Multi-criteria mathematical model of credit scoring in data science problems

Authors

DOI:

https://doi.org/10.20535/SRIT.2308-8893.2025.3.08

Keywords:

Data Science, Big Data, SCORIG machine learning, decision making, multi-criteria mathematical models, intelligent CRM, ERP systems

Abstract

A multi-criteria optimization mathematical model of credit scoring is proposed. The model is derived using a nonlinear trade-off scheme to solve multi-criteria optimization problems, allowing for the construction of a Pareto-optimal solution. The proposed approach forms an integrated assessment of a borrower’s creditworthiness based on a structured set of indicators that reflect the financial, credit, and social profile of clients. The model is designed for use in intelligent CRM and ERP systems operating on Big Data and does not rely on labeled training samples, making it applicable to unsupervised learning tasks. It can also serve as a foundational layer for further deep-learning analysis. Methodological steps for implementing the model, from indicator normalization to final decision-making, are described. A technological implementation demonstrates the model’s effectiveness in automated loan decisions and fraud detection.

Author Biographies

Oleksii Pysarchuk, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv

Doctor of Technical Sciences, a professor at the Department of Computer Engineering of the Faculty of Informatics and Computer Engineering of the National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv, Ukraine.

Maria Vasylieva, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv

Senior lecturer at the Department of Computer Engineering of the Faculty of Informatics and Computer Engineering of the National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv, Ukraine.

Danylo Baran, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv

Ph.D. student at the Department of Computer Engineering of the Faculty of Informatics and Computer Engineering of the National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv, Ukraine.

Illya Pysarchuk, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv

Graduate student at the Department of Computer Engineering of the Faculty of Informatics and Computer Engineering of the National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv, Ukraine.

References

Mohamed Hani AbdElHamid, Mohamed Tawfik ElMasry, Machine learning approach for credit score analysis: a case study of predicting mortgage loan defaults. NOVA Information Management School Instituto Superior de Estatística e Gestão de Informação Universidade Nova de Lisboa, 2019, 60p. Available: https://run.unl.pt/bitstream/10362/62427/4/TEGI0439.pdf

Maria Fernandez Vidal, Fernando Barbon, Credit scoring in financial inclusion. How to use advanced analytics to build credit-scoring models that increase access. Washington: Consultative Group to Assist the Poor: World Bank, 2019, 52 p. Available: https://www.findevgateway.org/guide-toolkit/2019/07/credit-scoring-financial-inclusion

“Credit scoring approaches guidelines,” The World Bank Group, 2019, 64 p. Available: https://thedocs.worldbank.org/en/doc/935891585869698451-0130022020/original/CREDITSCORINGAPPROACHESGUIDELINESFINALWEB.pdf

Rory P. Bunker, M. Asif Naeem, Wenjun Zhang, “Improving a Credit Scoring Model by Incorporating Bank Statement Derived Features,” Preprint submitted to Expert Systems with Applications, 2017. Available: https://www.researchgate.net/publication/309606871_Improving_a_Credit_Scoring_Model_by_Incorporating_Bank_Statement_Derived_Features

Engku Muhammad Nazri E.A. Bakar, “Credit scoring models: techniques and issues,” Journal of Advanced Research in Business and Management Studies, vol. 7, issue 2, pp. 29–41, 2017. Available: https://www.researchgate.net/publication/323306120_Credit_scoring_models_techniques_and_issues

Gerrit Muller, System Modeling and Analysis: a Practical Approach. University of South-Eastern Norway-NISE, 2023, 128 p. Available: https://www.gaudisite.nl/SystemModelingAndAnalysisBook.pdf

Nolberto Munier, Mathematical Modelling of Decision Problems. Using the SIMUS Method for Complex Scenarios. Springer, 2021, 196 p.

Albert Voronin, Multi-Criteria Decision Making for the Management of Complex Systems. IGI Global, 2017, 220 p.

Goran Cirovic, Dragan Pamucar, Multiple-Criteria Decision Making. MDPI, 2022, 313 p.

Downloads

Published

2025-09-29

Issue

No. 3 (2025)

Section

Mathematical methods, models, problems and technologies for complex systems research

License

This is an open access journal which means that all content is freely available without charge to the user or his/her institution. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles in this journal without asking prior permission from the publisher or the author. This is in accordance with the BOAI definition of open access.