When the second movie in the “Back to the Future” franchise was released in the year 1989, much of the world had no idea about computers. This did not prevent the makers of the movie from visualising a world 25 years in the future where a machine called Mr. Fusion could perform tasks using voice commands. Fast forward to the present day; not only have computers become omnipresent, but Artificial Intelligence (AI) is also a reality. Some statistics say that 77% of the devices we use daily incorporate AI in some form.

Artificial Intelligence is us trying to use technology to emulate human thinking, whether it be cognitive processes, speech, hearing, sight motion, etc. ChatGPT, Dall E, Amazon’s Alexa, Apple’s Siri, and Google Assistant are all examples of AI because so many different elements work together, such as speech, unstructured questioning, search, etc. 

AI has come a long way since its inception in the 1950s. What began as a niche field has become a ubiquitous technology that touches virtually every aspect of our lives. From virtual assistants to self-driving cars, AI has transformed how we live, work, and interact with the world around us.

AI has also emerged as a powerful force in the financial industry, including B2B Credit Risk Management. Risk management is a critical process for any business which is constantly exposed to credit risk from counterparties. AI is transforming this field as well by providing organisations with the ability to quickly identify and assess potential risks, monitor counterparties in real-time, and automate various parts of the credit workflow, including critical activities such as credit limit setting. 

Using machine learning to analyse vast amounts of data, AI can identify patterns and predict credit risks more accurately than traditional methods. This has resulted in more efficient credit practices and better outcomes for creditors.

Role of AI in B2B Credit Risk Management:

Risk Identification

Risk Identification is probably the most challenging part of Credit Risk Management because the question “How do you identify and predict the risk of credit default by counterparties?” is not an easy one to answer. This prediction is going to be premised on the historical information that you possess and on changes in environmental variables.

AI-based platforms can identify potential credit risks by analysing large volumes of data about counterparties from internal and external sources, including financial statements, payment track records, transaction history, and more. For example, AI algorithms can rapidly analyse the financial statements of counterparties at scale to identify any financial red flags, such as deterioration in key performance variables, financial ratios, etc. They can automatically detect and flag trends pertaining to delayed regulatory filings, employee payments, or deterioration in the working capital position of counterparties. This information can then be used to assess the financial stability of counterparties and determine if there is a risk of default.

In addition to financial data, AI can analyse non-financial data, such as court records, media articles, videos, social media, podcasts, etc., to identify potential counterparty risks. For example, you can deploy AI tools to monitor news and social media posts related to your counterparties to identify any developments that can have a negative impact on their risk profile. Counterparties whose risk profile has deteriorated can be flagged for reviewing their credit limits.

Credit Risk Model Development for assessing creditworthiness

AI-based Credit Risk Management Platforms not only identify or predict risk but also help in assessing the creditworthiness of counterparties. Traditionally, credit risk assessment involves manually analysing income statements and historical payment patterns and making judgments about the counterparty’s credit risk. This manual process is time-consuming, error-prone, costly, and cannot be done at scale. AI-based Credit Risk Models can be developed and deployed using multiple variables from structured and unstructured data sources. The Credit Risk Model is customised to include any additional risk variables that the trade creditor’s risk team may suggest; the weightage of each risk variable in the Credit Risk Model is calibrated to meet the risk appetite of the specific trade creditor.

Once the Credit Risk Model is deployed, it automatically assigns a Credit Score to all counterparties. In addition, many companies deploy Credit Limit Setting Models that automatically recommend credit limits for each counterparty depending on its Credit Score. This helps to limit human intervention in the credit limit-setting process, thus reducing the scope for error or corruption.

Once the Credit Risk and Limit Setting Models are tested and deployed, they become essential elements of a company’s credit workflow system. Any exception to the recommended Credit Limit would need higher-level approvals, facilitating better control by preventing the flow of credit to risky counterparties.  

Real-time monitoring

Real-time monitoring of counterparty credit risk is essential in today’s financial landscape, and AI-based platforms are making it easier than ever before. These platforms monitor counterparties in near-real-time to quickly identify and respond to potential risks. For example, an AI algorithm can monitor a counterparty’s financial transactions to identify any unusual activity, such as a sudden increase in transaction volume or a change in transaction patterns. Early Warning Systems (EWS) used by banks, financial institutions, and many large companies are excellent examples of real-time monitoring using AI. Early warning systems also monitor external data sources (news, social media, regulatory filings, credit bureau data, etc.) about counterparties to identify potential risks. Credit Risk Models automatically generate revised Credit Scores based on the new information generated by the Early Warning System. This allows a proactive reduction in credit risk exposure to counterparties whose credit profile has deteriorated.

Fraud detection

AI is transforming how we think about fraud detection and prevention and becoming an indispensable tool for managing counterparty risk in the digital age. In fact, Juniper Research estimates that the worldwide spending on AI-enabled financial fraud detection platforms will exceed $10 billion by 2027, increasing from just US$ 6.5 billion in 2022.

An exciting future

Fuelled by the growth in computing power, AI is already transforming Credit Risk Management and will do so more rapidly in the future. As AI advances, we can expect to see even more innovative risk management platforms emerge that will be more effective in identifying, predicting, assessing, monitoring, and mitigating credit risks at scale on a real-time basis. AI-powered tools will become more ubiquitous, and there will be even more significant benefits, including improved predictive accuracy and lower costs. The induction of AI platforms into the arsenal of companies augurs well for Credit Risk Management in the coming days.