|Task||Manual Process||aiAutoPilot ML ®|
|Determine the performance of the current rules and models in the system and know where they are performing well or not||10 minutes per rule||N/A|
|Determine the best data filters to apply to detect as much fraud as possible whilst reducing the non-fraud data||0.5 – 1 day||N/A|
|Create machine learning rules and models using aiSmartScore and aiSmartRule from the filters||1 day per run||N/A|
|Put the rules and models into production||1 day per run||N/A|
The pandemic showed retailers that an effective and adaptable system to track customer behaviour can be the key to improving sales and maintaining fidelity. Data management and AI deployment can fulfil this need by redefining the essence of loyalty programs.
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 and artificial intelligence has become common in many fraud detection strategies.
Fraud managers are looking for more ways to optimize their operations, as the rise in digital payments increases the strain on many anti-fraud systems. Fraud orchestration is set to be the next step in this evolving area. It helps fraud managers manage fraud problems, both new and existing, without the manual effort.
The automation of processes has advantages in many areas of business. Helping to create predictable success, like the autopilot technology on a plane has been perfect over many years and by decreasing the time that it takes to complete manual processes and removing the chance of human error.
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