Contributed by Mphasis

Anti-Money Laundering Transaction Monitoring implementations are ubiquitous across all tiers of institutions as the level of complexity of financial crimes is on the rise.

The level of sophistication of the TM platform varies as we move across tiers. This paper discusses some of the challenges faced by Financial Institutions (FIs) in implementing TM platforms. Though the challenges could apply to all sizes and nature of FIs, the scope of this paper is specifically for challenges at top-tier and mid-tier financial institutions.

The challenges faced by the FIs can be organized into the following 5 categories:

  • Span of the FI – the geographical, entity & LOB and instances/expanse of applications can pose specific challenges
  • Data availability and quality is a key issue most FIs grapple with
  • Configuration of business rules and the TM platform has a significant impact on how well the TM implementation works
  • Infrastructure selection and maintenance choices decide how effectively the TM platform performs
  • Cognitive computing is emerging as the new imperative with smart data, rule based computing and machine learning techniques being used increasingly for eliminating false positives and for detecting more complex alerts

Subsequent sections of this paper discuss each of these categories in more detail.

Anti-Money Laundering (AML) Transaction Monitoring [TM] implementations are ubiquitous across all tiers of institutions as the level of complexity of financial crimes is on the rise.