Ever since payments first began flowing across e-commerce websites in the 1990s, fraudsters have targeted online victims. In the interim, digitized payment channels have proliferated with the emergence of mobile payments, cryptocurrency, and peer-to-peer payment options along with new national payment systems. The volume of digital payments has risen along with the number of payment channels. McKinsey & Company valued global digital payment revenues at $2.2bn in 2023, up 11% from 2022. 

These rising transaction values have created plenty of opportunities for fraud, turning a steady stream of financial crime into a torrent. In 2023 alone, the economy lost an estimated $485bn globally to scams and bank fraud schemes—so the need for sophisticated tools to unpick complex fraudulent cash flows is more urgent than ever. Fraud analytics is now a game-changing technology in this battle, offering innovative solutions to detect, prevent, and mitigate financial crime.

To follow the money, follow the data

Fighting financial crime and fraud is not simply a ‘one and done’ process; it’s a constant cycle of continuous improvement as fraudsters re-arm themselves with the latest tactics and technology. Banks must keep enhancing their fraud analytics process with new capabilities, ranging from more sophisticated machine learning algorithms to streamlined workflows that facilitate a faster response. 

But prevention goes beyond analytics—it uses AI and advanced algorithms with data integration that is trained to identify and learn from both legitimate and fraudulent behavior. The emergence of modern machine learning now enables financial analytics tools to sift through massive volumes of transaction data. The Splunk App for Financial Crime and Fraud analyzes this machine-generated data to detect the anomalies and outliers that point to suspicious behavior with precision while reducing false positives.

A centralized platform is key to this fast response and organizational resilience. It offers a unified approach to fraud and crime prevention across multiple departments for consistent, collaborative investigations.

The Shift From Reactive to Proactive

The ability to spot financial crime is an advantage, but it’s not enough on its own. Banks must be proactive in their fraud mitigation by forecasting and preventing fraud with increasing accuracy. This not only helps to minimize financial losses, but also preserves another form of capital: trust. Customer trust is invaluable at a time when open banking makes it easier to skip between banking providers and customer loyalty is low. 

This is where data integration comes into play. Effective financial crime and fraud applications index machine-generated data continuously instead of in batches. This identifies threats as they emerge rather than leaving investigators to discover them after the fact.

Modeling and forecasting further enhance the fight against financial crime by analyzing historical data to forecast future risks. These AI-driven models identify potential threats before they happen by drawing on historical data to forecast future patterns, allowing organizations to implement preemptive measures. The Splunk platform generates risk scores based on its fraud detection algorithms, which helps to mitigate risks before they materialize.

Time: The ultimate weapon in fighting fraud

Preventing a fraudulent payment is a race against time. It is still even possible to retrieve a wire transfer after it has been issued, so every second is critical.

Effective fraud intelligence not only improves detection but also streamlines the financial crime and fraud response by prioritizing risks. With financial crime and fraud teams facing a rising tide of fraud alerts, it’s important to focus the lens on the most pressing, highest-risk cases. Machine learning models surface these for investigators, who are then able to hone their response.

One example of this is the Splunk automated risk-based alerting capability. Financial institutions can configure this through correlation searches that interrogate multiple data sources and quickly notify teams of potential fraud attempts.

Another extra layer of functionality for combating financial crime and fraud is process automation. Through automated workflows and playbooks, Splunk can execute predefined responses such as freezing accounts, alerting compliance teams, or initiating further investigations. While this rapid response reduces the window of opportunity for fraudsters and limits potential damage, that is not the ultimate goal of the Spunk platform.

Staying One Step Ahead of Fraudsters

Financial crime and fraud resilience applications have become indispensable in the fight against financial crime. Their ability to follow the money — and even to predict where, when, and how it will flow — makes them a key weapon in the fight against financial criminals. As fraudsters evolve, so too must our data-driven approaches to unmasking them. Beyond exposure and damage limitation, staying one step ahead of cyber criminals reinforces the stability of our financial systems and allows people to bank and invest with confidence.