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Could Big Data Have Detected India’s Largest Banking Frauds?

See What Technology Uses Big Data to Warn Banks about Accounts for Potential Fraud
  • ABG Shipyard Scam (Rs. 22,842 Crore)

  • Nirav Modi PNB Scam (Rs. 14,000)

  • Kingfisher Scam (Rs. 10,000 Crore)

India’s biggest banking scams surfaced when the deed was already done, and there was no way to recover losses. Diving deep, we discover that in most scams, the banks’ employees facilitated the looting; raising questions about the ethics of the employees, the credibility of modern banking solutions, and the interplay of human intervention with machines.

As a banking and analytics company, one pressing question that we repeatedly ask ourselves is, “Can our systems detect such frauds, especially when the internals of a bank were involved?” Unfortunately, the answer to this question is not a definite yes or no but is rather subjective, even despite being an advanced fraud detecting system.

Understanding Banking Frauds

Before we think of avoiding risks, it becomes essential to understand the nature of banking fraud.

Preventing fraud ranks first in the priority of banks.

As seen globally, a whopping US $ 3.5 trillion is lost to frauds, re-confirming the firm need for a fraud detection tool. On the other hand, as customers switch to online payments across devices and geographies, there is more ‘surface area’ for fraud to occur.

To cope with technological transformation, fraudsters have even adopted modern-age tools. For example, a group of tech-savvy fraudsters could employ modern-day business solutions like machine learning to defraud banks and hence cannot be ignored at all costs.

What has generally been observed in such cases is that conventional wisdom and practices fail to stop financial crimes. However, with big data analytics coming into the picture, there has been a rich combination of data and technical know-how to combat fraud.

Further, in some cases, big data does not only stop exposing the fraudsters themselves but has proven useful in identifying their networks, people, places, and processes.

Fraud Detection and Mitigation

The process of fraud detection and mitigation starts at a very early stage, even before the loan is sanctioned. Capital once paid as a loan is difficult to recover, and hence banks need advanced decision-making capabilities fueled by data. This is where Artificial Intelligence (AI) and machine learning step in to identify such instances early while reducing false positives.

Further, as more and more financial crimes were surfacing each day, the Reserve Bank of India (RBI) had prescribed strong measures to be adopted by the Banks to guard against such incidents.

Key recommendations include setting up a transaction monitoring group within the fraud risk management group, alert generation and redressal mechanisms, and dedicated email IDs and phone numbers for reporting suspected frauds, which generally could not be addressed by traditional methods for identifying fraud.

Concluding Remarks

Using Early Warning Systems, a software solution, banks can analyze an incoming transaction in less than 300 milliseconds, ensuring that fraudsters (both individuals and companies) get detected as early as possible.

With this advanced machinery in place, bankers can spot fraudulent behavior and withhold payments or loans that appear to be otherwise. But what could not be factored in is the intrinsic behavior of bank personnel, opening new frontiers to technological advancements in banking.


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