full sample into half samples A and B, estimate a 

predictive logistic regression using sample A, and use the coefficients to create predicted values for the 

observations in sample B. We then estimate a predictive regression using sample B and use the coefficients 

to create predicted values for observations in sample A. Finally, we determine the AUC for the full sample 

of observations, using all predicted values estimated out-of-sample. We repeat this procedure N=100 times 

and report the mean out-of-sample AUCs in column (2) of Table 8. 

Panel 1 of Table 8 reports out-of-sample results for scorable customers. The out-of-sample AUC is 

less than 1 PP lower than the in-sample AUC for all specifications apart from the fixed effects regression. 

In the fixed effects specification, where we insert granular month and region fixed effects, out-of-sample 

AUCs are 2.8 PP lower than in-sample AUCs. This is not surprising given that overfitting is in particular an 

 

22 See e.g. https://hbr.org/2017/01/fintech-companies-could-give-billions-of-people-more-banking-options 
22 

issue when many explanatory variables are used. AUCs for the fixed effects regressions are of little 

relevance for our paper as the fixed effects regressions serve the sole purpose of showing that neither the 

credit bureau score nor the digital footprint variables are simple proxies for month or region fixed effects. 

Panel 2 of Table 8 reports out-of-sample results for unscorable customers. The out-of-sample AUC 

using Nx2-fold cross validation decreases by 3.9 PP for unscorable customers compared to the in-sample 

estimate. The sample size for unscorable customers is significantly smaller than for scorable customers so 

that a larger gap between out-of-sample and in-sample AUCs is expected. The out-of-sample AUC of the 

digital footprint for unscorable customers (68.3%) is, however, still similar to the out-of-sample AUC of 

68.8% for scorable customers. Overall, our main conclusion – digital footprints provide a similar predictive 

power for unscorable customers than for scorable customers – is clearly confirmed in out-of-sample tests as 

well. 

[Table 8] 

3.6 Alternative default definitions and sample splits 

Table 9 provides various robustness tests. Panel A uses alternative default definitions and Panel B 

provides results for various sample splits. In all Panels, we report the area under curve (AUC) for the credit 

bureau score, for the digital footprint, and for both together. 

Panel A uses an alternative default definition, namely default after efforts by the collection agency, 

in column (2). The collection agency is able to fully recover approximately 40% of the claims, resulting in 

a reduced default rate after the collection agency process. The relative importance of credit bureau score 

versus digital footprint is almost unaffected and the AUC increases slightly. This seems intuitive, given that 

it is harder to predict customers who don’t pay in the first months, but pay at a later point in time than to 

simply predict customers that won’t be able to pay at all. Column (3) of Panel A reports results using the 

loss given default as the dependent variable. Compared to the credit bureau score, the digital footprint is 

both economically and statistically a better predictor of loss given default. The digital footprint therefore 

does not only help to predict default, but also helps to predict recovery rates for defaulted exposures. Panel 
23 

B reports various sub-sample splits. Results are very similar for small and large orders (split at the median) 

as well as for female and male customers. 

Overall, the robustness tests suggest that our key results from Table 5 – digital footprints predict 

default as well or even better than the credit bureau score, and digital footprint and credit bureau score 

being complements rather than substitutes, is robust for different default definitions and various sample 

splits. This suggests even simple, easily accessible variables from the digital footprint are important for 

default prediction over and above the information content of credit bureau scores. 

[Table 9] 

3.7 External validity 

 The evidence presented so far provides evidence of the predictive power of the digital footprint for 

short term loans for products purchased online. In Section 2.3 (and Appendix Table A.2) we have shown 

that our data set is largely representative to a typical German consumer loan sample in terms of age 

distribution, geographic distribution, as well as default rates. Appendix Table A.2 – also discussed in 

Section 2.3 – further shows that the credit bureau score has a very similar predictive power in our sample 

compared to consumer loan samples both at German savings banks as well as at German private banks. 

 In this section, we provide further evidence for the external validity of our setting. In particular, we 

test whether digital footprints today can forecast future changes in the credit bureau score. If a good digital 

footprint today predicts an increase in the credit bureau score in the future, then this is evidence that digital 

footprints matter for other loan products as well. We therefore run regressions of the form: 

∆	

, 	


 =  + ∆

, 	


 +  +  (1) 

where ∆	

, 	


 is the change in credit bureau score between t+1 and t, 

∆, 	


 is the difference between predicted default rates using the digital footprint variables 

(i.e., predicted values from column (2) of Table 5) and predicted default rates using the credit bureau score 

(i.e., predicted values from column (1) of Table 5), and X is a set of control variables. We winsorize both 

the dependent and the independent variable in equation (1) at the 1/99 percent level. A limitation of our 
24 

dataset is that the left-hand side variable is available only for customers that are part of our original dataset 

and have returned to the E-Commerce company at least once up to March 2018.23 For each observation in 

our original data set from Table 5 we check whether the customer returned to the platform and report the 

latest available credit bureau score for each customer. For returning customers, the E-Commerce company 

only requests a new credit bureau score if the existing credit bureau score is older than six months, 

implying that the difference between t and t+1 in equation (1) is at least 181 days. The average (median) 

time between t and t+1 in equation (1) is 450 days (431 days), i.e. a little over one year.24

 

Figure 7 provides descriptive evidence of the predictive power of the digital footprint for the 

subsequent development of credit bureau scores. We split our sample into 10 deciles by credit bureau score 

and further split each of the 10 decile into observations where the digital footprint predicts a lower 

probability of default than the credit bureau score (grey line in Figure 7) and observations where the digital 

footprint predicts a higher probability of default than the credit bureau score (black line in Figure 7). We 

then plot the average of the subsequent credit bureau score on the vertical axis for each of these 20 bins (10 

deciles x digital footprint better/worse than credit bureau score). Figure 7 shows that the grey line is 

consistently above the black line, suggesting that customers with better digital footprints also see a better 

development of their credit bureau score in the future. 

 [Figure 7 and Table 10] 

 Table 10 provides formal regression results. Column (1) provides results without control variables. 

The coefficient on ∆

, 	


 is economically and statistically highly significant. The 

 

23 The data set in Table 5 is limited to the period from October 2015 to December 2016 to allow for a subsequent 

observation of default rates and loss given defaults. For changes in credit bureau scores we expand the data set until 

March 2018. Please note that while the sample from Table 5 is limited to customers that pass the minimumcreditworthiness condition (see Section 2.1), the subsequent credit bureau score is also available for returning 

customers that were denied buying via invoice upon returning due to a very low credit bureau score. 

24 It is plausible that changes in credit bureau scores affect customers’ decision to return to the E-Commerce 

company, but such a selection does not necessarily invalidate our regression design. For the estimate of β1 in 

equation (1) we rely on the assumption that the decision to return to the E-Commerce platform is not related to both 

the difference ∆(DFt,CreditScoret) and the subsequent change in credit bureau scores. If, for example, customers 

whose creditworthiness using the digital footprint is better than their creditworthiness using the credit bureau score 

return only if their credit bureau score has increased, then the coefficient β1 would be downward biased (and vice 

versa). 
25 

coefficient of -74.56 suggests that if the digital footprint default prediction is 1PP higher than the credit 

bureau default prediction (for example, the digital footprint predicts a 2% default probability while the 

credit bureau score predicts a 1% default probability), then the credit bureau score decreases by 0.75 points 

in the future. Given that German credit bureau scores represent 1-year survival probabilities, this suggests 

that the credit bureau score adjusts 75% on its way towards the digital footprint. Figure 7 shows some 

mean reversion in credit bureau scores, with low credit bureau scores getting better on average and high 

credit bureau scores getting worse. To ensure that our results are not purely driven by mean-reversion, we 

control for CreditScoret

 in column (2). As expected, the coefficient decreases but remains both 

economically and statistically highly significant at -28.14. Controlling for month and region fixed effects 

barely changes the coefficient (column (3) of Table 10). The effect is rather monotone across quintiles by 

∆

, 	


, suggesting that effects are not driven only by particularly negative or particularly 

positive digital footprints. Consistent with Figure 7, there is some evidence that the effects are somehow 

larger for lower credit bureau scores (see column (5) and (6) of Table 10), but the digital footprint clearly 

possesses predictive power for future changes in the credit score for higher credit scores as well. 

 Taken together, the evidence suggests that digital footprints today forecast subsequent changes in 

credit bureau scores. This result provides a window into the traditional banking world. As credit bureau 

scores are known to predict default rates for traditional loan products, our results point to the usefulness of 

digital footprints for traditional loan products as well. 

4. Implications for the behavior of consumers, firms, and regulators 

Implications for the behavior of consumers 

Our prior results are subject to the Lucas (1976) critique, with customers potentially changing their 

online behavior if digital footprints are widely used in lending decisions. Some of the digital footprint 

variables are clearly costly to manipulate (such as buying the newest smart device or signing up for a paid 
26 

email account) while others require a customer to change her intrinsic habits (such as impulse shopping or 

making typing mistakes). In the following we lay out two major implications of the Lucas critique: one that 

directly affects the use of digital footprints in lending decisions, and another implication that affects the 

right to free development and expression of one's personality more generally. 

First, in the long-run, the discriminatory power of the digital footprint depends on how easily bad 

types can mimic good types. If mimicking good types is costless, an uninformative pooling equilibrium 

evolves. A sufficiently high cost for mimicking good types results in a separating equilibrium, possibly 

making the digital footprint even more informative than is currently the case (Spence (1973)). Clearly, 

other digital footprint variables may evolve in the future that are costly to manipulate or proxy for a 

person’s innate character. A particular vibrant example is Pentaquark’s scoring model that rejects loans 

from applicants who write a lot about their souls on Facebook, as these people are usually too concerned 

about what will happen in thirty years, but not the fine print of today’s life.25

 

Second, and even more importantly, the repercussions of our findings seem even stronger when 

consumers adapt their behavior in a conformable way. A world of conformity, where consumers fear to 

express their individual personality and act with a permanent desire to portray a positive image to others is 

not the role model of a society that most people think of. This argument becomes even more relevant if 

lenders expand the scope of digital footprint variables they use, the more of our devices are connected to 

the internet, and the more of our personal communication can be traced online. A wider implication of our 

findings is therefore that the use of digital footprints has a considerable impact on everyday life, with 

consumers constantly considering their digital footprints which are so far usually left without any further 

thought. 

Implications for the behavior of firms and regulators 

The Lucas critique applies not only to consumer behavior, but firms and regulators are equally 

likely to react to an increased use of digital footprints. Firms associated with low creditworthiness products 

 

25 See BBVA (2017): The digital footprint: a tool to increase and improve lending, accessed via 

https://www.bbva.com/en/digital-footprint-tool-increase-improve-lending/ 
27 

may object to the use of digital footprints and may conceal the digital footprint of their products. 

Commercial services that offer to manage individual’s digital footprints may develop. On the other hand, 

firms whose products are pooled with low-quality products (for example high-cost Android phones) may 

want to ensure that their digital footprint clearly distinguishes them from lower-reputation products in the 

same category. Overall, similar to our discussion on consumer behavior, the reaction by firms can either 

increase or decrease the predictive power of the digital footprint. 

 Regulators are likely to watch the use of digital footprints closely. Regulators worldwide have long 

recognized the key role of credit scores for consumers’ access to key financial products. As a consequence, 

lending acts worldwide – such as the Equal Credit Opportunities Act in the U.S. – legally prohibit the use 

of variables that can lead to an unfair discrimination for specific borrower groups. Prohibited variables 

usually include variables such as race, color, gender, national origin, and religion. Lenders using digital 

footprints are likely to face scrutiny whether the digital footprint proxies for such information and therefore 

violate fair lending acts (see Fuster et al. (2017) on this issue). It is also conceivable that incumbent 

financial institutions, threatened by competitors using digital footprints, use their well-established access to 

politicians and regulators to lobby for a restriction of the use of digital footprints on these grounds. 

5. Conclusion 

In this paper, we have analyzed the information content of the digital footprint – a trail of 

information that people leave online simply by accessing or registering on a website – for predicting 

consumer default. Using more than 250,000 observations, we show that even simple, easily accessible 

variables from the digital footprint match or exceed the information content of credit bureau scores. 

The correlation between the score based on the digital footprint variables and the credit bureau 

score is approximately 10%. As a consequence, the discriminatory power of a model using both the credit 

bureau score and the digital footprint variables significantly exceeds models that only use the credit bureau 

score or only use the digital footprint variables. This suggests that the digital footprint complements rather 

than substitutes for credit bureau information and a lender that uses information from both sources (credit 
28 

bureau score + digital footprint) can make superior lending decisions compared to lenders that only access 

one of the two sources of information. 

 We also show that the discriminatory power for unscorable customers matches the discriminatory 

power for scorable customers. These results suggest that digital footprints can indeed help to overcome 

information asymmetries between lenders and borrowers when standard credit bureau information is not 

available. Digital footprints thus have the potential to boost access to credit to parts of the currently two 

billion working-age adults worldwide that lack access to services in the formal financial sector, thereby 

fostering financial inclusion and lowering inequality. 

Finally, while consumers might plausibly change their online behavior if digital footprints are 

widely used for lending decisions, we show that some of the digital footprint variables are clearly costly to 

manipulate, but, more importantly, such a change in behavior can lead to a situation where the use of 

digital footprints has a considerable impact on everyday life, with consumers constantly considering their 

digital footprints which are so far usually left without any further thought. Firms and regulators are 

equally likely to react, with firms managing the digital footprint of their products and regulators 

scrutinizing compliance with fair lending acts worldwide. 

Overall, our results have potentially wide implications for financial intermediaries’ business 

models going forward, for access to credit for the unbanked, and for the behavior of consumers, firms, and 

regulators in the digital sphere. 
29 

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31 

Figure 1a: Number of observations per month 

This figure shows the monthly number of observations for scorable customers, for unscorable customers, as well as 

for the total sample. The sample period is from October 19, 2015 to December 2016. The number of observations for 

October 2015 is scaled up by a factor of 31/13 to make it comparable to a monthly figure. For variable definitions see 

Table 1. 

Figure 1b: Geographic distribution of customers in our sample compared to the German population 

This figure illustrates the share of customers by state in our sample compared to the German population by state. 

0 5000 10000 15000 20000 25000 30000

No. of monthly observations

01oct2015 01jan2016 01apr2016 01jul2016 01oct2016 01jan2017

Date

With credit bureau score Without credit bureau score

Total
32 

Figure 2: Credit bureau score distribution and default rates 

This figure shows the distribution of the credit bureau score and the raw and smoothed default rates as a function of 

the credit bureau score. (Default(0/1)) is equal to one if the claim has been transferred to a debt collection agency. The 

smoothed default rates have been determined using a logistic regression and a second-order polynomial of the credit 

bureau score. The sample period is from October 19, 2015 to December 2016. For variable definitions see Table 1. 
33 

Figure 3: Default rates by combinations of digital footprint variables 

This figure shows default rates for combinations of the variables “Operating System” and “Email Host”. The x-axis 

shows default rates, the y-axis illustrates whether the respective dot comes from a single digital footprint variable (for 

example, “Android users”) or whether it comes from a combination of digital footprint variables (for example, 

“Android + Hotmail”). Default rates for credit bureau score deciles are provided as reference points in the row at the 

very bottom. The sample only includes customers with credit bureau scores. The sample period is from October 19, 

2015 to December 2016. For variable definitions see Table 1. 
34 

Figure 4: AUC (Area Under Curve) for scorable customers for various model specifications 

This figure illustrates the discriminatory power of three different model specifications by providing the receiver 

operating characteristics curve (ROC-curve) and the area under curve (AUC). The ROC-curves are estimated using a 

logit regression of the default dummy on the credit bureau score (light gray), the digital footprint (gray), both credit 

bureau score and digital footprint (dark gray). The sample only includes customers with credit bureau scores. The 

sample period is from October 19, 2015 to December 2016. For variable definitions see Table 1. 

Credit 

bureau 

score 

68.3% 

Digital 

Footprint 

69.6% 

Credit bureau 

score + Digital 

Footprint 

73.6% (+5.3 PP) 

45 degree 

line
35 

Figure 5: Correlation between Digital Footprint and Credit Bureau Score (scorable customers) 

This figure illustrates the correlation between the credit bureau score and the digital footprint. The x-axis shows 

percentiles by credit bureau score. The y-axis shows percentiles by the digital footprint. The digital footprint is 

estimated using the results from column (2) of Table 5 and multiplied by minus 1 to ensure the same ordering as the 

credit bureau score (high value = low default probability). The sample only includes customers with credit bureau 

score and is based on a 1% random sample in order to be able to visualize the results. The sample period is from 

October 19, 2015 to December 2016. For variable definitions see Table 1. 

0 20 40 60 80 100

Percentiles by digital footprint

0 20 40 60 80 100

Percentiles by credit bureau score

n = 2488 RMSE = 28.8525

PctByDigitalFootprint = 46.23 + 0.10 PctByCreditScore R

2

 = 1.0%
36 

Figure 6: AUC for scorable vs. unscorable customers 

This figure illustrates the discriminatory power for different samples by providing the receiver operating 

characteristics curve (ROC-curve) and the area under curve (AUC) for scorable customers (light gray) and unscorable 

customers (dark gray). The ROC-curves are estimated using a logistic regression of the default dummy on the digital 

footprint. The sample period is from October 19, 2015 to December 2016. For variable definitions see Table 1. 

 

Unscorable 

customers 

AUC 72.2% 

Scorable 

customers 

AUC 69.6%

45 degree 

line 
37 

Figure 7: Digital footprint and subsequent changes in the credit bureau score 

This figure illustrates the predictive power of the digital footprint for subsequent changes in the credit bureau score. 

The grey line depicts subsequent credit bureau scores when the creditworthiness using the digital footprint is better 

than the creditworthiness using the credit bureau score. The black line depicts subsequent credit bureau scores when 

the creditworthiness using the digital footprint is worse than the creditworthiness using the credit bureau score. Values 

are shown by decile of credit bureau score. 

 92 93 94 95 96 97 98 99 100 Subsequent credit score (0=worst, 100=best)

92 93 94 95 96 97 98 99 100

Credit score (0=worst, 100=best)

Creditworthiness using digital footprint worse than credit score

Creditworthiness using digital footprint better than credit score
38 

Table 1: Description of variables 

Variable Description Unit 

Order, customer, credit bureau score, and payment behavior 

Order amount Purchase amount in EUR Numerical variable 

Gender Gender of customer (female or male) Dummy variable 

Age Age of customer in years. Information about age is obtained 

from the credit bureau. Missing information on age indicate that 

the credit bureau does not have information about a customer’s 

age. 

Numerical variable 

Credit bureau score Credit bureau score. The score is based on credit history data 

from various banks, sociodemographic data, as well as payment 

behavior data sourced from retail sales firms, telecommunication 

companies, and utilities. 

Numerical variable, 

0=worst, 100=best 

Default Dummy variable equal to one if the claim is transferred to a debt 

collection agency (i.e., the customer did not pay the invoice after 

the third reminder of the firm). 

Dummy variable 

Digital footprint variables 

Device Type Device type. Main examples: Desktop, Tablet, Mobile. Categorical variable 

Operating System Operating system. Main examples: Windows, iOS, Android, 

Macintosh. 

Categorical variable 

Email Host Email host. Main examples: Gmx, Web, T-Online, Gmail, 

Yahoo, Hotmail. 

Categorical variable 

Channel Channel through which customer comes to website. Main 

examples: Paid (including paid and retargeted clicks), Direct, 

Affiliate, Organic. 

Categorical variable 

Check-Out Time Time of day of purchase. Numerical variable (0-24hrs)

Do not track setting Dummy equal to one if customer does not allow tracking of 

device and operating system information, and channel. 

Dummy variable 

Name in Email Dummy equal to one if first or last name of customer is part of 

email address. 

Dummy variable 

Number in Email Dummy equal to one if a number is part of email address. Dummy variable 

Is Lower Case Dummy equal to one if first name, last name, street, or city are 

written in lower case. 

Dummy variable 

Email Error Dummy equal to one if email address contains an error in the 

first trial (Note: Clients can only order if they register with a 

correct email address). 

Dummy variable 
Table 2: Descriptive statistics 

This table presents summary statistics for the whole sample. The sample period is from October 19, 2015 to December 2016. Panecustomers with credit bureau score. Panel B provides descriptive statistics for customers without credit bureau score. For variable 

Panel A: Customers with credit bureau score 

Variable Unit N Mean Std. P25 Order and customer 

Order amount Euro 254,808 317.75 317.10 119.99 Gender Dummy (0=male, 1=female) 254,808 0.66 0.47 0 Age# Number 254,604 45.06 13.31 34 Credit bureau score Number (0=worst, 100=best) 254,808 98.11 2.05 97.58 

Payment behavior 

Default Dummy (0/1) 254,808 0.009 0.096 0 

Panel B: Customers without credit bureau score 

Variable Unit N Mean Std. P25 

Order and customerOrder amount Euro 15,591 324.58 319.26 119.99 Gender Dummy (0=male, 1=female) 15,591 0.70 0.46 0 Age# Number 555 38.20 10.46 30 Credit bureau score Number (0=worst, 100=best) 15,591 n.a. n.a. n.a. Payment behaviorDefault Dummy (0/1) 15,591 0.025 0.156 0 

# Based on information from the credit bureau. Missing information on age indicate that the credit bureau does not have information about 

non-missing age in Panel B are cases where the credit bureau has information about the age of the customer, but not enough information to 
40 

Table 3: Credit Bureau score, digital footprint variables, and default rates (scorable customers) 

This table provides default rates by credit bureau score quintile as well as default rates by category of each of the digital 

footprint variables. The sample is based on scorable customers, i.e. the set of customers for which a credit bureau score is 

available. The sample period is from October 19, 2015 to December 2016. For variable definitions see Table 1. 

Variable Value Observations Proportion Default rate T-test against 

Baseline 

Credit Bureau Score All 254,808 100% 0.94% 

(by quintile) Q1 - lowest 50,966 20% 2.12% Baseline 

 Q2 50,964 20% 1.01% *** (14.19) 

 Q3 50,956 20% 0.68% *** (19.51) 

 Q4 50,983 20% 0.47% *** (23.33) 

 Q5 - highest 50,939 20% 0.39% *** (24.93) 

 

Device All 254,808 100% 0.94% 

Desktop 145,871 57% 0.74% Baseline 

Tablet 45,575 18% 0.91% *** (-3.62) 

Mobile 26,808 11% 2.14% *** (-21.84)

Do not track setting 36,554 14% 0.88% *** (-2.90) 

Operating System All 254,808 100% 0.94%

Windows 124,598 49% 0.74% Baseline 

iOS 41,478 16% 1.07% *** (-6.35) 

Android 29,089 11% 1.79% *** (-16.64)

Macintosh 21,162 8% 0.69% (0.79) 

Other 1,927 1% 0.11% * (1.74) 

Do not track setting 36,554 14% 0.88% *** (-2.66) 

Email Host All 254,808 100% 0.94% 

 Gmx (partly paid) 58,609 23% 0.82% Baseline 

 Web (partly paid) 54,864 22% 0.86% (-0.70) 

 T-Online (affluent customers) 30,277 12% 0.51% *** (5.32) 

 Gmail (free) 27,842 11% 1.25% *** (-6.02) 

 Yahoo (free, older service) 11,922 5% 1.96% *** (-11.33) 

 Hotmail (free, older service) 10,241 4% 1.45% *** (-6.11) 

 Other 61,053 24% 0.90% (-1.38) 

Channel All 254,808 100% 0.94% 

Paid 111,398 44% 1.11% Baseline

 Direct 45,178 18% 0.84% *** (4.77) 

 Affiliate 24,769 10% 0.64% *** (6.68) 

Organic 18,295 7% 0.86% *** (3.00)

 Other 18,614 7% 0.69% *** (5.24) 

 Do not track setting 36,554 14% 0.88% *** (3.68) 

Check-Out Time All 254,808 100% 0.94% 

 Evening (6pm-midnight) 108,543 43% 0.85% Baseline 

 Night (midnight-6am) 6,913 3% 1.97% *** (-9.49) 

 Morning (6am-noon) 46,600 18% 1.09% *** (-4.55) 

 Afternoon (noon-6pm) 92,752 36% 0.89% (-0.91) 

 

Do not track setting All 254,808 100% 0.94% 

 No 218,254 86% 0.94% Baseline 

 Yes 36,554 14% 0.88% (1.12) 

 

Name in Email All 254,808 100% 0.94% 

 No 71,016 28% 1.24% Baseline 

 Yes 183,792 72% 0.82% *** (9.99) 

 

Number in Email All 254,808 100% 0.94% 

 No 213,640 84% 0.84% Baseline 

Yes 41,168 16% 1.41% *** (-10.95)

 

Is Lower Case All 254,808 100% 0.94% 

No 235,560 92% 0.84% Baseline

 Yes 19,248 8% 2.14% *** (-18.07) 

 

Email Error All 254,808 100% 0.94%

 No 251,308 99% 0.88% Baseline 

 Yes 3,500 1% 5.09% *** (-25.71) 
Table 4: Correlation/Association between credit bureau score and digital footprint variables (sc

This table provides a measure of association, Cramér’s V, between credit bureau score quintiles and the digital footprint vassociation between two categorical variables and is bounded between [0,1], with 0 denoting no association and 1 den

calculation of Cramér’s V, the credit bureau score has been transformed into categories by forming quintiles of the credit digital footprint variables are nominal categorical variables so that Pearson’s correlation coefficient or Spearman’s rank cosample is based on scorable customers, i.e. the set of customers for which a credit bureau score is available. The sample December 2016. For variable definitions see Table 1. 

Credit 

bureau 

score quintile

1

Device Type 

Operating System 

Email Host Channel 

CheckOut Time

1

Name in 

Email 

Numb

in Em

Credit bureau score quintile

1 1.00*** 0.07*** 0.05*** 0.07*** 0.03*** 0.03*** 0.01*** 0.07*

Device Type 1.00*** 0.71***

2 0.07*** 0.06***

2 0.04*** 0.05*** 0.06*

Operating System 1.00*** 0.08*** 0.06***

2 0.04*** 0.06*** 0.08*

Email Host 1.00*** 0.03*** 0.03*** 0.08*** 0.18*

Channel 1.00*** 0.02*** 0.01*** 0.02*

 Check-Out Time

1 1.00*** 0.01*** 0.01*

Name in Email 1.00*** 0.22*

Number in Email 1.00*

Is Lower Case 

Email Error 

1. Transformed into quintiles. 2. We exclude customers with a do not track setting, as the setting simultaneously applies to device, operating system, and channel info
42 

Table 5: Default regressions (scorable customers) 

We estimate default rate regressions where the dependent variable (Default(0/1)) is equal to one if the claim has been transferred to 

a debt collection agency. Column (1) provides results using the credit bureau score as the independent variable, column (2) provides 

results using the digital footprint variables as independent variables, column (3) uses both the credit bureau score and the digital 

footprint variables as independent variables, and column (4) adds age and month and region fixed effects. All models are estimated 

using a logistic regression model. The sample is based on scorable customers, i.e. the set of customers for which a credit bureau 

score is available. The sample period is from October 19, 2015 to December 2016. For variable definitions see Table 1. 

(1) (2) (3) (4) 

Credit bureau score Digital footprint 

Credit bureau score 

& digital footprint 

Credit bureau score & 

Digital footprint, fixed 

effects 

VARIABLES Coef. z-stat Coef. z-stat Coef. z-stat Coef. z-stat 

 Credit bureau score -0.17*** (-7.89) -0.15*** (-6.67) -0.14*** (-5.83) 

 Computer & Operating system 

 Desktop/Windows Baseline Baseline Baseline 

 Desktop/Macintosh -0.07 (-0.53) -0.13 (-1.03) -0.17 (-1.35) 

 Tablet/Android 0.29*** (3.19) 0.29*** (3.06) 0.30*** (3.25) 

 Tablet/iOS 0.08 (1.05) 0.08 (0.97) 0.06 (0.81) 

 Mobile/Android 1.05*** (17.26) 0.95*** (15.34) 0.97*** (15.53) 

 Mobile/iOS 0.72*** (9.07) 0.57*** (6.73) 0.56*** (6.65) 

 Email Host 26

 Gmx (partly paid) Baseline Baseline Baseline 

 Web (partly paid) -0.00 (-0.01) -0.02 (-0.22) -0.01 (-0.07) 

 T-Online (affluent customers) -0.40*** (-3.89) -0.35*** (-3.34) -0.27** (-2.53) 

 Gmail (free) 0.34*** (3.79) 0.28*** (3.08) 0.28*** (2.98) 

 Yahoo (free, older service) 0.75*** (9.19) 0.72*** (8.98) 0.72*** (8.71) 

 Hotmail (free, older service) 0.35*** (3.70) 0.28*** (2.73) 0.25** (2.37) 

 Channel 

 Paid Baseline Baseline Baseline 

 Affiliate -0.49*** (-5.33) -0.54*** (-5.56) -0.56*** (-5.67) 

 Direct -0.27*** (-4.24) -0.28*** (-4.43) -0.19*** (-3.10) 

 Organic -0.15* (-1.79) -0.15* (-1.73) -0.13 (-1.52) 

 Check-Out Time 

 Evening (6pm-midnight) Baseline Baseline Baseline 

 Morning (6am-noon) 0.28*** (4.52) 0.28*** (4.62) 0.29*** (4.77) 

 Afternoon (noon-6pm) 0.08 (1.42) 0.08 (1.47) 0.09* (1.77) 

 Night (midnight-6am) 0.80*** (7.74) 0.75*** (7.11) 0.73*** (6.86) 

 Do not track setting -0.02 (-0.25) -0.07 (-0.90) -0.05 (-0.63) 

 Name In Email -0.28*** (-5.67) -0.29*** (-5.69) -0.29*** (-5.81) 

 Number In Email 0.26*** (4.50) 0.23*** (3.92) 0.22*** (3.82) 

 Is Lower Case 0.76*** (13.04) 0.74*** (13.16) 0.74*** (13.25) 

 Email Error 1.66*** (20.01) 1.67*** (20.37) 1.70*** (20.56) 

Constant 12.43*** (5.77) -4.92*** (-62.84) 9.97*** (4.49) 10.46*** (4.76) 

Control for age No No No Yes 

Month & region fixed effects No No No Yes 

Observations 254,808 254,808 254,808 254,604 

Pseudo R20.0244 0.0525 0.0718 0.0808 

AUC 0.683 0.696 0.736 0.749 

(SE) (0.006) (0.006) (0.005) (0.005) 

Difference to AUC=50% 0.183*** 0.196*** 0.236*** 0.249*** 

Difference AUC to (1) 0.013* 0.053*** 0.067*** 

 

26 We only report coefficients for the 6 largest email providers even though we use the largest 18 categories in the regression (all 

email providers with at least 1000 observations). Using only the 6 reported email hosts does not affect our results. 
43 

Table 6: Digital footprint variables and default rates (unscorable customers) 

This table provides default rates by category of each of the digital footprint variables. The sample is based on 

unscorable customers, i.e. the set of customers for which a credit bureau score is not available. The sample period is 

from October 19, 2015 to December 2016. For variable definitions see Table 1. 

Variable Value Observations Proportion Default rate

T-test against 

baseline

Device All 15,591 100% 2.49% 

Desktop 9,191 59% 2.15% Baseline 

Tablet 2,618 17% 1.64% (1.63) 

Mobile 1,546 10% 6.21% *** (-9.07) 

Do not track setting 2,236 14% 2.28% (-0.37) 

Operating System All 15,591 100% 2.49% 

Windows 7,770 50% 2.19% Baseline 

iOS 2,424 16% 2.35% (-0.48) 

Android 1,646 11% 4.80% *** (-6.01) 

Macintosh 1,421 9% 1.69% (1.20) 

Other 94 1% 7.44% *** (-3.42)

Do not track setting 2,236 14% 2.28% (-0.26) 

Email Host All 15,591 100% 2.49%

 Gmx (partly paid) 3,681 24% 2.42% Baseline 

 Web (partly paid) 3,352 21% 2.63% (-0.55) 

T-Online (affluent customers) 1,711 11% 1.52% ** (2.13)

 Gmail (free) 1,694 11% 3.60% ** (-2.44) 

 Yahoo (free, older service) 732 5% 3.14% (-1.14) 

 Hotmail (free, older service) 546 4% 2.75% (-0.46)

 Other 3,875 25% 2.22% (0.57) 

Channel All 15,591 100% 2.49% 

 Paid 6,447 41% 2.89% Baseline 

 Direct 3,262 21% 1.87% *** (3.00) 

 Affiliate 1,395 9% 2.65% (0.47) 

 Organic 1,178 8% 2.55% (0.64) 

 Other 1,073 7% 2.14% (1.36) 

 Do not track setting 2,236 14% 2.28% *** (1.51) 

 

Check-Out Time All 15,591 100% 2.49% 

Evening (6pm-midnight) 6,349 41% 2.05% Baseline 

Night (midnight-6am) 369 2% 3.52% ** (-1.91) 

Morning (6am-noon) 2,960 19% 2.74% ** (-2.08)

 Afternoon (noon-6pm) 5,913 38% 2.77% *** (-2.63) 

Do not track setting All 15,591 100% 2.49%

 No 13,355 86% 2.52% Baseline 

 Yes 2,236 14% 2.28% (0.68) 

Name in Email All 15,591 100% 2.49% 

 No 4,433 28% 3.93% Baseline 

 Yes 11,158 72% 1.92% *** (7.27)

 

Number in Email All 15,591 100% 2.49% 

 No 12,967 83% 1.99% Baseline 

 Yes 2,624 17% 4.95% *** (-8.91) 

 

Is Lower Case All 15,591 100% 2.49% 

 No 14,566 93% 2.21% Baseline 

 Yes 1,025 7% 6.44% *** (-8.42) 

 

Email Error All 15,591 100% 2.49% 

 No 15,305 98% 2.31% Baseline 

 Yes 286 2% 12.24% *** (-10.72) 
44 

Table 7: Default regressions (unscorable customers) 

This table provides regression results for the same model specifications as in Table 5 using the sample of unscorable

customers. Since this sample only includes customers for whom credit bureau scores are unavailable, the first column from 

the initial specification is omitted. We estimate default rate regressions where the dependent variable (Default(0/1)) is equal 

to one if the claim has been transferred to a debt collection agency. Column (1) provides results using the digital footprint 

variables as independent variables. Column (2) provides a comparison to the results for the sample of scorable customers. 

Column (3) adds month and region fixed effects. All models are estimated using a logistic regression model. The sample is 

based on unscorable customers, i.e. the set of customers for which a credit bureau score is not available. The sample period is 

from October 19, 2015 to December 2016. For variable definitions see Table 1. 

(1) (2) (3) 

 

Digital footprint for 

unscorable customers 

For comparison: Digital 

footprint for scorable 

customers (column (2) 

of Table 5) 

Digital footprint for 

unscorable 

customers, fixed 

effects 

VARIABLES Coef. z-stat Coef. z-stat Coef. z-stat

 Computer & Operating system 

 Desktop/Windows Baseline Baseline Baseline 

 Desktop/Macintosh -0.26 (-1.10) -0.07 (-0.53) -0.22 (-0.93) 

 Tablet/Android -0.22 (-0.85) 0.29*** (3.19) -0.18 (-0.72) 

 Tablet/iOS -0.45* (-1.71) 0.08 (1.05) -0.47* (-1.76) 

 Mobile/Android 1.07*** (5.98) 1.05*** (17.26) 1.05*** (5.56) 

 Mobile/iOS 0.63*** (2.69) 0.72*** (9.07) 0.60** (2.42) 

 Email Host 27

 Gmx Baseline Baseline Baseline 

 Web 0.02 (0.11) -0.00 (-0.01) 0.02 (0.13) 

 T-Online -0.39 (-1.14) -0.40*** (-3.89) -0.44 (-1.29) 

 Gmail 0.32 (1.36) 0.34*** (3.79) 0.32 (1.35) 

 Yahoo 0.17 (0.61) 0.75*** (9.19) 0.14 (0.45) 

 Hotmail -0.02 (-0.06) 0.35*** (3.70) -0.09 (-0.31) 

 Channel 

 Paid Baseline Baseline Baseline 

 Affiliate -0.08 (-0.39) -0.49*** (-5.33) -0.02 (-0.11) 

 Direct -0.42** (-2.35) -0.27*** (-4.24) -0.39** (-2.08) 

 Organic -0.05 (-0.24) -0.15* (-1.79) 0.03 (0.12) 

 Check-Out Time 

 Evening (6pm-midnight) Baseline Baseline Baseline 

 Morning (6am-noon) 0.30* (1.81) 0.28*** (4.52) 0.29* (1.74) 

 Afternoon (noon-6pm) 0.39*** (2.71) 0.08 (1.42) 0.38*** (2.63) 

 Night (midnight-6am) 0.45 (1.38) 0.80*** (7.74) 0.43 (1.36) 

 Do not track setting -0.16 (-0.83) -0.02 (-0.25) -0.12 (-0.59) 

 Name In Email -0.59*** (-4.68) -0.28*** (-5.67) -0.55*** (-4.33) 

 Number In Email 0.63*** (4.32) 0.26*** (4.50) 0.64*** (4.35) 

 Is Lower Case 0.95*** (5.44) 0.76*** (13.04) 0.90*** (4.78) 

 Email Error 1.66*** (7.82) 1.66*** (20.01) 1.67*** (7.33) 

Constant -3.80*** (-19.20) -4.92*** (-62.84) -4.11*** (-14.33) 

Month & region fixed effects No No Yes 

Observations 15,591 254,808 15,591 

Pseudo R20.0907 0.0525 0.139 

AUC 0.722 0.696 0.777 

(SE) (0.014) (0.006) (0.012) 

Difference to AUC=50% 0.222*** 0.196*** 0.277*** 

 

27 We only report coefficients for the 6 largest email providers even though we use the largest 18 categories in the 

regression (all email providers with at least 1000 observations). Using only the 6 reported email hosts does not 

significantly affect the results. 
45 

Table 8: Out-of-sample estimates 

This table provides robustness tests out-of-sample for all main regression specifications. Panel 1 reports AUCs for scorable 

customers for the model specifications from Table 5, and Panel 2 reports AUCs for unscorable customers for the model 

specifications from Table 7. Column (1) reports the baseline results for scorable and unscorable customers. Column (2) reports outof-sample estimates of the AUC using Nx2-fold cross validation. We thereby randomly divide the full sample into half samples A 

and B. We then estimate a predictive logistic regression using sample A and use the coefficients to create predicted values for 

observations in sample B. We also estimate a predictive regression using sample B and use the coefficients to create predicted 

values for observations in sample A. We then determine the AUC for the full sample of observations, using all predicted values 

estimated out-of-sample. The AUCs reported in Column (2) are the mean AUCs from 100 iterations. The sample period is from 

October 19, 2015 to December 2016. 

(1) 

Baseline 

(In-sample) 

(2) 

Out-of-sample 

Panel 1: Scorable customers 

AUC credit bureau score 0.683 0.680 

N 254,808 254,808 

AUC Digital Footprint 0.696 0.688

N 254,808 254,808

AUC credit bureau score + Digital Footprint 0.736 0.728

N 254,808 254,808

AUC credit bureau score + Digital Footprint, 

fixed effects 0.749 0.721

N 254,604 254,604

Panel 2: Unscorable customers

AUC Digital Footprint 0.722 0.683

N 15,591 15,591

AUC Digital Footprint, fixed effects 0.777 0.647 

N 15,591 15,591
46 

Table 9: Robustness tests (scorable customers) 

This table provides robustness tests using alternative default definitions as well as various sample splits. Panel A provides results 

using alternative default definitions. Column (1) reports results using the standard default definition (default = transfer to debt 

collection agency), column (2) provides a stricter default definition (default = no full repayment after attempts of debt collection 

agency). Column (3) uses only the sample of defaulted loans and uses the loss given default as the dependent variable. In column (3) 

we report the R-squared instead of the AUC, as column (3) is estimated using a linear regression model while all other models are 

estimated using a logistic regression. Panel B provides results for various sample splits. All models are estimated using a logistic 

regression model; apart from column (3) in Panel A, which is estimated using a linear regression model. The sample is based on 

scorable customers, i.e. the set of customers for which a credit bureau score is available. The sample period is from October 19, 

2015 to December 2016. For variable definitions see Table 1. 

 

Panel A: Default definition (1) (2) (3) 

Baseline 

(Default = Transfer to 

collection agency) 

Default = Writedown Loss given default 

(R2 reported) 

AUC credit bureau score 0.6826 0.6918 0.0126 

AUC Digital footprint 0.6960 0.7232 0.0650 

AUC credit bureau score + 

digital footprint 

0.7360 0.7564 0.0715 

N 254,808 254,808 2,384 

Panel B: Sample splits (1) (2) (3) (4) 

Small orders 

< EUR 218.92 

Large orders 

≥ EUR 218.92 

Female Male 

AUC credit bureau score 0.6878 0.6784 0.6893 0.6696 

AUC Digital footprint 0.7126 0.6910 0.6997 0.6999 

AUC credit bureau score + 

digital footprint 

0.7497 0.7306 0.7448 0.7245 

N 127,404 127,404 168,366 86,442 
47 

Table 10: Predicting changes in credit bureau score with the digital footprint 

This table provides a linear regression of changes in credit bureau scores on the difference between the default probability using 

the digital footprint and the default probability using the credit bureau score. The independent variable ∆(DigitalFootprintt, 

CreditScoret) measures the difference in predicted values of column (2) of Table 5 and the predicted values of column (1) of Table 

5. The dependent variable (∆ (CreditScoret+1, CreditScoret)) measures the change in credit bureau score between i) the credit 

bureau score as of the date of purchase from Table 5 and ii) the latest available credit bureau score up to March 2018. Two credit 

bureau scores at two different dates are only available when customers return to the E-Commerce company at least once between 

October 2015 and March 2018. Column (2) adds the initial credit bureau score as a control variable, column (3) adds month and 

region fixed effects, column (4) displays results by quintile of ∆(DigitalFootprintt

, CreditScoret), and columns (5) and (6) split the 

sample at the median credit bureau score. For variable definitions see Table 1. 

(1) (2) (3) (4) (5) (6)

Sample All All All All CreditScoret < 

Median 

CreditScoret ≥ 

Median 

Dependent variable ∆ 

(CreditScoret+1, 

CreditScoret) 

∆ 

(CreditScoret+1, 

CreditScoret) 

∆ 

(CreditScoret+1, 

CreditScoret) 

∆ 

(CreditScoret+1, 

CreditScoret) 

∆ 

(CreditScoret+1, 

CreditScoret) 

∆ 

(CreditScoret+1, 

CreditScoret) 

∆ (DigitalFootprintt, 

CreditScoret

) 

-74.56*** 

(-11.71) 

-28.14*** 

(-4.56) 

-29.74*** 

(-4.95) 

 -34.24*** 

(-4.23) 

-20.75** 

(-2.48) 

Q1 (-100% to -0.49%) 0.39** 

(2.38) 

Q2 (-0.49% to -0.25%) 0.15* 

(1.74) 

Q3 (-0.25% to -0.05%) baseline 

Q4 (-0.05% to +0.35%) 0.08 

(0.92)

Q5 (+0.35% to +100%) -0.39*** 

(-3.05) 

CreditScoret-0.43*** 

(-13.81)

-0.42*** 

(-13.66)

-0.42*** 

(-10.27)

-0.42*** 

(-8.38)

-0.19 

(-1.37)

Constant 0.37*** 

(8.64) 

42.31*** 

(13.84) 

absorbed absorbed absorbed absorbed 

Month & region fixed effects No No Yes Yes Yes Yes

Observations 17,645 17,645 17,645 17,645 8,853 8,792 

Adj. R2

2.74% 6.95% 7.95% 7.92% 7.13% 1.54% 
48 

Appendix 

Table A.1: Comparability of default rates to other retail data sets 

Study Sample Default rate Time horizon Default rate 

(annualized) 

This study 

This study 270,399 purchases at a German Ecommerce company between October 

2015 and December 2016 

1.0% ~4 months 3.0% 

Germany 

Berg, Puri, and Rocholl 

(2017) 

100,000 consumer loans at a large 

German private bank, 2008-2010 

2.5% 12 months 2.5% 

Puri, Rocholl, and Steffen 

(2017) 

1 million consumer loans at 296 

German savings banks, 2004-2008 

1.1% 12 months 1.1% 

Schufa (2017)1

 – study by the 

major credit bureau in 

Germany 

17.4 million consumer loans covered 

by the main credit bureau in 

Germany in 2016 

2.2% 12 months 2.2% 

Schufa (2016)1– study by the 

major credit bureau in 

Germany 

17.3 million consumer loans covered 

by the main credit bureau in 

Germany in 2015 

2.4% 12 months 2.4% 

Deutsche Bank (2016)2 All retail loans of Deutsche Bank (i.e, 

the largest German bank) 

1.5% 

(Basel II PD 

estimate) 

12 months 1.5% 

Commerzbank (2016)3

 All retail loans of Commerzbank 

(i.e., the second largest German 

bank) 

2.0% 

(Basel II PD 

estimate) 

12 months 2.0% 

United States 

Federal reserve4 Charge-off rate on consumer loans, 

Q4/2016 

2.09% 12 months 

(annualized 

quarterly data) 

2.09% 

Federal reserve4 Charge-off rate on consumer loans, 

Q4/2015 

1.76% 12 months 

(annualized 

quarterly data) 

1.76% 

Hertzberg, Liberman, and 

Paravisini (2016) 

12,091 36-months loans from 

Lending Club issued between 

December 2012 and February 2013 

9.2% ~26 months 4.2% 

Lending Club (own analysis) 375,803 36-month loans from 

Lending Club issued between 

October 2015 and December 2016 

5.11% 12 months 5.11% 

Iyer, Khwaja, Luttmer, and 

Shue (2016) 

17,212 36-months loans from 

Prosper.com issued between 

February 2007 and October 2008 

30.6% 36 months 10.2% 

Puri, Hildebrandt, and 

Rocholl (2017) 

12,183 loans from Prosper.com 

between February 2007- April 2008 

10.8%-18.6% per 1,000 days 3.9%-6.8% 

1. Schufa is the main credit bureau in Germany, comparable to Fair Isaac Newton in the U.S., for example, For data on 2016 default rates see Figure 2.11 on page 17 in 

https://www.schufa.de/media/editorial/themenportal/kredit_kompass_2017/SCHUFA_Kredit-Kompass_2017_neu.pdf . For the data on 2016 default rates see Figure 2.11 on 

page 18 in https://www.schufa.de/media/editorial/ueber_uns/bilder/studien_und_publikationen/kredit_kompass/SCHUFA_Kredit-Kompass-2016.pdf (available in German 

only). 

2. See Table on page 90 in Deutsche Bank’s Pillar 3 Report 2016, available via https://www.db.com/ir/en/download/Deutsche_Bank_Pillar_3_Report_2016.pdf 

3. See Table 12 on page 34 in Commerzbank’s Disclosure Report 2016, available via 

https://www.commerzbank.de/media/aktionaere/service/archive/konzern/2017/Disclosure_Report_2016.pdf 

4. Series “CORCABAS” in FRED, see https://fred.stlouisfed.org/series/CORCACBS 
49 

Table A.2: Comparability of Area-Under-Curve to other retail data sets 

Study Sample AUC using credit 

score 

Area Under the Curve (AUC) using credit bureau scores only 

This study 270,399 purchases at a German ECommerce company in 2015/2016 68.3% 

Berg, Puri, and Rocholl (2017)#100,000 consumer loans at a large 

German private bank, 2008-2010

66.6%

Puri, Rocholl, and Steffen (2017)#

 1 million consumer loans at 296 German 

savings banks, 2004-2008 

66.5% 

Iyer, Khwaja, Luttmer, and Shue 

(2016) 

17,212 36-months loans from 

Prosper.com issued between February 

2007 and October 2008 

62.5% 

Lending Club (own analysis) 375,803 36-month loans from Lending 

Club issued between October 2015 and 

December 20161

59.8% 

 

AUC and changes in the Area Under the Curve using other variables in addition to the credit score 

 AUC Change Combined AUC 

This study Digital footprint versus credit bureau 

score only 

+ 5.3PP 73.6% 

Berg, Puri, and Rocholl (2017)#

 Bank internal rating (which includes 

credit bureau score) versus credit bureau 

score only 

+8.8PP 75.4% 

Puri, Rocholl, and Steffen (2017)# Bank internal rating (which includes 

credit bureau score) versus credit bureau 

score only 

+11.9PP 78.4% 

Iyer, Khwaja, Luttmer, and Shue 

(2016) 

Interest rates versus credit score only +5.7PP 68.2% 

Iyer, Khwaja, Luttmer, and Shue 

(2016) 

All available financial and coded 

information (including credit score) 

versus credit score only 

+8.9PP 71.4% 

Lending Club (own analysis) Lending Club loan grade (which includes 

credit score) versus credit score only 

+11.9PP 71.7% 

# These results are not in the original papers but were provided to us by the authors using exactly the same data set from the paper. 

1. Results are very similar for 60-month loans. 
50 

Table A.3: Descriptive statistics for computer and operating system category (scorable customers) 

This table provides descriptive statistics for the computer and operating system category. The sample is based on 

scorable customers, i.e. the set of customers for which a credit bureau score is available. The sample period is from 

October 19, 2015 to December 2016. For variable definitions see Table 1. 

Variable Value Observations Proportion Default rate T-test against 

baseline 

 Computer and Operating system All 254,808 100% 0.94% 

Desktop/Windows 123,085 48% 0.74% Baseline

Desktop/Macintosh 21,158 8% 0.69% (0.70) 

Desktop/other 1,628 1% 0.74% (0.03) 

Tablet/Android 15,111 6% 1.11% *** (-4.86)

Tablet/iOS 29,940 12% 0.79% (-0.88) 

Tablet/other 524 0% 1.53% ** (-2.08) 

Mobile/Android 13,967 5% 2.53% *** (-20.92) 

Mobile/iOS 11,531 5% 1.80% *** (-11.90) 

Mobile/other 1,310 1% 1.15% * (-1.68) 

Do not track setting 36,554 14% 0.88% *** (-2.70) 

Table A.4: Descriptive statistics for computer and operating system category 

(unscorable customers) 

This table provides descriptive statistics for the computer and operating system category. The sample is based on 

unscorable customers, i.e. the set of customers for which a credit bureau score is not available. The sample period is 

from October 19, 2015 to December 2016. For variable definitions see Table 1. 

Variable Value Observations Proportion Default rate

T-test against 

baseline

 Computer and Operating system All 15,591 100% 2.49% 

Desktop/Windows 7,688 49% 2.20% Baseline 

Desktop/Macintosh 1,421 9% 1.69% (1.22) 

Desktop/other 82 1% 6.10% ** (-2.37) 

Tablet/Android 857 5% 2.10% (0.19) 

Tablet/iOS 1,737 11% 1.44% ** (2.01) 

Tablet/other 24 0% 0.00% (0.73) 

Mobile/Android 789 5% 7.73% *** (-9.15) 

Mobile/iOS 687 4% 4.66% *** (-4.04) 

Mobile/other 70 0% 4.43% (-1.18) 

Do not track setting 2,236 14% 2.28% (-0.23)