White Box or Black Box? Explainable Machine Learning Decisions in Compliance

More and more bank compliance departments are turning to effective compliance software to help them tackle the scale of the challenges that they face. When selecting a software provider, it is important to ensure the traceability of decisions made on the basis of machine learning. This is the only way to ensure compliance with legal requirements and exploit all the benefits of the system.

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Over recent years, the work of compliance departments has become increasingly complex and time-consuming. The number of new regulations has sky-rocketed, along with the volume of criminal activities and cross-border transactions. It has become almost impossible to adequately detect critical cases that indicate money laundering, market abuse, fraud or terrorist financing using traditional methods.

Compliance software boosts efficiency and cuts costs

The answer lies in effective compliance software that can analyze vast quantities of data and identify suspicious patterns and risks. This provides a massive boost to productivity and cuts costs. Effective compliance software is based on dynamic machine learning processes that are able to learn from experience and find independent solutions, yet some software on the market is unable to present such complex decision-making processes in a transparent and explainable way. However, neglecting this issue will only lead to problems further on down the road. There are 5 main reasons for tackling this issue.

5 key reasons why explainability is vital in machine learning decision-making processes:

  1. To comply with regulatory requirements for BDAI-based decisions
  2. To create trust and transparency in the company
  3. To ensure ongoing optimization of the systems
  4. To improve customer communication
  5. To achieve a better hit rate, which reduces costs and increases productivity

Luxury or Absolute Must-have?

It’s clear that explainability is not a luxury but an absolute must-have. Germany’s Federal Financial Supervisory Authority BaFIN clearly stipulates that machine learning models in the financial sector have to be explainable. Explainability also helps to reduce the scepticism about AI solutions that is widespread at all levels in banks, and it also builds confidence. Everyone involved should be able to understand the rules that govern the processes and how certain decisions are made. The models can be continuously improved by checking and adjusting the accuracy of the results that are automatically generated based on self-learning algorithms. Here too, traceability is vital. And finally, relationship managers need to be able to provide their customers with a clear explanation for why their transactions have been classified as money laundering or fraud.

The Conclusion? Explainability is Essential

For compliance departments, the explainability of machine learning decision-making processes is essential for ensuring compliance with laws and regulations and to steadily improve the quality of the system. That’s why banks should only select software companies who can guarantee to provide this explainability in every respect. This is the only way for compliance departments to truly increase their productivity and cut costs.

Checklist: How banks benefit from Machine Learning Decision-making Processes

Discover further reasons why it is worthwhile for banks and financial service providers to make machine learning comprehensible and find out how to do this in our checklist.

Download Checklist

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