The Coalition for Integrity has published a report summarising how machine learning (or AI) can be used in anti-bribery and corruption (ABC) compliance programmes (the Report).

The Report provides companies with key points to consider in determining whether (and if so how) they should consider developing or using machine learning solutions for their ABC programmes.

The Report follows increased interest from regulatory authorities globally in relation to the use of data in ABC compliance programmes.

As well as providing examples of where machine learning has been successfully implemented in ABC programmes, the Report summarises some of the key legal, cost and practical issues that can arise in using machine learning.


Potential advantages of AI include:

  1. Assisting in the timely identification of higher-risk transactions and relationships, including higher risk contracts and transactions with third parties.
  2. Helping companies detect new or changing risks and adjusting their compliance programmes to keep pace with their businesses’ evolution (as required by most authorities).
  3. Cutting through large volumes of data quickly.

One case study the Report examines is the implementation of machine learning in post-acquisition compliance integration. The purchaser created a platform to draw on data from across multiple systems of the newly acquired group including finance, compliance, and human resources to identify transactions and third parties that potentially posed a risk. The platform applied a risk-scoring approach; certain transactions or relationships were deemed higher-risk based on the number of risk attributes and greater weighting of higher-risk attributes (e.g., involvement of a politically connected entity). The solution reportedly reduced the review costs associated by millions of dollars.


The Report also summarises challenges when using AI in compliance programmes, including the following:

  1. Care needs to be taken to ensure that AI is used ethically. Ultimately, humans should remain in control of delegation to AI systems and the responsibilities those systems have.
  2. Companies should be wary of data privacy/protection laws and cybersecurity implications when processing data given that development of any machine learning system involves drawing on large datasets that may be stored in a variety of locations.
  3. The process of developing and implementing machine learning solutions tailored to a company’s needs is expensive and time-consuming. Sophisticated AI solutions are more feasible where a company’s size, business model, financial resources and data volumes make machine learning a cost-effective solution.

In short

For the biggest companies, AI can be an extremely useful tool to assist with ABC compliance (just as it is for many financial institutions in relation to anti-money laundering compliance).