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.