By : Laurie Gentz
January 18, 2018 01:00 PM
In the ongoing battle against fraudsters, financial institutions (FIs) need to equip themselves with the most effective tools to combat the criminals and protect their customers.
One area of innovation that is making a particularly significant contribution to the fight against fraud is machine learning - the ability for a computer to analyze data and adapt the patterns and detections based on data, without human input.
This level of efficiency is proving increasingly important for FIs that want to react quickly to the evolving threats posed by fraudsters. The signs suggest that it won't be long before machine learning is seen as a critical part of fraud detection and prevention systems.
Why machine learning is so valuable
One of the key reasons why machine learning is gaining importance as a component of fraud prevention is its ability to keep up with changes and trends in the threat landscape and to react real time and not after the fact.
Fraudsters are learning, evolving and becoming more dangerous all the time. With such a large and multifaceted threat to contend with, FIs need to approach fraud prevention with layered defenses, machine learning and rules.
Today's advanced machine learning solutions are capable of combing through vast amounts of data to identify fraud trends, find signs of suspicious activity or new threats.
A recent report from Aite Group, sponsored by iovation, revealed that more than two-thirds (68 percent) of FIs in North America have plans to invest in machine learning to help combat fraud.
Julie Conroy, research director at Aite Group, said: "Effective fraud prevention is now a competitive issue for FIs. Early adopters of advanced analytics are able to increase their fraud detection, and the associated improvements to the customer experience give them a decided edge over their competitors that lag in these investments."
Considerations in implementing machine learning
There is widespread agreement on the value of machine learning for fraud detection and prevention. If your business has committed to adopting this technology, the next step is to find a solution that meets the specific needs of your organization.
The report Operationalizing Machine Learning for Fraud, published by American Banker and sponsored by Feedzai, emphasized the importance of knowing exactly what you want to achieve.
Is your focus on driving up fraud detection rates or minimizing disruption to consumers, for example? Or has it become essential to update your technology infrastructure simply because older systems are no longer able to cope?
The answers to these questions will provide the guidance required to ensure you choose the right solution for your enterprise, in some instances the answer may be a combination of solutions
It's also important to think about characteristics such as the flexibility and scalability of machine learning technology. Implementing an adaptable, cross-channel system, for example, can improve your ability to detect and prevent fraudulent transactions in everything from the ATM channel to merchant point-of-sale devices.
By taking a considered approach and ensuring that you choose the most effective enterprise fraud solution, you can protect your business and their customers from current and future fraud threats.