Review of Machine Learning models for Credit Scoring Analysis - Rudra Kumar, Madapuri; Kumar Gunjan, Vinit (Ingeniería Solidaria, 2020)
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. Only the inputs in terms of shortcomings from the existing models are taken in to account and accordingly the proposed solution is developed.
Keywords: creditworthiness evaluation; credit score evaluation; machine learning for a credit score; solutions for credit score molels; information and communication technologies