Using best-of-breed machine learning orchestration technologies and AutoPilot ML systems dramatically reduces the time it takes to analyze data models, increasing precision while lowering decision lag time.
Fremont, CA: When the number of digital payments grows, so does the pressure on many fraud detection systems. Fraud managers are searching for new ways to improve their operations. Many fraud detection techniques now incorporate machine learning (ML) and artificial intelligence (AI). Many businesses have recognized the importance of machine learning and are increasingly expanding their use of the technology. However, although this method of working is appropriate for the first few model implementations, the more machine learning projects performed, the more manual effort is needed to keep the bots configured and current.
AI and ML have aided in detecting further fraud by being even more effective at detecting and defending against fraud patterns that would usually take much longer to discover manually. On paper, this sounds fantastic, but it also means that fraud managers will have to spend more time tracking model output and developing new models to deal with ever-changing threats.
The manual model management method may seem manageable at first, but as the number of ML models grows, adding more workers to handle and control all of the processes becomes unsustainable. Although machine learning is doing some of the ‘‘heavy liftings,’’ there is still a lot of testing needed to stay on top of fraud. This, combined with the proliferation of electronic payments, means that manually performing the routine fraud-fighting procedures is becoming prohibitively time-consuming. Managing machine learning and artificial intelligence can be very costly, which is why efficient management processes are so important.
Using best-of-breed machine learning orchestration technologies and AutoPilot ML systems dramatically reduces the time it takes to analyze data models, increasing precision while lowering decision lag time, resulting in a more agile approach to machine learning implementation and faster extraction of value from data. This translates to lower false-positive rates, fewer drops, and more transactions in the case of fraud. Process automation continues to evolve, resulting in improved productivity and benefit increases in the areas where it is used. The automation revolution is already here, not on its way. It is up to you to determine whether the fraud prevention procedures are up to par.