Three ways AI is improving risk management

Risk management has always had an issue with how to process large amounts of data quickly and effectively, but artificial intelligence (AI) could soon have the answer. Combining this with machine learning – and a dash of human intelligence – is already giving organisations greater insight into the risks they face, and helping those responsible make better and more informed decisions.

Here, I’ve distilled three key ways in which AI is helping risk managers, taken from a private meeting with a number of our network’s risk leaders (the full write up of which can be found by members in the Intelligence platform).

1. Doing the grunt work

Scenario planning tools that use AI and machine learning can process large swathes of data and information to create thousands of cause-effect relationships in a matter of hours, something that would take months to complete if being carried out manually.

This information can then use plausibility, rather than probability, to create a number of plausible scenarios for the near future. These can then be used by risk managers to help inform their decisions regarding what actions to take in order to take advantage of these scenarios, or prevent them from happening.

2. Helping to understand the unknown

While traditional risk management tools are very effective at detecting common risks, or white swans, they cannot as easily identify those less common risks. Unknown unknowns, or black swans, may be a few years off being easily identifiable, but AI is already working well in detecting so-called grey swans.

By using natural language processing, which is a technique that helps you to take the statistical patterns in language, risk managers are now able to create detailed language models.

These language models can then be used to extract cause-effect relationships within data, identify entities, and create summaries from very large data sets.

3. Delivering deeper insight

AI is also making use of publicly available datasets to provide greater insights into what is going on in the world around us, as well as creating early warning signals.

This can then be fed into AI-based scenario planning tools to ensure organisations are always as up-to-date as they can be on the risks they are facing.

Sentiment analysis of public opinion is also possible using AI, and machine learning can then be used to determine what this means for particular future scenarios.

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