Regularization of Autoencoders for Bank Client Profiling Based on Financial Transactions
Predicting if a client is worth giving a loan—credit scoring—is one of the most essential and popular problems in banking. Predictive models for this goal are built on the assumption that there is a dependency between the client’s profile before the loan approval and their future behavior. However,...
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doaj-1a575d90d6064326a0151a393d471bd92021-03-18T00:06:52ZengMDPI AGRisks2227-90912021-03-019545410.3390/risks9030054Regularization of Autoencoders for Bank Client Profiling Based on Financial TransactionsAndrey Filchenkov0Natalia Khanzhina1Arina Tsai2Ivan Smetannikov3Machine Learning Lab, ITMO University, 49 Kronverksky Pr., St. Petersburg 197101, RussiaMachine Learning Lab, ITMO University, 49 Kronverksky Pr., St. Petersburg 197101, RussiaComputer Technologies Department, Formerly ITMO University, 49 Kronverksky Pr., St. Petersburg 197101, RussiaMachine Learning Lab, ITMO University, 49 Kronverksky Pr., St. Petersburg 197101, RussiaPredicting if a client is worth giving a loan—credit scoring—is one of the most essential and popular problems in banking. Predictive models for this goal are built on the assumption that there is a dependency between the client’s profile before the loan approval and their future behavior. However, circumstances that cause changes in the client’s behavior may not depend on their will and cannot be predicted by their profile. Such clients may be considered “noisy” as their eventual belonging to the defaulters class results rather from random factors than from some predictable rules. Excluding such clients from the dataset may be helpful in building more accurate predictive models. In this paper, we report on primary results on testing the hypothesis that a client can become a <i>defaulter</i> in two scenarios: intentionally and unintentionally. We verify our hypothesis applying data driven regularized classification using an autoencoder to client profiles. To model an intention as a hidden variable, we propose an especially designed regularizer for the autoencoder. The regularizer aims to obtain a representation of defaulters that includes a cluster of <i>intentional defaulters</i> and <i>unintentional defaulters</i> as outliers. The outliers were detected by our model and excluded from the dataset. This improved the credit scoring model and confirmed our hypothesis.https://www.mdpi.com/2227-9091/9/3/54clusteringautoencoderregularizationneural networksmachine learningcredit scoring |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Andrey Filchenkov Natalia Khanzhina Arina Tsai Ivan Smetannikov |
spellingShingle |
Andrey Filchenkov Natalia Khanzhina Arina Tsai Ivan Smetannikov Regularization of Autoencoders for Bank Client Profiling Based on Financial Transactions Risks clustering autoencoder regularization neural networks machine learning credit scoring |
author_facet |
Andrey Filchenkov Natalia Khanzhina Arina Tsai Ivan Smetannikov |
author_sort |
Andrey Filchenkov |
title |
Regularization of Autoencoders for Bank Client Profiling Based on Financial Transactions |
title_short |
Regularization of Autoencoders for Bank Client Profiling Based on Financial Transactions |
title_full |
Regularization of Autoencoders for Bank Client Profiling Based on Financial Transactions |
title_fullStr |
Regularization of Autoencoders for Bank Client Profiling Based on Financial Transactions |
title_full_unstemmed |
Regularization of Autoencoders for Bank Client Profiling Based on Financial Transactions |
title_sort |
regularization of autoencoders for bank client profiling based on financial transactions |
publisher |
MDPI AG |
series |
Risks |
issn |
2227-9091 |
publishDate |
2021-03-01 |
description |
Predicting if a client is worth giving a loan—credit scoring—is one of the most essential and popular problems in banking. Predictive models for this goal are built on the assumption that there is a dependency between the client’s profile before the loan approval and their future behavior. However, circumstances that cause changes in the client’s behavior may not depend on their will and cannot be predicted by their profile. Such clients may be considered “noisy” as their eventual belonging to the defaulters class results rather from random factors than from some predictable rules. Excluding such clients from the dataset may be helpful in building more accurate predictive models. In this paper, we report on primary results on testing the hypothesis that a client can become a <i>defaulter</i> in two scenarios: intentionally and unintentionally. We verify our hypothesis applying data driven regularized classification using an autoencoder to client profiles. To model an intention as a hidden variable, we propose an especially designed regularizer for the autoencoder. The regularizer aims to obtain a representation of defaulters that includes a cluster of <i>intentional defaulters</i> and <i>unintentional defaulters</i> as outliers. The outliers were detected by our model and excluded from the dataset. This improved the credit scoring model and confirmed our hypothesis. |
topic |
clustering autoencoder regularization neural networks machine learning credit scoring |
url |
https://www.mdpi.com/2227-9091/9/3/54 |
work_keys_str_mv |
AT andreyfilchenkov regularizationofautoencodersforbankclientprofilingbasedonfinancialtransactions AT nataliakhanzhina regularizationofautoencodersforbankclientprofilingbasedonfinancialtransactions AT arinatsai regularizationofautoencodersforbankclientprofilingbasedonfinancialtransactions AT ivansmetannikov regularizationofautoencodersforbankclientprofilingbasedonfinancialtransactions |
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