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Because the investment decisions were made a long time ago, the systems bind high resources for both IT and the department and are now reaching their limits, recently I noticed a clear change of opinion. While the statement used to say, “We need a monitoring system,” we hear more and more often: “We want to see better and, at the same time, reduce costs.” When designing the options, different approaches are chosen:
Replacement of the existing system:
A common goal here is to find a system that is easy to use, which facilitates the operation and configuration of the system for non-IT-related specialist users. The departments want to be able to adapt workflows of the system to their own processes, to independently analyze large amounts of data without statistical know-how and to simulate new rules in the combination of scorecards by hand, without having to initiate complex IT processes Artificial intelligence solutions in canada provides you the assistance in your task . The banks that choose this path have usually reached their threshold with the existing system, and the decision makers at the time have thankfully left the area or the bank.
Optimization of the existing system:
This is mostly about optimizing the rule logic to reduce the false positive rate. One speaks also of “Tuning”. In the first step, the generic rule set is replaced by an individual that more closely matches the bank’s product and customer profile, and then, in a second step, uses statistical methods that define the characteristics and corresponding expressions in the construct of a scorecard meet more precisely. This procedure is only recommended if it is ensured that the necessary statistical know-how is available in the bank and a stable process is established so that it does not remain a one-time exercise AI Solutions in canada solve this for you. It is only a matter of time before providers emerge on the market who offer this ongoing optimization of rule logic as a service model.
Complete abolition of classical detection methods:
A new trend that has recently been seen in the press in the context of a leading UK bank is the abandonment of the traditional way of transactional monitoring. The reasons are the ones described above: The classical systems produce too many false positives, and the complex cases do not recognize them at all. Instead, the bank is talking about expanding analytics teams to use artificial intelligence and machine learning to identify money laundering patterns and identify individual cases with Artificial intelligence solutions in canada .
Should retain the agility with Artificial Intelligence:
Meanwhile, banks in our language area, at least in part, seem to follow this trend. I am seeing more and more banks building new teams with young scientists who work exclusively with newer technologies in parallel with traditional monitoring. Here’s the challenge of keeping these young teams working outside of the mills of business and IT so that they retain the agility they need in the dynamics of money laundering and fraud. Furthermore, close coordination with the external auditors and supervisory authorities in such a project is essential.
AI Solutions in canada recognized these developments at an early stage and positioned itself as a market-leading supplier of analytical software for optimizing and supplementing existing prevention systems.