Data Mining is Useful in Preventing Bank Fraud

Competition among banks is getting stiffer and is spreading worldwide. For this reason, every bank should keep up with the changes in order to be patronized by the clients. The banks that still use old fashioned banking techniques could find themselves disappointing their clients in the long run. In short, in order for banks to stay alive in the future, they have to resort to technological means to predict what the clients need in order to provide personalized services. 

Data Mining explained

Now, banks can predict the needs of their clients through data mining. If you are not aware of this, a bank holds a lot of information about its clients. The management just needs to tap into this information regarding transaction preferences, investment wishes, and financial history so that the bank can provide services that caters to specific needs. In short, the bank should come up with ways to entice client and make them believe that the services are necessary. Of course, personalized services may be expensive but they do tend to provide more satisfaction to customers. 

Of course, before data can be mined, there must be data to start with. Thus, a bank has to come up with a centralized database for storing and managing information. This database can also benefit from artificial intelligence so that be better understanding of why clients do business with their bank. Plus, rule can also be incorporated into the workflows so that the bank can become better in detecting anomalies and abnormal banking behavior. 

Data Mining used to detect fraud

The big question is: How should a bank utilize the loads of data available? Well, the data should be used to deter bank fraudsters. An organized system of data is useful in detecting abnormal patterns that are common when fraud is attempted in credit transactions. Based on what the system gives an alert on, the bank can take the right steps to prevent fraud as it happens. 

Data mining does not only detect bank fraud. It can also help through:

• Attrition prevention. This finds out what a customer does before transferring to another bank.

• Cross selling. This can be used to find out the things that customers are most likely to purchase. 

• Defaults and bad loans prevention. Banks can identify borrowers who are high-risk.

• Increase customer loyalty and retention. Banks can come up with services that their clients really enjoy. 

• Target marketing. This is used to come up with specific banking solutions that answer clients’ needs. 

In conclusion, data mining is a useful process that can discover certain pieces of information about clients. Through the verification step of data mining, relevant information about a client can be obtained. But more importantly, information gathered from the software through banking transaction is helpful in future interactions with clients. When data mining for anomaly detection is part of the workflow, bank tellers can flag bad checks in real time. It is for this reason that real time fraud management systems are very important. Data mining, then, is more than just a strategy to stay alive in the banking industry. It is a strategy used to compete with other banks.

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