Fraud and Scams are Always Evolving

But are financial institutions evolving fast enough to curb or pro-actively combat its risks?

Financial institutions are expected to safeguard client information and investments along with optimizing returns on their investments.

However, with fraud getting multi-dimensional and more sophisticated by the day, banks are in a greater need to go beyond their conventional control systems, and invest in advanced analytics platforms to achieve a more robust fraud defense system.

Identity theft risks through credit and debit cards, email phishing, and frauds with wires and cheques are just some common cases associated with the banking sector.

In case of highly sophisticated phishing fraud, individuals may not even be aware that they are a victim. Not all customers clearly understand the policies and procedures of their banks and hence are highly vulnerable to attacks of fraud like phishing, where emails disguised to look legitimate ask clients for confidential and highly sensitive information.

The sooner banks are able to effectively detect fraud, the more manageable are the reputational damages incurred on the financial institution. However, there is also a need to reduce the amount of “false positives” or false alarms to control the negative impact it may have on client trust.

 

Fraud Analytics 101

Fraud analytics gathers, stores, and mines large sets of data to spot discrepancies and patterns to identify anomalies. Analyzing and profiling a customer’s spending behavior and deriving a predictive score can help in identifying high-risk, seemingly fraudulent transactions.

For example, a credit card may be blocked by the financial institute due to suspicious transactions. If a credit is usually used for small purchases and one day used for a large purchase at an unusually distant geographical location, the card may be instantly blocked or the cardholder may receive a call from the bank’s representative to reconfirm if it was a legitimate transaction.

With the huge explosion of data availability, it has become impossible to effectively utilize or analyze data without leveraging intelligent automated systems like Machine Learning or Artificial Intelligence.

Score and Deploy ML Models into Production Within the Same Platform

Banks should leverage data insights, which help identify anomalies like unusual patterns in fund transfers and a sudden use of a dormant account involving high value transactions.

For certain, all banks have their own internal Control Teams, who manually look at every transaction that takes placemaking it unsustainable in the long run. At the rate data is growing, financial institutions require stronger systems which flag fraudulent activities automatically, and run predictive analytics on historical data points to predict possible scenarios of attempts of fraud that may arise in the future.

Just recently, the National Payments Corporation of India (NPCI) announced that a security breach has put around 641 customers at risk of fraudulent activities on their debit cards. This can happen to any organization, not only the financial sector. Both debit and credit card details can be breached from almost any industry.

We have had examples in the past of malware infections in mobile where one-time passwords (OTPs) can be accessed by fraudsters to perform online transfers. SMS OTPs, once considered to be a very secure form of performing banking transactions, is also falling prey to fraud.

Apart from fighting fraud, Artificial Intelligence (AI) can reproduce other direct benefits to financial institutions in the areas of credit analysis, product designs, financial planning, etc. Considering the data volume and complexities banks deal with, it would be more than right to move from a conventional system to some advanced form of Artificial Intelligence to turn data into valuable customer insights and create personalized plans for every client to stay competitive.

In an entirely customer-centric industry like banking, these institutions need to ensure they attack fraud strategically without causing any disruption to their customers’ banking needs. With banking going widely electronic and more virtual, there is a greater need for the banks to have more advanced technology in place to detect fraud on a real-time basis.

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