A Review on Data Mining in Banking Sector

Abstract

The banking industry has undergone various changes in the way they conduct the business and focus on modern technologies to compete the market. The banking industry has started realizing the importance of creating the knowledge base and its utilization for the benefits of the bank in the area of strategic planning to survive in the competitive market. In the modern era, the technologies are advanced and it facilitates to generate, capture and store data are increased enormously. Data is the most valuable asset, especially in financial industries. The value of this asset can be evaluated only if the organization can extract the valuable knowledge hidden in raw data. The increase in the huge volume of data as a part of day to day operations and through other internal and external sources, forces information technology industries to use technologies like data mining to transform knowledge from data. Data mining technology provides the facility to access the right information at the right time from huge volumes of raw data. Banking industries adopt the data mining technologies in various areas especially in customer segmentation and profitability, Predictions on Prices/Values of different investment products, money market business, fraudulent transaction detections, risk predictions, default prediction on pricing. It is a valuable tool which identifies potentially useful information from large amount of data, from which organization can gain a clear advantage over its competitors. This study shows the significance of data mining technologies and its advantages in the banking and financial sectors.

Digitalisation and Big Data Mining in Banking

“…Following a comprehensive investigation of existing literature, to the best of our knowledge, only two review papers focused on the DM applications in banking [7,8] and both covered a number of DM implementations before 2013. For such a rapidly developing subject that progresses on a daily basis, it is important to provide researchers and interested parties with the most up to date status of DM and banking collaborations.…”

“…As such, we thoroughly review the DM applications in banking, especially for the recent years, post 2013. It is noteworthy that we will not repeat the contents covered in [7,8], but instead focus on the most recently developed DM applications in the banking sector. This paper aims to serve as the most up to date one stop directory guide for relevant researchers and apprise them of the evolution of big data analytics in banking with an outlook for future research.…”

Money laundering regulatory risk evaluation using Bitmap Index-based Decision Tree

“…The main drawbacks are due to the fact that there appears no identical tactics with financial institutions to deal with the anti-money laundering. Digital forensics and database analysis were integrated (Flores et al, 2011) to verify the customer in an extensive manner and detect fraudulent activities (Jayasree and Siva Balan, 2013). However, still it has challenged the evaluation of the digital forensic practices within the organization.…”

On the Improvement of Default Forecast Through Textual Analysis

“…Credit scoring models are fundamental for banks to guarantee a correct forecast of default risk for financed loans, which translates into a reduction in losses and an increase in profits. There are numerous techniques for this purpose (Efron, 1979 ; Baesens et al, 2003 ; Jayasree and Balan, 2013 ; Emekter et al, 2015 ). Although nowadays most of the models in question use quantitative information, typically financial data, the latter is no longer sufficient to properly profile customers in a world that is now increasingly digital.…”