Risk Management Techniques in Banking Sector Using Data Mining

Abstract

There is a growing power of data mining in business applications, with several solutions already applied and many more being discovered. Since the worldwide financial crisis, risk management in banks has increased more distinction, and there has been a continuous attention around how risks are actuality identified, measured, reported and managed. Significant research in academia and industry has concentrated on the expansions in banking and risk management and the current and evolving challenges. This paper, through an analysis of the accessible literature seeks to analyses and evaluates data mining techniques that have been researched in the environment of banking risk management, and to classify regions or difficulties in risk management that have been incompetently explored and are possible areas for further research. The review has exposed that the application of data mining in the managing of banking risks such as market risk, credit risk, ,liquidity risk and operational risk has been discovered; however, it doesn’t appear proportionate with the present industry level of focus on both risk management and data mining. A huge number of regions persist in bank risk management that could pointedly advantage from the study of how data mining can be functional to address exact problems