Application of Data mining in the banking sector

Application of Data mining in the banking sector

Introduction

Financial fraud is a growing concern with significant consequences within company organizations, finance trade, and governments. In the modern world, banks have been highly dependent on web technology, which has magnified financial and banking transactions. Nonetheless, fraud in the financial and banking sector has also increased with an increase in offline and online transactions. Since transactions have become more popular as a payment mode, there has been a growing focus on how to prevent fraud and its effect I the banking and financial sector. Data mining has led to establishments of various techniques that banks and financial organizations can use to hinder fraud. Data mining has offered different logarithms and techniques that aid in focusing on essential data patterns from banks’ databases hence hindering fraud activities.

Application of Data mining in the banking sector

The banking and financial industry has faced various changes in the manner of conducting business. The implementation of electronic banking has made it easy to capture transactional data simultaneously (Herawan and Deris, 2011). Data mining offers aid through contributing to providing solutions to business issues through finding associations, findings, and correlations hidden in information stored in databases. There are various examples of how the banking sector is utilizing data mining.

Fraud Detection

The ability to detect fraud activities is of great concern for various businesses, but data mining has made it easy for the banking sector to identify any fraud patterns. There are two significant approaches assumed by the financial industry to aid in detecting fraud. The first step entails tapping of warehouse data of third parties and utilizing data mining techniques to point out fraud patterns. Financial institutions /could conduct a cross-reference on the trends with their databases to find out internal malicious activities. The second approach is the identification of fraud patterns, which is strictly based on institutions’ internal information. The majority of financial institutions utilize a hybrid approach.

Data Mining Techniques

Classification

Classification refers to the most popular and widely used technique. It employs various pre-classified examples to create a model that could classify the record’s population. Credit risk and fraud detection are uniquely well suited to this form of analysis.

Clustering

Clustering could be referred to as the identification of the same classes of objects. In this technique, any transactions with similar patterns of behavior tend to be combined into a single group. Clustering could be utilized as a preprocessing approach to attribute classification and selection of subsets.

Association Rule

The main task of the association rule is finding sets of binary variables occurring together in a usual manner in transaction databases (Herawan and Deris, 2011). Thus technique entails various algorithms like CDA, APRIORI, and DDA. These rules are statements that aid in uncovering an association between data that is separate relational databases.

Prediction

This is a technique that uncovers relationships between independent and dependent variables. Illustratively, the prediction analysis method could be used implemented in financial institutions to predict any fraudulent activities (Herawan and Deris, 2011). To be more productive, this technique tends to integrate regression techniques to understand these relationships. This technique includes nonlinear regression and linear regression.

Sequential patterns

This technique aims at discovering the same patterns in data transactions over a specific business period. The patterns that are uncovered are utilized in business analysis to point out data relationships.

Literature Review

According to Ravisankar et al. (2011), Fraud detection refers to the recognition of fraud symptoms where no prior tendency or suspicion of fraud exists. Hu and Liao (2011) fraud detection is the process of detecting criminal activities in financial organizations like banks, organizations issuing credit cards, insurance agencies, stock markets, and mobile companies. Malicious users could be real customers of an organization or could be fake customers positing to be false. According to Bhattacharya et al. (2011), data mining enables the bank to prevent fraud activities by eradicating unnecessary grouping. The authors argue that in case of fraud is detected, banks can use data mining to make comparisons of databases in which fraud has been detected through the use of clustered algorithms. The clusters eradicate in deviation detection algorithms to hinder future exceptions and errors. Additionally, Abdullah and Titus (2010) show that the data mining algorithm could also curb fraud activities by limiting access. Clustering of groups of employees authorized in bank hinders every employ from getting access to the database (Herawan and Deris, 2011).

Conclusion

Data mining is a vital tool in the prevention of fraud as the detection of fraud in financial institutions. Operations executed in data mining are utilized to offer security to databases and also enhance the power to make a decision. Data mining fetches vital patterns from the vast databases which aid in improving the quality of databases. Based on the reviewed data mining techniques, bankers are in apposition to point out any malicious activities taking place in the bank hence hindering fraudulent activities. Therefore, it is vivid that the creation of data mining techniques has been of great significance in thwarting fraud activities in the banking sector since it enables bankers to avoid the adverse consequences of fraudulent actions like loss of integrity, loss of customers trust and loss in profits.