AI credit risk modeling: Streamlining credit risk operations

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  Quantumrun Foresight
• February 27, 2023

The problem of modeling credit risk has plagued banks for decades. Machine learning and artificial intelligence (ML/AI) systems offer new methods to analyze the data involved and provide more dynamic, more accurate models.

AI credit risk modeling context

Credit risk refers to the risk that a borrower will default on their loan payments, resulting in a loss of cash flows for the lender. To assess and manage this risk, lenders must estimate factors such as the probability of default (PD), the exposure at default (EAD), and the loss-given default (LGD). The Basel II guidelines, published in 2004 and implemented in 2008, provide regulations for managing credit risk in the banking industry. Under the First Pillar of Basel II, credit risk can be calculated using a standardized, an internal foundation rating-based, or an advanced internal ratings-based approach.

The use of data analytics and AI/ML has become increasingly prevalent in credit risk modeling. Traditional approaches, such as statistical methods and credit scores, have been supplemented by more advanced techniques that can better handle non-linear relationships and identify latent features in the data. Consumer lending, demographic, financial, employment, and behavioral data can all be incorporated into models to improve their predictive capability. In business lending, where there is no standard credit score, lenders may use business profitability metrics to assess creditworthiness. Machine learning methods can also be used for dimensionality reduction to build more accurate models.

Disruptive impact

With the implementation of AI credit risk modeling, consumer and business lending can employ more accurate and dynamic lending models. These models give lenders a better assessment of their borrowers and allow for a healthier lending market. This strategy is beneficial for business lenders, as smaller enterprises have no benchmark to judge their creditworthiness the same way standard credit scores function for consumers.

One potential application of AI in credit risk modeling is using natural language processing (NLP) to analyze unstructured data, such as company reports and news articles, to extract relevant information and gain a deeper understanding of a borrower's financial situation. Another potential use is the implementation of explainable AI (XAI), which can provide insight into the decision-making process of a model and improve transparency and accountability. However, using AI in credit risk modeling also raises ethical concerns, such as potential bias in the data used to train models and the need for responsible and explainable decision-making.

An example of a company exploring the use of AI in credit risk is Spin Analytics. The startup uses AI to automatically write credit risk modeling regulation reports for financial institutions. The company's platform, RiskRobot, helps banks aggregate, merge, and cleanse data before processing it to ensure compliance with regulations in different regions, such as the US and Europe. It also writes detailed reports for regulators to ensure accuracy. Writing these reports typically takes 6-9 months, but Spin Analytics claims it can reduce that time to less than two weeks. 

Applications of AI credit risk modeling

Some applications of AI credit risk modeling may include:

• Banks using AI in credit risk modeling to significantly reduce the time and effort required to produce detailed reports, allowing financial institutions to launch new products more quickly and at a lower cost.


• AI-powered systems being employed to analyze large amounts of data more quickly and accurately than humans, potentially leading to more accurate risk assessments.


• More ‘unbanked’ or ‘underbanked’ people and businesses in the developing world gaining access to financial services as these novel credit risk modeling tools can be applied to discern and apply basic credit scores to this underserved market.


• Human analysts being trained to use AI-based tools to reduce the risk of errors.


• Artificial intelligence systems being used to detect patterns of fraudulent activity, helping financial institutions reduce the risk of fraudulent loans or credit applications.


• Machine learning algorithms being trained on historical data to make predictions about future risk, allowing financial institutions to proactively manage potential risk exposures.

Questions to comment on

• What metric do you believe businesses should use to benchmark their creditworthiness?


• How do you envision AI changing the role of human credit risk analysts in the future?