Recent trends of Data mining in Banking

Banking is the life blood of finance in a country. A country cannot do any transactions without banks. Therefore this sector offers a large database for analysis.  The most recent technology adopted – core banking has created even more avenues.

Banks generate a large amount of data during its everyday transactions like customer information, transaction details, risk profiles, credit card details, limit and collateral details, compliance and Anti Money Laundering (AML) related information, trade finance data, SWIFT and telex messages. This data is used by the banks to arrive at conclusions and make different decisions regarding customers are optimizing their processes.

Data mining and banks

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Data mining is the process of deriving knowledge hidden from large volumes of raw data. The knowledge must be new, not obvious, must be relevant and can be applied in the domain where this knowledge has been obtained.

Banking has become very competitive in today’s world. The bankers have to make smart decisions to stay in competition.

  • In today’s highly competitive market environment customers are spoilt for choices. Banks need to be proactive in analyzing customer preferences and profiles and tune their products and services accordingly in order to retain their customer base. It would appear to be the case to keep running to stay in the same place. (Bhambri, 2011). By segmenting customers into good customers and bad customers, banks can avoid losses before it is too late (Kazi and Ahmed, 2012). By analyzing patterns of transactions, bank can track fraud transactions before it affects its profitability (Ogwueleka, 2011). These are highly desirable areas where data mining could help
  • The MIS of banks contain huge volume of data both operational and historical. Data mining can help in using this huge volume of data to make critical decisions.  Banks which adopt methods to wisely use such available data by applying data mining techniques will be hugely benefited and have an advantage over others who don’t. The areas which are benefited using data mining techniques to make decisions include marketing, risk management, fraud prevention, customer satisfaction,  money laundering etc.  credit appraisal systems are also benefited through this
  • All lending activities of a bank involve a certain amount of risk; Proper assessment of this risk will make the risk management process easier and will also enable to limit the risk of financial loss to the bank. Most important is assessing correctly the capacity of the customer to repay the loan.  Use of data mining techniques will make the task of the credit manager easier.  It will help the credit manager to know which customer will delay or default in repayment of the loan.  This advanced knowledge will help the bank to adopt preventive measures to avoid losses.

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  • To forecast such situation parameters such as turnover trends, balance sheet figures, utilization of credit limits, cheque return patterns are analysed. Historical default patterns will help in predicting future defaults when some patterns are discovered. Usage of data mining techniques will then be helpful to enhance the accuracy of credit scores and predict defaults in advance. The credit score represents the borrowers creditworthiness. Behavioral scores are obtained from probability models of customer behavior to forecast their future behavior in various situations. Data mining can derive this score using the past behavior of the borrower related to debt repayments by analyzing available credit history.
  • Banks are using the data which is available to enable personal loans for the customers through their ATM’s. This automated loan facility will help banks revive retail credit. The banks are using big data analytics to analyse facts such as the customer’s personal details, work profile, income, and payment capacities to decide on the customer’s credit worthiness. After analyzing all this data, the bank decides the amount of loan that the customer is eligible to get. The customer gets an offer for the loan, the next time he uses the ATM. If the customer is interested, he can just agree to the terms and conditions and type in his registered mobile number to get the loan amount will be credited to the customer’s bank account.

Hence usage of data mining techniques will greatly help banks in improving their business.

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