Data Mining for Risk Assessment in Banking and Finance: A Review

Abstract

A powerful analytical tool, data mining has become a common method utilized by financial institutions to identify and manage risks. The paper aims to provide a comprehensive analysis of the various techniques used in the mining of data for financial and banking organizations. It also explores the applications of these techniques in the risk assessment process. Risk assessment is a vital part of the operations of financial institutions. It involves identifying, measuring, and mitigating the risks that can affect an institution's financial health. The paper covers the various types of data mining tools that are commonly utilized for assessing financial and banking risks, such as clustering, association rule mining, and classification. It also provides a review of the limitations and challenges associated with using such techniques. The paper also reviews the literature on the various applications of data mining in the financial and banking sectors. These include operational risk assessment, credit risk assessment, and fraud detection. The review provides an overview of each application's aspects, such as data sources, preprocessing techniques, algorithms, and the results. The paper then explores the future directions for the research on the use of data mining for the assessment of financial and banking risks. It covers the latest trends in the field of data analysis, such as the incorporation of artificial intelligence, machine learning, big data analytics, and more