IDENTIFYING AND MANAGING RISK WITH MACHINE LEARNING AND NATURAL LANGUAGE PROCESSING
Oct 24, 2019
In recent years, growing regulatory burdens and increased complexity have made calculating and managing risk harder for companies. Compliance issues have become more complicated, and the business environment has become more volatile, leaving Enterprise Risk Management more expensive and time consuming for companies. However, in that same time, great strides have been made in the field of Artificial Intelligence (AI). AI techniques, such as natural language processing (NLP) and machine learning (ML), can help organizations deal with increasing volumes of data, while providing more accurate risk assessments and monitoring for non-compliance.
NLP is one of the most used tools for risk management. In a Chartis Research survey examining the adoption of AI methods by risk and compliance professionals, 37% of respondents said that NLP was either a “core component” or in “extensive use” at their organization. NLP unlocks unstructured data, by studying patterns in free-form data. Most organizations deal with a vast amount of free-form data, which doesn’t follow strict, easily analyzed formats, such as letters or legal documents. NLP can help surface valuable insight from these documents, without the need for people to physically read every one.NLP is an especially useful method for regulatory issues. In one case study, hundreds of pages of bank legal documents were analyzed to create regulatory rules for the bank’s compliance process. By using NLP, organizations can save time and catch compliance issues faster.
ML is the second most commonly used AI method, after NLP, for risk management and it is rapidly becoming more popular. It’s a technique that uses large sets of data to train computer systems to identify patterns and help make decisions without the need for much human intervention. ML has a wide variety of use cases. It can be used in conjunction with NLP to turn unstructured data into structured data, while algorithms trained using ML can estimate predicted risk for a variety of scenarios, such as predicting the insider risk for a company or spotting fraud for an insurance agency. By analyzing huge amounts of data, ML can spot patterns and offer suggestions based on those patterns.
As AI becomes a more central part of Enterprise Risk Management, adopting tools such as NLP and ML helps organizations stay ahead of risks, make more informed decisions, and make compliance with regulation easier.
If you are interested in learning what ML and NLP can do for your organization, contact us at email@example.com.