Default avoidance on credit card portfolios using accounting, demographical and exploratory factors: decision making bas
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Default avoidance on credit card portfolios using accounting, demographical and exploratory factors: decision making based on machine learning (ML) techniques Nikolaos Sariannidis1 · Stelios Papadakis2 · Alexandros Garefalakis2 · Christos Lemonakis2 · Tsioptsia Kyriaki‑Argyro3
© Springer Science+Business Media, LLC, part of Springer Nature 2019
Abstract Effective and thorough credit-risk management is a key factor for lending institutions, as significant financial losses can arise from the borrowers’ default. Consequently, machine learning methods can measure and analyze credit risk objectively when at the same time they face increasingly attention. This study analyzes default payment data from a credit cards’ portfolio containing some 30,000 clients from Taiwan with twenty-three attributes and with no missing information. We compare prediction accuracy of seven classification methods used, i.e. KNN, Logistic Regression, Naïve Bayes, Decision Trees, Random Forest, SVC, and Linear SVC. The results indicate that only few out of most of the typical variables used can adequately analyze default characteristics in terms of lending decisions. The results provide effective feedback to credit evaluators, lending institutions and business analysts for in-depth analysis. Also, they mention to the importance of the precautionary borrowing techniques to be used to better understand credit-card borrowers’ behavior, along with specific accounting, historical and demographical characteristics. Keywords Debt · Credit card portfolios · Machine learning (ML) methods · Explanatory factors · Accounting data · Demographic data · Credit history data
1 Introduction Classification methods allow one to predict the category to which a state of financial condition belongs based on key characteristics. The prediction of credit default is a typical example of applying machine learning techniques. The credit card markets, in recent years, have been growing globally. However, it is a fact that credit card is a high-risk product, which is why credit card interest rates are kept at high levels. It is therefore expected that, as financial institutions seek to maximize their profits, they also strive to limit their bad debts of insolvent customers. At the heart of the banking institutions, their principal
* Nikolaos Sariannidis [email protected] Extended author information available on the last page of the article
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Vol.:(0123456789)
Annals of Operations Research
concerns—among others—are to adequately manage of the increased credit risk, involves in credit card payments and their use in general. The high risk of credit card development has highlighted weaknesses identified worldwide in assessing credit quality for potential customers. This study discusses data processing related to the multivariate classification dataset provided in the UCI Machine Learning Repository. The data contains of some 30,000 clients with twenty-three attributes with no missing information. We look for credit card defaults in all cases with the use of account
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