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The business value of anomaly detection use cases in banks is obvious. From credit card fraud to check fraud to money laundering to cybersecurity, precise and quick anomaly detection is necessary to conduct business, protect customers and protect the bank from potential losses.

Cross-border payments form an enormous chunk of transnational cash flow. However, the boon has not been without a bane – the risk of fraud.

Increasing instances of related party transactions (RPT) fraud in banks seem to be making the old ‘blood is thicker than water’ saying truer by the day.

By watching out for internal fraud schemes as they happen, real-time technology helps banks respond to threats faster, prevent financial losses and reputational damage.

Even as banks further tighten controls and processes for fraud risk management, they also need to consider some of the key technologies now available to combat fraud.

One in every 2500 calls to an IVR is fraudulent. With advancements in AI and ML, IVR systems can be made to deliver a richer and more secure customer-centric experience.

One can leverage Machine Learning as the situation warrants in a smart way, it shouldn’t be force-fit into every situation which could otherwise be handled successfully. With the advent of newer technologies, we can expect fraudsters to invent more novel attempts. So, it is imperative that the Fraud Management System is continuously enriched to discover emerging fraud patterns and arrest them from occurring.

It may be premature and, as some critics claim, presumptuous, to readily imagine a world where ‘cognitive consonance’ of non-organic, silicon-based entities blurs the lines of reality as we currently (and organically) understand it. That said, it is not difficult to objectively view and perhaps embrace the reality of AI changing the fundamental building blocks of modern economy, as we know it.