The Risks Of Machine Learning In Financial Services

Our first article discussed some of the key findings from the Bank of England's (BoE) and Financial Conduct Authority's (FCA) 'machine learning in UK financial services' survey.

Here, we will delve into the risks associated with machine learning (ML) applications and discuss what we have learnt from our experience of analysing and investigating how financial institutions (FIs) use sophisticated models.

Machine learning risks

Risks associated with ML are driven by three interconnected factors:

• Data;
• Models; and
• Governance.

The data used to train, validate, and test ML applications may be erroneous, biased, or unrepresentative. Models may have mechanisms and outputs that are unexplainable or uninterpretable. Weak and ineffective governance can lead to insufficient assessments and control of ML-based applications.