Multi-View Risk Classification for Customer Due Diligence
Juan Manuel Gonzalez Huesca1,3 , Simon van der Zon1 ,Werner van Ipenburg2 , Jan Veldsink2 , Mykola Pechenizkiy1
1

Department of Computer Science, TU Eindhoven, the Netherlands
2
Rabobank Compliance, the Netherlands
3
Data Science and Engineering group, ASML Netherlands B.V., the Netherlands

Abstract
In the last years, it has become critical for financial institutions to develop efficient solutions that leverage all available
heterogeneous customer data. While financial crime is growing in scale and complexity, financial analysts require a new
set of tools to protect and shield the financial system against
illegal activities. We propose a multi-view semi-supervised
risk classification approach that makes use of text and financial transactions data for Customer Due Diligence (CDD)
reporting. We conducted a case study which illustrates thatby using multiple learning sources (views) at the same time,higher performance is achieved compared to models learnedon each source individually. Furthermore, our system allows
to explain why and how the prediction was done thus providing new insight to domain experts.

Introduction
The Customer Due Diligence (CDD) report is a key requirement of the Know Your Customer (KYC) processes, as itprovides a complete overview to assess customer risk basedon different characteristics. These processes are the cornerstone for effective anti-money laundering and counterterrorism financing programs, including other illegal activities.
There are numerous manual steps in the current processes
and most of the CDD report is done manually by domainexperts, leading to inconsistent results and low efficiency,
while only a small set of all the available data is used. Another challenge is the complexity of manually extracting
knowledge from big data and finding patterns, this is an extremely difficult if not impossible task for these domain experts.
Recently, financial institutions have strengthened theKYC (PricewaterhouseCoopers 2014) processes and due totheir importance, we were motivated to find more robust
artificial intelligence (AI) solutions that leverage the highamount of available data: unstructured (text) and structured(financial transactions).
We proposed an AI solution that helps automating a risk
classification task which provides more insight to CDD exCopyright c 2020, Association for the Advancement of Artificial
Intelligence (www.aaai.org). All rights reserved.

perts. The nature of these data do not allow for training one
model to leveraging the structural properties of the different
sources. Therefore, a multi-view learning paradigm has been
implemented which outperformed single-view learning scenarios in many fields (Xu, Tao, and Xu 2013). This implementation also considers a semi-supervised learning schema
which only needs a relatively small initial labeled training
set and that leverages a large amount of heterogeneous unlabeled data. Another major consideration was the need of aninterpretable model. The goal was to augment the capabilities of domain experts with new knowledge by displaying
the top features (per view) driving the classification.

Framework
The proposed framework implements co-training, one of themost popular multi-view learning algorithms (Li, Li, and Fu
2016). As we focused on improving the process for the CDD
report, while including the domain experts in the knowledge discovery process, co-training was chosen because it
is a model-based integration method and has a good tradeoff between classification accuracy and model interpretability (Molnar 2018).
In a co-training schema, two classifiers are trained independently, after which the labels predicted by each of them(one per data view) help each other to retrain themselves andaugment the labeled dataset (as shown in Figure 1). This allowed us to select a more appropriate classifier per data type(text or financial transactions).
This framework provides flexibility to augment the labeled dataset based on prediction certainty: we only consider the predicted instances with the highest probability forboth classes to be included in the labeled dataset. By using
this approach, as the number of training and predicting iterations increases, the performance is better and our solution
is more robust.
We assume to have an instance space O = O1 +O2 , whereO1 and O2 represent the different views (text and financialtransactions) of the same instance (customer). In this way,
every training instance o has two components: o1 , o2 . One
key assumption is that every view contains enough data tobuild an initial classification model.

Figure 1: Framework for multi-view predictive analytics.

Text classification analysis (unstructured data)
Text represents most of the information available in theworld; this may be the reason why text mining has become very important and why many companies and researchgroups are very interested in finding ways to leverage andextract value from it for many use cases (Miner et al. 2012).
This provides us with strong reasons to focus on and leverage the potential of text in knowledge discovery. For financial institutions this applies as well, they want to start using
the huge volume of mostly unstructured text data to gain insight and improve KYC processes.
Text classification analysis is divided in three steps: text
preprocessing, feature extraction and text classification.
The first step, text preprocessing, is defined with sevenphases: tokenization, formatting numbers, formatting emails
and URLs, removing stopwords, removing punctuation,
stemming and removing monosyllabic words. This step isimportant due to the nature of text documents, it is very difficult to process raw text files so some clean up is needed.
The second step is to extract features from raw documents. Based on its properties (Baeza-Yates and RibeiroNeto 2008), we used Term Frequency-Inverse Document
Frequency (TF-IDF) (Manning, Raghavan, and Schütze2008) to create a vector space representation for every document associated with a customer. Luhn (1958) suggested thatboth extremely common and extremely uncommon words
were not useful for indexing.
The third and final step is the text classification. In thisstep, the chosen classification algorithm is Multinomial
Naive Bayes (MNB) because it is a canonical text classification algorithm usually used as a baseline. This algorithm
based on the Bayes theorem performs better than multivariate Bernoulli, proving an average of 27% reduction inerror (McCallum and Nigam 1998). This supervised learning algorithm has the “naive” assumption that every pair offeatures are independent. In real-world applications this assumption usually does not hold, but empirical experiments
have shown a competitive performance with state-of-theart supervised algorithms, leading in some cases to a dependence cancelation among the different features. Furthermore, Naive Bayes is inherently interpretable, contributing
to one of our main goals.

Financial transactions classification analysis(structured data)
One data type that is ubiquitous in financial institutions are
the transactions. Every customer has at least one accountwhere they have double-entry transactions defined as “debit”
or “credit”. Extracting features from financial transactions isa challenging task. We used several properties of the transactions (time stamp, transaction type, amount of money, currency, balance, etc.), which were provided by the CDD experts, obtained through literature review, and statistics, to engineer features from this time series dataset.
The chose algorithm for this classification analysis is Random Forest (Breiman 2001) because of its performance andbecause there exist very efficient explanation methods fortree ensembles, thus having the ability to explain predictions. Also, ensemble algorithms have shown better performance than a single model, that also motivates us to use Random Forest.

Co-training
In the financial services industry there is a high amount ofdata from several heterogeneous sources. While this may beseen as a drawback, empirical evidence shows that by exploring several views of the same instance, an algorithm canbe more effective and it can generalize better than singleview classifiers (Xu, Tao, and Xu 2013).

There are different methodologies for data integration (Ritchie et al. 2015): a) early integration, where there
is a simple concatenation of the several views in a single
feature space; b) intermediate integration, where the data istransformed in a common feature space before combining
them; and c) late integration, where we analyse each view
separately and the results are combined. Co-training falls in
the latter, where we have one classifier per view after whichthe predictions are combined.
The co-training algorithm (Blum and Mitchell 1998) allows us to exploit multiple views of the same instance, learn
from a small labeled dataset and leveraging unlabeled data,
all of this while providing a good classification performancewith prediction explanations.
Data: L1 , L2 , U1 , U2
Result: Trained classifiers: h1 , h2
begin
Given:• a set L1 of labeled training examples for h1• a set L2 of labeled training examples for h2• a set U1 of unlabeled examples for h1• a set U2 of unlabeled examples for h21while k 6= K do
Use L1 to train a classifier h1
Allow h1 to label 1 positive and 1 negative
examples from U1
Add these self-labeled examples to L2
Remove self-labeled examples from U1 andU2
Use L2 to train a classifier h2
Allow h2 to label 1 positive and 1 negative
examples from U2
Add these self-labeled examples to L1
Remove self-labeled examples from U1 andU2
k += 1end
end
Algorithm 1: Proposed Co-training Algorithm.

Based on the original co-training algorithm we propose
two modifications (as shown in Algorithm 1). The first is tochange the order of the training and predicting operations.
We propose to do the training and prediction steps alternately between the two classifiers, contrary to the originalalgorithm where they are simultaneous. Our second modification is to have two labeled (L1 and L2 ) and unlabeled (U1and U2 ) datasets, and treat them separately (one per view),
such that labeled instances from h1 can go only to the training instances for h2, something that is not happening in theoriginal algorithm.
The output of five experiment configurations is recorded
in Table 1, where the combined classifier is done using aprobability sum-rule because it is less sensitive to noise thanother rules (Kittler et al. 1998) and it has a high recogni-tion rate (Tulyakov et al. 2008): Single classifier (two separate classifiers with simple score combination, without iterations and without interaction between them), self-training(each classifier is being retrained in isolation using its predicted samples), co-training original (original co-training algorithm), co-training proposal 1 (described in Algorithm 1)
and co-training proposal 2 (only when both classifiers predict the same label). The next section discusses the impactof each modification by look at the change in performance.

Experimental evaluation and results
The dataset contains 693 instances, each one of them consistof two views: text and financial transactions features. Building the dataset was a challenging task: we had around 35
thousands files in Dutch language, in different formats (xls,xlsx, doc, docx, pdf, rtf, etc.) from several sources, so we designed and implemented an information retrieval system toobtain as much of the data for each customer with high precision. Due to customer data confidentiality it is not possible
to show sample vectors of features.
When using a co-training schema, the first assumption isthat we have a small initial labeled dataset. In the originalco-training implementation, 12 labeled instances initial instances where used with 30 iterations. In our case, as ourdataset is bigger, we experimented with 12, 24 and 50 initial labeled instances. Another parameter that we vary is thenumber of iterations: 10, 20, 30, 40, 50, 60, 70, 80 and 100.
We wanted to analyze the trade-off between the number of
initially labeled instances, number of iterations and algorithm performance. The average performance values of 10
runs are recorded in the results.
The dataset has a class imbalance of low/high risk ratio 1:2. The metrics to evaluate performance were accuracy(ACC) and the area under the Receiver Operating Characteristic (ROC) known as AUC.
We showed empirical evidence that our co-training proposal outperformed the other experiments, and more importantly, the insight provided by both text and financial transaction features was relevant and useful for domain experts.
The latter was confirmed by a CDD expert through an interview and two case studies, where we showed new patterns
that can be used in future CDD reporting cases.
For most cases, as the number of initially labeled instances increases, the classification performance increases,
but we decided to use 24 initially labeled instances and 30
iterations as this experiment showed more steady results andhigher insight value to domain experts. We can see that ourco-training proposals perform best in the set of experiments
shown in Table 1. Also, the co-training proposal 1 had thestandard deviation of the accuracy and AUC scores.
Another useful observation was the correlation between
some text and financial transaction features. This is important to domain experts because sometimes there is no text
available to start a CDD report. For those cases where thetext data is missing, with this moderate correlation (>0.3
Pearson’s correlation coefficient) we may be able indicate
some initial text inquiries based on just the financial transactions’ classification analysis, however further research needs
to be done to confirm the consistency of these results.

Configuration
Singleclassifier
Selftraining
Co-training
original
Co-training
proposal 1
Co-training
proposal 2

Textclassifier
ACC
AUC

Transactionsclassifier
ACC
AUC

Combinedclassifier
ACC
AUC

0.80880.78160.5104

0.49660.80930.78230.66840.61040.64400.61340.66060.60170.64820.60070.62540.61470.6456

0.59860.81430.81720.65630.64390.81010.81290.74120.71950.66640.60840.74450.7257

Table 1: Accuracy (ACC) and AUC scores for experimentswith 24 initial training instances and 30 iterations.

A senior CDD expert validated the results and gave a positive feedback: ”the tool provided new relevant insight fortarget cases while having an accurate prediction”. The domain experts confirmed that this tool has the potential to
make their day to day work more efficient and effective.
Additionally, we carried out an experiment relying on
early data integration with just one classifier (Random Forest). It was surprising that a mere concatenation of text andfinancial transactions features achieved very good performance. However, the drawback is in the loss of interpretability, the model did not provide insights for the domain experts. With this experiment we were able to see the trade-off
between accuracy and interpretability, where the latter is of
crucial importance for our application.

Conclusions
The analysis showed empirical evidence of the benefits ofco-training for a risk classification in the context of CDD reporting. We showed that a multi-view learning scenario (textand financial transactions data) with a co-training schema
outperforms a single-view learning scenario. We propose
two modifications to the original co-training scheme, andshowed that each modification improves the performance(accuracy and AUC scores) even further. Furthermore, theinherently interpretable nature of the models for each view
allowed domain experts to extract valuable explanations.
This framework allowed us to augment the capabilitiesof domain experts and make them part of the knowledge
discovery loop by adding different sources to the machinelearning loop. Our method managed to provide new insights,
which can be used to speed up the process of CDD reporting,allowing the domain expert to focus on high risk customersmore effectively.
Most importantly overall, it showed how a financial institution can leverage a small set of labeled heterogeneous
customer data in a knowledge discovery process to assesscustomer risk and combat financial crimes. This will definitely improve the KYC processes and will help protecting
the financial system from illegal activities.

References
Baeza-Yates, R., and Ribeiro-Neto, B. 2008. Modern Information Retrieval: The Concepts and Technology Behind
Search. USA: Addison-Wesley Publishing Company, 2ndedition.
Blum, A., and Mitchell, T. 1998. Combining labeledand unlabeled data with co-training. In Proceedings of theEleventh Annual Conference on Computational Learning
Theory, COLT’ 98, 92–100. New York, NY, USA: ACM.
Breiman, L. 2001. Random forests. Machine Learning45(1):5–32.
Kittler, J.; Hatef, M.; Duin, R. P. W.; and Matas, J. 1998. On
combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(3):226–239.
Li, S.; Li, Y.; and Fu, Y. 2016. Multi-view time series classification: A discriminative bilinear projection approach. In
Proceedings of the 25th ACM International on Conferenceon Information and Knowledge Management, CIKM ’16,989–998. New York, NY, USA: ACM.
Manning, C. D.; Raghavan, P.; and Schütze, H. 2008. Introduction to Information Retrieval. New York, NY, USA:Cambridge University Press.
McCallum, A., and Nigam, K. 1998. A comparison of event
models for naive Bayes text classification. In Learning for
Text Categorization: Papers from the 1998 AAAI Workshop,41–48.
Miner, G.; Elder, J.; Fast, A.; Hill, T.; Nisbet, R.; and Delen,
D. 2012. Practical Text Mining and Statistical Analysis for
Non-structured Text Data Applications. Elsevier Science.
Molnar, C. 2018. Interpretable Machine Learning. A Guidefor Making Black Box Models Explainable. USA: Bookdown.
PricewaterhouseCoopers.
2014.
Know your customer: Quick reference guide.
https://www.pwc.com/
gx/en/financial-services/publications/assets/pwc-antimoney-laundering-know-your-customer-quick-referenceguide.pdf, Last accessed on 2018-05-28.
Ritchie, M. D.; Holzinger, E. R.; Li, R.; Pendergrass, S. A.;
and Kim, D. 2015. Methods of integrating data to uncovergenotype–phenotype interactions. Nature Reviews Genetics
16:85–97.
Tulyakov, S.; Jaeger, S.; Govindaraju, V.; and Doermann,D. S. 2008. Review of classifier combination methods. In
Machine Learning in Document Analysis and Recognition.
Xu, C.; Tao, D.; and Xu, C. 2013. A survey on multi-viewlearning. CoRR abs/1304.5634.