Article
Written by Craig Evans
Posted on 25/04/2019

Financial Crime - harnessing the power of AI, ML & CI

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What are AI, ML & CI?


Artificial Intelligence (AI) is the field of computer science focused on the development of computers to carry out activities typically executed by humans, specifically activities requiring humans to take decisions and rely on their intelligence

Machine Learning (ML) is a sub-field of AI where computers are able to learn when exposed to new data without being explicitly programed. Common examples of ML include recommendation engines, facial recognition and self-driving cars

Collective Intelligence (CI) is the combination of AI and subject matter expertise applied to detect and reapply insights across an entire domain of information to provide constant, ongoing improvement in results for all contributors

How can AI, ML & CI help in preventing Financial Crime?

All three have powerful benefits when it comes to prevention of Financial Crime. AI is currently being used to understand patterns and trends that can form very early and improved detection of identification for example. CI is also adding to the identification of the patterns that are being picked up. Whilst MI can help in much fewer False Positives being achieved. Combining all 3 helps to vastly reduce the time being spend on manual investigations.

The importance of Collective Intelligence (CI)

For more than 25 years, we have gathered and maintained a database of suspect corporate fraud intelligence. Collaborative approaches from industries such as IT, Telecoms, Petrochemicals, Construction and Leisure, have help us to remove over £180m of credit facility, where other credit information agencies have provided substantial credit limits.

So what is Collective Intelligence?

Collective intelligence (CI) is shared or group intelligence that emerges from the collaboration, collective efforts, and competition of many individuals and appears in consensus decision making. The term appears in sociobiology, political science and in context of mass peer review and crowdsourcing applications. It may involve consensus, social capital and formalisms such as voting systems, social media and other means of quantifying mass activity. In today’s workplace, where artificial intelligence is becoming increasingly prominent, a new type of collective intelligence has emerged – one where interconnected groups of people and computers work alongside each other to produce desirable results.

 

How Collective Intelligence Augments ML

  • Leverages the expertise of all participants
  • Derives insights from ongoing operation
  • Applies insights in a way that improves future results