Rethinking Your Fraud Approach: Activating Real-Time Fraud Detection and Prevention
A few weeks ago, we began our series on “Rethinking Your Fraud Approach” with a timely issue, namely: How do businesses address the alarming increase in fraud against consumers due to COVID-19? However, even after the current crisis is over, new societal behaviors such as working from home and more online time will remain — meaning we will always have to maintain heightened vigilance. Fraudsters aren’t going “unlearn” their new techniques, after all. Today, we’ll continue our exploration to examine how to actually activate your new strategy for better fraud detection and prevention.
So far, we’ve examined how leveraging the right mix of internal and third-party data can help organizations detect and prevent modern fraud activities. For example, with internal data you can apply network analysis and stream analytics to identify suspicious activity patterns for existing customers. With third–party data, you can create even more accurate risk assessments using insights from billions of online transactions, not to mention income, spending and payment behaviors about existing and prospective customers.
Now that you have all this data at your fingertips, how do you actually activate it for real-time fraud detection and prevention? Organizations tend to lean either to fully automating the decisioning process with this data or simply making the data available to their fraud operations team.
The case for full automation is hard to deny, yet it isn’t without its own risks. Full automation without any manual intervention increases the risk of turning away a perfectly good customer while accepting a fraudulent one. On the flip side, manual decision review is time-consuming — perhaps even more so once you provide your fraud team with a wealth of additional data to consider. The lack of timely decisions creates friction between the business and its customers which in turn increases the risk of losing those customers. Adding staff to handle manual review simply doesn’t scale.
With Enova’s 15+ years of fraud detection and prevention know-how, we recommend combining machine learning and a decision management service to augment — not replace — your fraud ops team. Machine learning can identify patterns humans cannot easily detect and automatically apply all those techniques we’ve discussed that leverage both first and third-party data. This enables you to both auto-approve applications that are clearly low risk and auto-decline those that are clearly fraudulent — while flagging those cases that aren’t so clear-cut for manual review by your fraud ops team.
Now comes the real question. Once organizations build an ML-based fraud detection and prevention model, why do so few put them into production? After defining the model, you have to deploy it, connect the various data sources, then run rules based on the outputs of the model. Managing all these details can be a real challenge, which is perhaps why, according to the International Institute for Analytics, successful deployment rates are less than 10%.
Fortunately, Enova Decisions can help your organization with all three without significant investments in IT infrastructure or software engineering resources. Contact us today to learn how you can leverage pre-integrated third-party data, machine learning models and our fraud solution accelerators to get your fraud detection and prevention up-and-running quickly.