Byzantine aspects within governance of data & related ‘Anti-Money Laundering’ woes

Ever since the history of mankind evolved with perceptive skills and expanded the horizon of his/her thought process, criminology has evolved and therefore crime and related fraud have become the topic of 21st century.

Today no industry is immune to these threats and financial institutions are
investing huge time and resources in developing robust compliance programs.
Additionally, investment in high-end solutions supported by analytical
engines with research capabilities to support in the submission of SAR’s
(Suspicious activity reports) has become critical.

Know your customer and risk rating them has become a norm, while not being able to rely on quality input and accessibility of quality data. Therefore, know your data has become yet the most important factor and related governance when it comes to relying on third parties, despite they being partners, has become the challenge.

Clear understanding of how data is leveraged across various AML processes and determining data lineage across systems, storage units is of paramount importance.

Indeed it is a burden for AML officers to continuously scrutinize and attain
proficiency in data management, while improved data management has assisted
them, and ‘trained’ them indirectly during their investigations.

What the customer needs, coupled with sophisticated software applications,
and the accurate understanding of data definitions is an understanding compliance and business units should acquire.

Yet, it is a daunting affair for many organizations despite having
sufficient data enablers. One needs to be cognizant that technology
platforms are to support in this aspect of governance while working with
centralized data dictionaries, along with data quality monitoring efforts.

Many solutions do rely on ‘if and then’ statements to generate quality alerts
that are supported by rules set by rigid standards and guidelines. Lack of
flexibility to monitor such rules against pre-programmed rules, or as some
may call as ‘detection models’, remain to be the key concern.

An approach towards transformation is required at this stage with clear
definition of the scale of operations that are well determined by the
products, businesses and geographies that are involved.

This should be taken care while synchronizing existing customer data and
in parallel re-mediating existing data. Furthermore, the management teams
will need to budget for infrastructure and training to set up a centralized
back-end review team that can process information across multiple tiers of
business lines.

Therefore, analytic s including behavioral studies based on a single and associated customer view needs to be established that are driven by enterprise data management and long term sustainable compliance solutions.

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