How Data Analytics Helps Banks with Fraud Detection

The advent of technology has empowered businesses across a broad spectrum of industries, including the banking industry, with plenty of benefits. However, it has also posed a growing challenge for banks: increasingly complex financial fraud. It has spotlighted solutions that can help banks fight against financial fraud and actively endeavor to prevent fraud.

Fraud could be referred to as a multi-structured and multi-layered phenomenon. It poses a significant challenge to financial institutions. Effectively mitigating financial fraud will help banks protect their customers, employees & reputation while enhancing the financial system’s resilience.

And today’s accelerating rate and complexity of financial frauds demand better and more effective defense solutions. It would include a combination of robust Machine Learning, Data Analytics, and Predictive Capability. One of the most popular solutions in this regard, then, has proven to be analytics, which, when combined with other tools and systems, enables substantially better fraud detection, prevention, and risk management. How?

Well, here is a closer look at some of the many, many ways in which analytics enables better fraud detection and prevention for banks:

Improved Transactional Monitoring

One of the critical measures banks use as part of their fraud detection and prevention strategy is transactional monitoring. Conventionally, this process uses solutions that use KYC data, set base transaction rules, and compare this data against pre-defined transactional monitoring settings. This method leads to a relatively high number of false positives and many other issues. But with analytics and advanced data science, banks can now leverage an automated ‘Alert Hibernation’ process to identify anomalies and, thus, cut down on the number of false positive alerts.

Enhanced Segmentation

High-quality segmentation is the foundation of any bank’s efforts against money laundering, risk alleviation, and fraud prevention. To ensure the continued efficacy of such actions, banks are now starting to leverage advanced data mining and aggregation methods to go from gleaning fewer high-level, hypotheses-driven segments to lower-level, behavior-driven segments. Such data-driven segmentation involves, among other things, extensive analysis of customer profiles which include behavior, etc.

Better Leverage Artificial Intelligence and Robotic Process Automation

More and more banks are now using the novel combination of analytics, intelligent automation in robotic process automation, and artificial intelligence to implement new-age processes across their operations. While there are plenty of use cases of this novel combination in banks, informed decision-making, malware identification, and task automation are the most relevant in the context of fraud prevention in banks.

Advanced Customer Reviews

Despite the continued advancement of technology, your run-of-the-mill KYC process in the banking industry still involves manual customer reviews, be they triggered by risk or simply periodic. Unfortunately, such manual reviews drive up costs more often than they lead to changes in customers’ risk ratings. Thankfully, a solution to this challenge is found in analytics-driven events-based reviews wherein analytics data, such as transaction monitoring, alerts, and events are integrated with external data to calculate customers’ Risk Scores. The reviews are triggered only in case of changes in risk score based on event changes.

That about sums it up, folks, some of the countless ways in which an analytics-driven approach can empower banks to not only reduce fraud but proactively prevent fraud from happening at all. As the risks and the market’s demands and expectations continue to proliferate, analytics have proven to be a rock-solid resource in banks’ arsenal, allowing them to improve their risk management and realize significantly better operational efficiencies.

So, what are you waiting for? Go ahead and start looking for a trusted service provider for developing a new-age, analytics-driven banking fraud detection software for your organization right away!