Main Article Content
Introduction:Increase in computing power and the deeper usage of the robust computing systems in the financial system is propelling the business growth, improving the operational efficiency of the financial institutions, and increasing the effectiveness of the transaction processing solutions used by the organizations.
Problem:Despite that the financial institutions are relying on the credit scoring patterns for analyzing the credit worthiness of the clients, still there are many factors that are imminent for improvement in the credit score evaluation patterns.
Objective:Machine learning is offering immense potential in Fintech space and determining a personal credit score. Organizations by applying deep learning and machine learning techniques can tap individuals who are not being serviced by traditional financial institutions.
Methodology:One of the major insights into the system is that the traditional models of banking intelligence solutions are predominantly the programmed models that can align with the information and banking systems that are used by the banks. But in the case of the machine-learning models that rely on algorithmic systems require more integral computation which is intrinsic.
Results:The test analysis of the proposed machine learning model indicates effective and enhanced analysis process compared to the non-machine learning solutions. The model in terms of using various classifiers indicate potential ways in which the solution can be significant.
Conclusion: If the systems can be developed to align with more pragmatic terms for analysis, it can help in improving the process conditions of customer profile analysis, wherein the process models have to be developed for comprehensive analysis and the ones that can make a sustainable solution for the credit system management.
Originality:The proposed solution is effective and the one conceptualized to improve the credit scoring system patterns.
Limitations: The model is tested in isolation and not in comparison to any of the existing credit scoring patterns.
As the author of the article, I declare that is an original unpublished work exclusively created by me, that it has not been submitted for simultaneous evaluation by another publication and that there is no impediment of any kind for concession of the rights provided for in this contract.
In this sense, I am committed to await the result of the evaluation by the journal Ingeniería Solidaría before considering its submission to another medium; in case the response by that publication is positive, additionally, I am committed to respond for any action involving claims, plagiarism or any other kind of claim that could be made by third parties.
At the same time, as the author or co-author, I declare that I am completely in agreement with the conditions presented in this work and that I cede all patrimonial rights, in other words, regarding reproduction, public communication, distribution, dissemination, transformation, making it available and all forms of exploitation of the work using any medium or procedure, during the term of the legal protection of the work and in every country in the world, to the Universidad Cooperativa de Colombia Press.
 W. Chen, G. Xiang, Y. Liu, and K. Wang, “Credit risk Evaluation by hybrid data mining technique,” Syst. Eng. Procedia, vol. 3, pp. 194–200, 2012.
 S. Moradi and F. Mokhatab Rafiei, “A dynamic credit risk assessment model with data mining techniques: evidence from Iranian banks,” Financ. Innov., vol. 5, no. 1, Dec. 2019.
 S. Akkoç, “An empirical comparison of conventional techniques, neural networks and the three-stage hybrid Adaptive Neuro-Fuzzy Inference System (ANFIS) model for credit scoring analysis: The case of Turkish credit card data,” Eur. J. Oper. Res., vol. 222, no. 1, pp. 168–178, Oct. 2012.
 R. P. Bunker, M. A. Naeem, and W. Zhang, Improving a Credit Scoring Model by Incorporating Bank Statement Derived Features.
 M. B. Waad, “On Feature Selection Methods for Credit Scoring,” no. January 2015.
 P. Abdou, H. A. Abdou, J. Pointon, and H. Abdou, “Credit scoring, statistical techniques and evaluation criteria: A review of the literature Title Credit scoring, statistical techniques and evaluation criteria: A review of the literature Credit Scoring, Statistical Techniques and Evaluation Criteria: A Review of the Literature,” Financ. Manag., vol. 18, no. 3, pp. 59–88, 2011.
 M. Schumann Yang Liu, P. Matthias Schumann, and D. Werk, “The evaluation of classification models for credit scoring Institut für Wirtschaftsinformatik.”
 R. E. Turkson, E. Y. Baagyere, and G. E. Wenya, “A machine learning approach for predicting bank credit worthiness,” in 2016 3rd International Conference on Artificial Intelligence and Pattern Recognition, AIPR, pp. 81–87.
 K. Tran, T. Duong, and Q. Ho, “Credit scoring model: A combination of genetic programming and deep learning,” in FTC 2016 - Proceedings of Future Technologies Conference, 2017, pp. 145–149.
 X. Zhang, Y. Yang, and Z. Zhou, “A novel credit scoring model based on optimized random forest,” in 2018 IEEE 8th Annual Computing and Communication Workshop and Conference, CCWC, vol. 2018-January, pp. 60–65.
 Su-Lin Pang, Yan-Ming Wang, and Yuan-Huai Bai, “Credit scoring model based on neural network,” in Proceedings. International Conference on Machine Learning and Cybernetics, vol. 4, pp. 1742–1746.
 C. Wang, D. Han, Q. Liu, and S. Luo, “A Deep Learning Approach for Credit Scoring of Peer-to-Peer Lending Using Attention Mechanism LSTM,” IEEE Access, vol. 7, pp. 2161–2168, 2019.
 X. Zheng, “A credit scoring model based on collaborative filtering,” in Proceedings - 9th International Conference on Computational Intelligence and Security, CIS 2013, pp. 144–148.
 G. Arutjothi and C. Senthamarai, “Prediction of loan status in commercial bank using machine learning classifier,” in Proceedings of the International Conference on Intelligent Sustainable Systems, ICISS 2017, 2018, pp. 416–419.
 P. Yao, “Credit scoring using ensemble machine learning,” in Proceedings - 2009 9th International Conference on Hybrid Intelligent Systems, HIS 2009, vol. 3, pp. 244–246.
 S. Birla, K. Kohli, and A. Dutta, “Machine Learning on imbalanced data in Credit Risk,” in 7th IEEE Annual Information Technology, Electronics and Mobile Communication Conference, IEEE IEMCON, 2016.
 R. Emekter, Y. Tu, B. Jirasakuldech, and M. Lu, “Evaluating credit risk and loan performance in online Peer-to-Peer (P2P) lending,” Appl. Econ., vol. 47, no. 1, pp. 54–70, Jan. 2015.
 H. Ince and B. Aktan, “A comparison of data mining techniques for credit scoring in banking: A managerial perspective,” J. Bus. Econ. Manag., vol. 10, no. 3, pp. 233–240, 2009.
 J. N. Crook, D. E. Edelman, and L. C. Thomas, “Credit Scoring,” J. Oper. Res. Soc., vol. 56, no. 9, pp. 1003–1005, Sep. 2005.
 Y. Liu and M. Schumann, “Data mining feature selection for credit scoring models,” J. Oper. Res. Soc., vol. 56, no. 9, pp. 1099–1108, Sep. 2005.
 B. Baesens, T. Van Gestel, S. Viaene, M. Stepanova, J. Suykens, and J. Vanthienen, “Benchmarking state-of-the-art classification algorithms for credit scoring,” J. Oper. Res. Soc., vol. 54, no. 6, pp. 627–635, Jun. 2003.
 F. Reserve Board, Board of Governors of the Federal Reserve System Report to the Congress on Credit Scoring and Its Effects on the Availability and Affordability of Credit.
 “FICO Credit Score Algorithm Group 14.”
 S. Sayad, Comparing Different Classification Techniques in Credit Scoring.
 S. Bhatia, P. Sharma, R. Burman, S. Hazari, and R. Hande, Credit Scoring using Machine Learning Techniques, 2017.
 Y. Hou, X. Ma, G. Mei, N. Wang, and W. Xu, “A Trial of Student Self-Sponsored Peer-to-Peer Lending Based on Credit Evaluation Using Big Data Analysis,” Comput. Intell. Neurosci., vol. 2019, 2019.
J. S. R. Jang, “ANFIS: Adaptive-Network-Based Fuzzy Inference System,” IEEE Trans. Syst. Man Cybern., vol. 23, no. 3, pp. 665–685, 1993.
 M. Bensic, N. Sarlija, and M. Zekic-Susac, “Modelling small-business credit scoring by using logistic regression, neural networks and decision trees,” Intell. Syst. Accounting, Financ. Manag., vol. 13, no. 3, pp. 133–150, Jul. 2005.
 M. Pagano and T. Jappelli, “Information Sharing in Credit Markets,” J. Finance, vol. 48, no. 5, pp. 1693–1718, 1993.
 B. Twala, “Multiple classifier application to credit risk assessment,” Expert Syst. Appl., vol. 37, no. 4, pp. 3326–3336, Apr. 2010.