Financial Crime World

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Industry Moves to Ensure Fitness of AI/ML Models in Financial Crime Compliance

In a bid to strengthen financial crime compliance (FCC) measures, leading financial institutions (FIs) and their partners are working to ensure the fitness of Artificial Intelligence (AI) and Machine Learning (ML) models. To achieve this goal, experts have developed guidelines for an industry yardstick that defines acceptable approaches to governance and ongoing assurance.

Shortening Development Timelines

The proposed yardstick will serve as a reference point for FIs and their partners to develop and implement AI/ML models more efficiently. With this framework in place, organizations can quickly identify gaps within their AI/ML models and address them accordingly.

Facilitating Strategic Decision-Making

In the long run, the yardstick will enable FIs to position themselves properly in terms of organizational maturity and make strategic decisions with visibility into future AI/ML models. The industry is also hopeful for further guidance on the use of algorithms in managing financial crime risks.

Better Training and Awareness

The reference point will guide stakeholders’ understanding of AI/ML models, ensuring that everyone involved in the development and implementation process is aware of the importance of model fitness.

Characteristics of a Maturity Model

A maturity model has two key components: staging mechanism and guiding principles. The staging mechanism outlines the stages of an organization’s AI maturity, ranging from aspirational to advanced implementations. Guiding principles underpinning development and operationalization can be summarized into four categories:

  • Culture
  • Governance and training
  • Data
  • Model architecture

A Suggested Maturity Model Framework

Based on Deloitte’s experience in this space, a suggested maturity model framework has been developed. This framework serves as the starting point for developing a maturity model tailored to address specific needs in FCC.

Compliance with Regulatory Requirements

Experts stress the importance of compliance with regulatory requirements, including:

  • Explainability of models and algorithms
  • Establishing culture principles
  • Undertaking risk-based approaches
  • Maintaining documentation and audit trails
  • Putting clear policies and procedures in place
  • Adequate training for staff

Deloitte’s Suggested Guiding Principles

Here is an outline of Deloitte’s suggested guiding principles incorporating key FCC requirements:

Figure 1: [Insert figure]

These principles include:

  • Tone from the top
  • Standardization of data
  • Model risk management
  • Integration into business-as-usual operations
  • External validation
  • Human resource and training
  • Data-based decision making
  • Adequacy of data pools/lakes
  • Model governance
  • Efficiency
  • Internal validation
  • Adequacy of documentation

The Future is Now

With these guidelines in place, FIs can ensure the fitness of AI/ML models in financial crime compliance. The future of FCC relies on the responsible use of AI to enhance compliance capabilities and cultivate an innovative culture.