Where can machine learning add value?

Where can machine learning add value?

Identification and Verification of customers: In the context of remote onboarding and authentication AI, including biometrics, machine learning and liveness detection techniques can be used to perform:

  • micro expression analysis,
  • anti-spoofing checks,
  • fake image detection, and
  • human face attributes analysis.

Monitoring of the business relationship and behavioural and transactional analysis:

  • Unsupervised machine learning algorithms: to group customers into cohesive groupings based on their behaviour, which will then create controls that can be set more adequately based on a risk-based approach (ex: transaction threshold settings), allowing a tailored and efficient monitoring of the business relationship.
  • Supervised machine learning algorithms: Allow for a quicker and real time analysis of data according to the relevant AML/CFT requirements in place.
  • Alert Scoring: Alert scoring helps to focus on a patterns of activity and issue notifications or need for enhanced due diligence.

Identification and implementation of regulatory updates:

  • Machine Learning techniques with Natural language processing (NLP), cognitive computing capability, and robotic process automation (RPA) can scan and interpret big volumes of unstructured regulatory data sources on an ongoing basis to automatically identify, analyse and then shortlist applicable requirements for the institution; or implement (to a certain extent) the new or revised regulatory requirements (via codification and generation of implementation workflows) so regulated entities can comply with the relevant regulatory products.

Automated data reporting (ADR):

  • the use of standardised reporting templates using automated digital applications (data pooling tools) making the regulated entities underlying granular data available in bulks to supervisors.



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