Banking Systems

Data Mining Helps Manage Bank Fraud

data_mining 2Nowadays, there is stiff competition among banks. This is why banks want to outdo each other by offering services that can appeal to clients. Now, banking institutions that still utilize old-fashioned marketing strategies and banking techniques will surely be left behind by the competition. In short, in order to compete with other banks, a bank must be technologically advanced and progressive as well as sensitive to clients’ needs. This is where data mining proves useful.

What is Data Mining?

Basically, a bank has a wide knowledge about its clients. All the managers have to do is mine this information in order to find out the needs of their clients regarding preferences in transactions, their financial history and their desires regarding investments. Having knowledge about this these can give banks the power to cater to what their clients need, making them hooked to the services offered. There is also no legal issues about the data generated as these can follow the internal and external audit requirements as well as meet the regulations of the central bank and the of the government.

But of course, before a bank can do data mining, it must first create a data warehouse. This data warehouse is a centralized storage system of data that can be saved, protected and managed. The bank can then use artificial intelligence in order to understand how and why every client uses their services. Rules can also be incorporated into the workflows so that the bank can detect abnormal behavior and that the bank can create alert systems to inform the management of abnormal banking behavior.

data miningHow can Data Mining be used against bank fraud?

The huge amount of data available to banks can be sued to deter bank fraud. How? The transparency of transactions provided by an organized system allows banks to detect fraudulent patterns in credit transactions. When these patterns are detected, banks can do take the right steps to stop fraud and keep it from happening again in the future. Aside from managing bank fraud, data mining can also help in the following ways:

  • Attrition prevention. Find out what a client does before transferring to another bank
  • Cross selling. Get a profile of their clients, include the products they are most likely to buy
  • Target marketing. Give clients specific banking solutions based on the knowledge the bank has about them
  • Defaults and bad loans prevention. Find out the profile of a high-risk borrower
  • Increase customer loyalty and retention. Determine the services and benefits that clients will really like

In conclusion, data mining is a procedure that intends to discover knowledge and not just verify the information. Verification affirms the information but the new data gathered from the software through banking transaction can be used for future interactions with the every client. Using data mining for anomaly detection as part of the workflow, a teller can flag bad checks as they are scrutinized at the teller window. This is a good example of real time fraud management systems detecting crime as it happens. The implication is the bank can protect its assets and its clients’ investments. In short, data mining is not just a strategy used to stay alive in the game. It is a strategy that works well in edging out the competition.

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