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.

This paper proposes to evaluate the adaptability risk in money laundering using Bitmap Index-based Decision Tree (BIDT) technique. Initially, the Bitmap Index-based Decision Tree learning is used to induce the knowledge tree which helps to determine a company's money laundering risk and improve scalability. A bitmap index in BIDT is used to effectively access large banking databases. In a BIDT bitmap index, account in a table is numbered in sequence with each key value, account number and a bitmap (array of bytes) used instead of a list of row ids. Subsequently, BIDT algorithm uses the ''select" query performance to apply count and bit-wise logical operations on AND. Query result coincides exactly to build a decision tree and more precisely to evaluate the adaptability risk in the money laundering operation. For the root node, the main account of the decision tree, the population frequencies are obtained by simply counting the total number of ''1" in the bitmaps constructed on the attribute to predict money laundering and evaluate the risk factor rate. The experiment is conducted on factors such as regulatory risk rate, false positive rate, and risk identification time. Ó 2016 University of Bahrain. Publishing services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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