Development Of Data Mining Applications In Banking Sector
Based on the review of over 100 journals the authors concluded that applying different data mining technique for managing risk, fraud detection and CRM in the banking sector has been successful though there has been exception on big data because of different variable features. The author used different techniques and software to achieve to get an insight to development of data mining for banking in future, there was special emphasis on the obstacle and challenges on impact of big data.
Our Perspectives
The study clearly analysed development of DM applications in banking by giving insight to future research and challenges by noting the unstructured big data could be cumbersome to explore and thereby showing the Dm technique that could be exploited in tackling the issue. E. g. text mining, entity extraction and social network analysis through clustering and classification which clearly shows enough evidence of both application and its popularity. The study also shows that Weka, MATLAB, SPSS, R and RapidMiner seems to be the most popular software adopted by banks which is used for fraud detection, risk management and CRM although most application used more than one technique. DM applications in banking has helped in value creation through customer information gathering and these generate insight into the potential and valuable information yet undiscovered.
Concerns and Limitation
The study is basically focused on creating value and use of data mining technique and financial technology in the banking industry however our major concern is that the study seems to have ignored that the use of expensive software or data mining is not enough in data science and it will be better if the study focus more on the embedded analytics and statistical model which is very important. Machine learning, deep learning is all scientific approach, so attention should also be given to the statistical approach. This is the era of big data and research directions of the applications of DM in banking keeps changing because of the amount of data generated, the cost of purchase and maintenance of this new technology might affect the banks.
The study should also focus on privacy issues as it relates to the new GDPR guidelines which involves banks collecting information about their customers through text mining to understand the trends in their purchase attitude which is very susceptible to being stolen through hacking. Data mining has lots of benefit for the banking industry, attention should be given to security, privacy and information misuse and therefore we suggest that this should be handled with care and all banks must therefore be compliant with the new GDPR guideline and be more focused on internet security.
Finally, the techniques used by the banks for data mining has provided the accuracy of its own limits a typical example is the Cambridge analytical getting information of people through text mining from the social media and using such information against the wishes of the people during the Brexit referendum.
