The fraud tool landscape is changing. Fraud managers are looking for more and more ways to optimize their operations as rising digital payments increase the strain on many fraud detection systems. The use of machine learning (ML) and artificial intelligence (A.I.) has become common in many fraud detection strategies. Many organizations have seen how effective ML can be and are rapidly expanding its use, further developing business value. However, while this way of working makes sense for the first few model implementations, the more machine learning projects that are undertaken, the more manual effort is required to maintain the bots to ensure they remain optimized and up to date.
There is no doubt that A.I. and ML has helped to increase the amount of fraud that is detected by becoming much more efficient at spotting and defending against fraud patterns that would otherwise take much longer to discover manually. This is great on paper, but the flip side is that the fraud managers are required to spend more time monitoring performance of the models, as well as building more models to take on the ever-changing threats.
The manual process of model management may be tractable at first, but as the number of ML models increase, it can become rapidly unsustainable to add more and more staff to manage and monitor all the processes. With machine learning doing some of the ‘heavy lifting’, there is still a large amount of monitoring that is required to keep on top of fraud. This, coupled with the rise in electronic payments, means it is becoming far too time consuming to manually perform the usual processes used in combating fraud. Managing ML and A.I. can become very expensive, which is why efficient management processes are so critical. Even when they are utilizing ML and A.I., many rules-based fraud detection solutions require near round the clock monitoring to ensure effective fraud detection.
Automating the fraud management process using model orchestration is set to be the next step in this evolving area. Helping fraud managers to manage fraud problems, both new and existing, without much of the manual effort that the process usually requires. Complete fraud management will also be possible by extending the classic monitoring tools to focus on maintaining fraud strategy effectiveness. When the performance of those models or tools drops, or new fraud patterns are identified, new models can be developed and tested automatically and optimized into the strategy.
This will be particularly effective for reducing the impact of fast-moving types of fraud. As the system will rapidly update automatically to keep on top of the moving trend – much faster than if it were managed manually. As well as detecting more fraud and increasing acceptance levels of genuine transactions. This requires investment, but the reasons for that initial investment are clear – offering huge savings, as any fraud will be detected and stopped much faster; while allowing fraud managers to focus on extremely difficult fraud cases, as well as on providing more value for their customers. As a result, automated A.I./ML based fraud systems are quickly becoming seen as the future of the fight against fraud.
Using best of breed machine learning orchestration technology and AutoPilot ML systems significantly reduces the amount of time it takes to analyse data models, increasing accuracy whilst reducing decision lag time, leading to a more agile approach to machine learning implementation and increased value from data, faster. When it comes to fraud, this means reduced false positive rates, less declines, and more transactions. Process automation is continuing to innovate and provide increased efficiency and profit gains in the places it’s implemented. The automation revolution isn’t coming, it’s already here. It is up to you to decide whether your fraud processes are up to scratch.