3 Practical Uses for AI in Risk Management

Banks, insurance companies, asset managers and other industry players need to rethink how they approach financial risk management.

Every year, financial crime becomes more sophisticated, new malware emerges and fraud losses rise. Top that problem up with continuously evolving regulations and hefty non-compliance penalties, and financial institutions are facing an increasingly complex risk landscape.

To compete in the new environment, banks, insurance companies, asset managers and other industry players need to rethink how they approach financial risk management. That’s where artificial intelligence can lend a helping hand. With advanced analytical capabilities, AI can augment human-led risk management activities to drive better outcomes much faster. It is estimated that through better decision-making and improved risk management, AI could generate more than $250 billion in the banking industry.

Insurance companies, banks and fintech startups alike are starting to integrate AI-driven analytics into their financial risk management software. Here’s a roundup of three practical use cases to give you the idea of AI potential.

Accurate fraud detection

The complexity and visibility into multi-channel fraud prevention is a major challenge for financial institutions. Scammers are getting more sophisticated and quickly find creative ways to steal from banks and their customers. Each year, fraud costs over $5 trillion, a sum more than 80% greater than the U.K.’s entire GDP.

To stay agile and quickly respond to threats, banks are augmenting their fraud detection toolkit with machine learning capabilities. The idea behind ML-driven fraud analytics is that fraudulent transactions have telltale signs that algorithms can uncover much more effectively than rule-based monitoring systems. By processing customer, transactional and even geospatial data, they can even spot patterns that seem unrelated and simply go unnoticed by human data analytics.

As a rule, ML algorithms leverage supervised or unsupervised learning techniques for fraud detection. The difference between these two types is that supervised learning-based algorithms heavily rely on explicit labels, meaning that machines need to be repeatedly trained on what a legitimate versus fraudulent transaction is. Unsupervised learning models, in contrast, do not need prior labeling to recognize abnormal activity, so they can continuously update their datasets and detect even previously unknown fraud and abuses.

Credit risk prediction

In simple terms, credit risk refers to the risk of financial loss when a borrower fails to meet financial commitments. And as these non-performing assets continue to grow, it has become imperative for banks to find better and more robust mechanisms to manage default risks.

Advanced ML-driven analytics can do just that. By analyzing a vast amount of financial and non-financial data, trained machine learning algorithms can model credit risk and predict default with a much higher degree of accuracy than traditional methods.

See also: Claims and Effective Risk Management

There is no shortage in up-and-coming startups that work on AI-powered credit scoring solutions to help the financial industry fight high delinquency rates. One such example is British startup SPIN Analytics, which has developed its RiskRobot to optimize credit decisions. The solution leverages advanced analytics to forecast credit behavior and credit losses of individual customers and entire credit portfolios.

Effective regulatory compliance

Over the years, the number of rules and regulations that banks and financial organizations need to adhere to has multiplied — EMIR, SFTR, MiFID II/MiFIR, MMF, GDPR. With this raft of regulatory bodies, updates are issued every seven minutes. And, with hefty fines and penalties, non-compliance is not an option.

Handling the overwhelming volume of regulatory change is no easy feat. But recent advancements in natural language processing (NLP), an AI subfield, are bringing us closer to effectively solving the compliance puzzle. With the ability to understand the human language, NLP-based solutions can scan and analyze millions of lines in regulatory content, including legal documents, commentary, guidance, legal cases, to spot applicable requirements much faster — that’s what London-based Waymark offers its corporate clients.

Another prominent regtech player is IBM, which offers its cognitive computing platform Watson to drive down regulatory compliance costs. Trained with the help of Promontory, Watson identifies and tags obligations, guides and controls to facilitate regulatory change management.

The bottom line

The financial risk landscape is changing fast. Staying on top of emerging fraud threats, credit risk and rapid regulatory changes requires a superhuman effort.

AI can augment human intelligence with rich analytics and pattern prediction capabilities to drive fraud and credit risk detection with higher accuracy and at a larger scale. In the regtech space, AI-fueled analytics solutions can significantly accelerate compliance procedures while reducing the costs.

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