Advances in Credit Risk Assessment Using Bank Statement Analytics and Automated Scoring Models

• Author(s): Oluwasola Emmanuel Adesemoye ; Ezinne C. Chukwuma-Eke ; Comfort Iyabode Lawal ; Ngozi Joan Isibor ; Abiola Oyeronke Akintobi; Florence Sophia Ezeh
• Paper ID: 1708077
• Page: 234-252
• Published Date: 31-03-2021
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 4 Issue 9 March-2021

Abstract

Advancements in credit risk assessment are transforming the financial industry, with bank statement analytics and automated scoring models playing pivotal roles in enhancing the accuracy and efficiency of evaluating borrower risk. Traditionally, credit risk assessment relied on static credit scores and financial statements, which often provided limited insights into a borrower’s financial behavior. However, the use of bank statement analytics allows for a more granular analysis of a borrower’s income, spending habits, cash flow, and financial stability. This data-driven approach provides a comprehensive picture of a borrower’s financial health, enabling lenders to make more informed decisions. Automated scoring models further enhance this process by incorporating machine learning algorithms, such as decision trees, neural networks, and support vector machines, to analyze large volumes of financial data in real-time. These models integrate bank statement analytics with alternative data sources, enabling the prediction of creditworthiness with greater accuracy and speed. By automating the scoring process, financial institutions can assess risk more quickly, reduce operational costs, and scale their lending operations. Despite these advancements, challenges remain in data privacy, security, and ethical considerations, especially when incorporating alternative data sources. Additionally, automated scoring models require careful oversight to avoid issues such as model bias and lack of transparency. Future developments in technology, such as real-time data processing, AI-driven risk assessments, and the integration of blockchain for enhanced security, promise to further revolutionize credit risk assessment. This explores the impact of these innovations on the financial sector, highlighting their potential to improve decision-making, enhance credit access, and provide more accurate risk predictions in a rapidly evolving financial landscape.

Keywords

Credit risk assessment, Bank statement, Analytics, Automated scoring models

Citations

IRE Journals:
Oluwasola Emmanuel Adesemoye , Ezinne C. Chukwuma-Eke , Comfort Iyabode Lawal , Ngozi Joan Isibor , Abiola Oyeronke Akintobi; Florence Sophia Ezeh "Advances in Credit Risk Assessment Using Bank Statement Analytics and Automated Scoring Models" Iconic Research And Engineering Journals Volume 4 Issue 9 2021 Page 234-252

IEEE:
Oluwasola Emmanuel Adesemoye , Ezinne C. Chukwuma-Eke , Comfort Iyabode Lawal , Ngozi Joan Isibor , Abiola Oyeronke Akintobi; Florence Sophia Ezeh "Advances in Credit Risk Assessment Using Bank Statement Analytics and Automated Scoring Models" Iconic Research And Engineering Journals, 4(9)