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Scaling credit decisions in FinTech : overcoming boundaries through behavioural credit risk models
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Resumo(s)
The decision whom to grant a credit is of utmost importance for financial institutions
in order to develop both financially profitable, as well as widely accessible
financial products. To do this, companies have to be able to distinguish credit
applicants, who are able and likely to pay back their loan, from those, who will
be unable or unwilling to do so in the future. To improve this decision in the
future, the integration of additional behavioural data into the credit decision is
proposed in this thesis.
FinTech firms are increasingly moving interactions between financial institutions
and their customers from local bank branches into digital environments. This
transformation enables companies to gather and analyze a large set of previously
unavailable behavioural indicators, which can help estimate an individuals credit
default risk. This study presents the transforming market conditions that FinTech
firms operate in from a regulatory, technical and behavioural perspective and
outlines the key changes that impact the offering of credit products. Additionally,
it presents the leading approaches of consumer credit research and leverages their
best practices in the creation of a behavioural risk scoring model for a FinTech
company.
The evaluation of the model shows that the inclusion of behavioural indicators
into the credit decision is able to significantly improve the performance of decision
tree based credit risk models. Models trained with additional behavioural data are
able to outperform the base variable set in all performed tests, when compared
using the AUC and Kolmogorov-Smirnov measures, while showing no change
when assessed using the Brier-Score.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
Palavras-chave
Behavioural Data Credit Scoring FinTech Finance Risk Modelling Binary Classification Random Forests Gradient Boosting Logistic Regression Feature Engineering