Screening
How is screening research defined ?
Think of a search box that could find any information on anything. Yes, search engines. The objective is to compare an (extensive) list of sources of information against specific research or terms to return the matches that were found.
Returned results are analysed and ranked to provide the best potential information before being displayed to the operator for interference.
What is the basis for developing a screening tool ?
A clear goal vision and close collaboration with business operators are mandatory to anticipate future obsolete results.
A first step to building such engines is to define the model of the solution.
Beginning with the obvious, what do we search? Depending on the scale of the context, the complexity of the screening methodology can vary from plain to advanced.
At this point it seems enough, but that's without considering the amount of data that must be analysed to return potential true positive information.
Should the screening process be entirely automated ?
Well, one should say it depends on the type of sources of information. If you are considering the use of an automated screening methodology in your operations it's because you want to process a considerable amount of data, link multiple unstructured sources of data and reconcile the results.
Simple balances are more comfortable to handle manually, and structured sources of information are fine.
In the previous context, will a customised algorithm narrow the list of results to precisely what you are looking for? In that context again, the automation of screening results will never be 100% reliable (not even close) and will always require an analyst's interaction to validate the findings. The most straightforward proof is Google. As performant as the most used research algorithm of the planet is, it might also return unwanted results, making you wonder why are they there?
The main reason is the variety, amount, and duplication of data that you capture in your unstructured sources. These generate false-positive results due to the non-categorized root information that falsely leads the algorithm to classify them as potential matches.
Businesses and financial institutions continuously face risks and obligations: business growth, regulatory expectations, etc. The level of responsibility is growing in the prevention of financial crime. It goes without saying for companies and financial institutions to be vigilant about the collection and preparation of data and the solving of problems.
Pideeco can help you set up an automation technique when filtering. By adding an innovative solution to meet your specific needs while respecting the regulation, you will have an advantage that will save you time.
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