Fraud analytics can identify the current behaviour and help in fraud detection whereas applying this knowledge in a model of predictive analytics can help in fraud prevention. Since tasks like data extraction and pre-processing are of paramount importance, we would need data scientists who possess not only a technical knowhow but more importantly patience, perseverance, critical thinking, and domain understanding. In here, the imputation for missing values may not be required but reported for certain attributes. Even when required, it may not be as easy and straightforward as in the different problem statements, especially when a few indicators are about to raise a red flag.
Banking runs on a set of regulatory guidelines and deals with numbers, it was only about time that it would board the AI bus. Secondly, there is this deviation angle. As we fully realize the fact that anything handled by a human is prone to deviation or personal discretion; so to be vigilant, all inputs are to be taken with a generous pinch of salt. In other words, everything must undergo a reviewing pair of eyes. How about implementing a system that can auto-understand and auto-function and auto-verify without or with minimal human intervention barring some very delicate cases?
Artificial Intelligence has been buzzing around more frequently with higher and ever-increasing intensity every passing day. Reason is as clear as a crystal: Its power and possibilities it can create. It doesn’t really matter who thinks of AI highly or lowly but one thing is certain: Its time has come. Before you buy time in realizing and delay in deciding of its adoption, it may come and stare straight in your face! You may not even have the bargaining power that you still enjoy today.