FinScan has combined experience in data quality with technological expertise in screening to provide an Anti-Money Laundering (AML) solution designed to help financial institutions develop more efficient AML programmes that generate fewer false positives and support better detection of true alerts.
The company is part of Pittsburgh-based Innovative Systems, which was founded in 1968 and developed data quality solutions to cleanse, profile, match and link data with precision. In 1995, Innovative Systems applied its data matching technology to address the need for AML compliance screening and created FinScan.
“We have two lines of business, data quality, which provides our customers with accurate data to make better business decisions, and FinScan, an AML application based on our data quality engine,” explains Mayank Sharma, senior product marketing manager at the company. Data preparation capabilities provided by Innovative Systems improve screening accuracy and efficiency by fixing data anomalies and provide a base layer for FinScan AML, which includes modules covering customer screening, entity screening, transaction screening, ultimate beneficial ownership due diligence, and risk scoring of business relationships.
The solution comprises a proprietary data quality engine based on early work by Innovative Systems, a crowd sourced data dictionary that captures phrases and name patterns, and access to over 70 public sanctions and exclusion lists, including OFAC, HM Treasury, EU consolidated and terrorist lists, PEPs and more. Using cognitive and fuzzy data matching algorithms rather than rules-based probabilistic or weighted field scoring approaches common to many AML products, FinScan claims to reduce false positives and provide explainable matching that can be shared with regulators. The inclusion of AI automates the detection of names in different records and provides a simulation tool to suggest what users should see if a data field is missing.
FinScan can be used on premise, cloud hosted or in a hybrid environment with the majority of new users favouring the cloud option. It runs natively in the Microsoft Azure cloud, along with FinScan services such as constant updating of sanctions and PEPs lists. It can also be implemented in other clouds such as AWS or Google Cloud, although at this stage, users must manage the database maintenance and scheduling activities.
Sharma highlights FinScan’s differentiators in the AML market as advisory services provided to customers ahead of solution implementation, the capabilities of the company’s data matching engine, and its data dictionary. He concludes: “We go to market to help firms find hidden risk so that our customers can do business confidently.”
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