On the Rise of FinTechs –

Credit Scoring using Digital Footprints

Tobias Berg, Frankfurt School of Finance & Management

Valentin Burg, Humboldt University Berlin

Ana Gombović, Frankfurt School of Finance & Management

Manju Puri, Duke University, FDIC and NBER
Motivation

• Digital footprint: Trace of simple, easily accessible information about almost

every individual worldwide

• One key reason for existence of financial intermediaries/banks: Superior 

ability to access and process information for screening borrowers

• This paper: How much information does simply surfing the internet and 

registering on websites leave behind? How well does it predict default rates?

• Wide implications

– Financial intermediaries‘ business models

– Access to credit for unbanked

– Behavior of consumers, firms, and regulators in the digital sphere

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Motivation: New York – Use of operating systems

3 Source: Gnip, MapBox, Eric Fischer, Data 2011-2013

Red = iOS, Green = Android, Purple = Blackberry

Information about customers’ operating system available to every website without any effort
Digital footprint – 10 easily accessible variables

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Bivariate results

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Credit bureau 

score, highest 

decile

Credit bureau 

score, lowest 

decile

Yahoo

Android

T-online

Mac + 

T-online

Android + 

Yahoo

Android + 

Hotmail

Windows + 

T-online

Mac

Single variable: Operating System

Single variable: Email Host

Digital Footprint variable(s) Deciles by credit bureau score

iOS

Hotmail
Judging discriminatory power: AUC

• Method: logistic regression with default dummy as the dependent variable

• Formal analysis of discriminatory power: Receiver Operating 

Characteristics (ROC) and Area-under-the-Curve (AUC) 

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0.00 0.25 0.50 0.75 1.00

0.00

0.25

0.50

0.75

1.00

Percentile by score (worst to best)

Proportion of defaults 

Lowest 25% by 

score cover 65% 

of defaults ROC

AUC (greyshaded area)

• Range: 50% (random prediction) to ~ 100% 

(perfect prediction)

• Closely related to GINI: GINI = 2·AUC – 1

• Interpretation: Probability of correctly 

identifying good case if faced with random 

(good, bad)-pair

• Iyer, Khwaja, Luttmer, Shue (2016): 60% 

desirable in information-scarce environments, 

70% in information-rich environments

• See also Vallee and Zeng (2018) and Fuster, 

Plosser, Schnabl, and Vickery (2018)

0.65
Area-under-Curve: Credit bureau score versus digital footprint

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External validity: Idea

• Evidence so far: Predictive power of digital footprint for shortterm loans for products purchased online

• Now: Test whether digital footprint with predictive power for 

traditional loan products as well. 

• Idea: Does the digital footprint predict future changes in the 

credit bureau score? Answer is yes.

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Economic impact of introducing digital footprint: 

Default rates go down

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October 19, 2015 = Introduction of digital 

footprint and extension of bureau score 

Pre-October 19: 

• No digital footprint

• Credit bureau score for >€1100 and “unknowns”

(“unknowns = customer not known to basic credit 

bureau”)

Post-October 19:

• Digital footprint for every observation

• Credit bureau score for every observation
Access to credit for unbanked

• Two billion working-age adults lack access to financial services

• High expectations in digital footprints 

– World Bank: “Can digital footprints lead to Greater Financial Inclusion?”

– Harvard Business Review: Fintech Companies Could Give Billions of People More Banking Options

– Prior evidence on availability of credit and credit scores (Japelli and Pagano, 1993; Brown, Japelli, 

and Pagano, 2009; Djankov, McLiesh, and Shleifer, 2009; Beck, Demirguc-Kunt, and Honohan, 2009)

• Our paper: Digital footprint help to alleviate credit constraints for unscorables

– ~6% of our sample: no credit bureau score (but: existence of customer confirmed and customer not in 

private bankruptcy)

– Discriminatory power for unscorable customers is similar

– Digital footprint helps to access credit for this sample

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Unscorable vs. scorable customers: AUC comparison 

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Implication 3: Behavior of consumers, firms, and 

regulators in digital sphere

• Lucas critique: Change in consumers behavior if digital footprint is used by intermediaries

– Some variables costly to manipulate

– Others require change in consumer habits

• If Lucas critique applies

– Risk of costly signaling equilibrium (Spence 1973): expensive suit vs. expensive phone

– Lucas critique: consumers react to use of digital footprint. Implication: considerable 

impact on everyday’ s life

• Beyond consumer behavior

– Firms: Response by firms associated with low-creditworthiness products

– Regulators: May intervene in case of violation of fair lending acts, incumbant banks 

might lobby regulators to intervene

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Conclusion

• Is digital footprint useful for predicting payment behavior?

– Simple, easily accessible variables with predictive power as credit 

bureau score

– Complement rather than substitute to credit bureau score

– Works equally well for unscorable customers

• Potentially wide implications 

– Financial intermediaries’ business model: Digital footprint helps to 

overcome information asymmetries between lenders and borrowers

– Access to credit for the unbanked

– Behavior of consumers, firms, and regulators in the digital sphere

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