Machine-Learning-based algorithms lend themselves as useful tools for analysing the possibilities of credit fraud. This is typically done by running the algorithms on various transactional data and analysing for hidden patterns.
How it differs from conventional techniques
Conventional statistical techniques for analysing frauds, such as regression analysis, assumes not only the existence of a particular pattern/relationship in the development data but also makes the assumption that such patterns/relationships would stay constant. In reality, such patterns/relationships have drifts. Drifts may have been induced artificially by a fraud perpetrator to avoid detection (E.g. Series of transactions involving small amounts of money, but adding up to a considerable amount) or may have crept in due to the nature of transactions (E.g. On account of seasonal fluctuations).
ML-based algorithms can be used to detect and flag suspect patterns. Such suspect transactions can be subjected to further investigations and courses of action. Extending the argument, the algorithms can be used to reduce false positives as well. For example, seasonal fluctuations in a series of transactions can be flagged separately and allowed to pass (if genuine).
ML techniques can be intelligently applied to a variety of use cases, particularly in the case of frauds:
- Fund Diversion: Machine Learning can be used to detect any unusual patterns pointing towards possible cases of diversion of fund
- Are funds being routed to individuals or groups of individuals on a regular basis?
- Are funds being transferred to third parties on pre-set dates (say, the 2nd fortnight of every month)?
- Are fixed amounts getting transferred? Alternatively, are amounts being broken down into smaller chunks and transferred, to avoid detection?
- A combination of the above situations
- Transactions with Blacklisted Parties: Transactions with parties who are internally or externally blacklisted (E.g. present in the AML watch list). ML-based algorithms can be used to monitor any transactions with such parties.
- End Use Monitoring: Are the funds being transferred totally unconnected to the lines of business of the borrower?
- Network Analysis: Is the beneficiary indirectly related to the borrower? (E.g. Beneficiary is one of the common directors)
With electronic transactions surging, it would be impossible for banks and other financial institutions to keep a tab on transactions manually. ML-based algorithms, working on banks’ data, can provide an effective way to keep fraudulent transactions under control.
The Lehman Brothers crisis and the subsequent recession gave rise to a new economic regime. Now, in the next normal, it’s time for yet another regime change. When it comes to charting the strategic growth of financial institutions, model risk management has earned its seat at the table in the banking and financial services industry.Read More
What does the next normal hold for ERM – a critically indispensable banking function in the post-pandemic era?
With the resurgence of COVID-19 in several parts of the globe, we are currently grappling with the biggest black swan event of our lifetime. The crisis continues to unfold to this day, impacting several industry sectors and businesses of all scales and sizes. Even banks are not an exception to the pangs of the pandemic. In the current precarious and rapidly escalating situation, enterprise risk management has re-emerged as a potent interventional mechanism to effectively alleviate the impact of a crisis.