How artificial intelligence can help with credit risk decisions

1. What trends and challenges do credit risk professionals face?

In a world where information is everything, credit risk professionals struggle to manage information overload and recognize what’s important over noise. Monitoring the news is costly and time consuming, while identifying which credit related information sources to trust is challenging. Even when professionals do know which information they can rely on, analyzing that information to identify what is critical is a real challenge.

Fortunately artificial intelligence (AI), machine learning and automation are helping credit professionals make better decisions faster.

AI and machine learning in credit risk assessment can increase efficiency, reduce cost and increase speed. Trained systems reduce human error and reduce reliance on credit analysts’ manually created financial models. Enormous processing power allows vast amounts of data to be handled in a short time; cognitive computing helps to manage that data, both structured and unstructured, quickly.

Increasingly, algorithms are being trained to analyze and track company performance against the projections made by credit analysts. They can also track decisions credit professionals have made in the past, when analyzing vast amounts of data. Uncovering patterns in events and related decisions helps to identify potential red flags early, before they materialize.

The credit risk field is already harnessing the power of AI, machine learning and automation to create credit risk products. At Bureau van Dijk, we’ve analyzed the challenges faced by our clients to create client-led solutions that deliver game-changing advantages with machine learning. For example, helping clients process, standardize and analyze thousands of unstructured and heterogeneous financial statements from portfolios around the world. Our products will generate credit sentiment scores from negative global news and provide insights on credit quality, helping clients to identify early warnings regarding credit performance.

When it comes to negative credit events, perception is a leading indicator in the absence of hard data. Sentiment scores provide an early warning signal, supported by the underlying events, automatically integrated into our clients’ portfolios. These scores, powered by AI and automation, can be viewed company-by-company to help predict probability of bankruptcy and default.

These initiatives aim to help our clients make better decisions more quickly. For example, we’re integrating new technology into Credit Catalyst to take care of the spreading of thousands of financial statements. This means credit professionals can have standardized information at their fingertips fast, and at reduced cost. The greatest value of AI and machine-learning driven tools comes from integrating them into credit risk solutions in this way without manual interaction.

As unstructured data becomes more accessible and the sophistication of algorithms increases, credit-risk solution providers will continue to look to AI and machine learning to meet the evolving needs of credit risk professionals.

Bureau van Dijk is committed to exploring ways to further develop machine learning and AI-powered applications. These applications can help to identify early whether clients are exposed to potential risks and help reduce subjectivity in decision making. The ability to collect and analyze large amounts of information and unstructured data quickly and accurately is becoming mission-critical. This task has been performed manually by risk analysts, but we expect to see their role supported by AI and machine learning in the future, interpreting companies’ past performance and analysts’ decisions to help predict credit decisions.

Find out more about how AI and machine learning tools are helping with credit risk decisions.