AI transforms credit rating agencies, enhancing accuracy and efficiency

AI and machine learning tools can analyse a variety of information like financial statements, market trends and price fluctuations. They continuously learn and adapt

Understanding complex financial numbers to assess the financial health of organisations is not an easy task. Solutions based on artificial intelligence (AI) are now helping rating agencies predict the future with better results.

 

“CareEdge Ratings has been at the forefront of integrating AI and ML (machine learning) into its credit rating processes. By leveraging AI-driven predictive analytics, CareEdge Ratings enhances its ability to assess credit risk with greater accuracy and efficiency,” says Mehul Pandya, managing director and group chief executive officer of the agency.

 

“The agency employs advanced AI models to analyse a wide range of data sources, including financial statements, market trends, and alternative data, to provide comprehensive and reliable credit ratings.” The agency says that it uses tools for natural language processing – the ability of a computer programme to understand human language as it's spoken and written – to automate the extraction and analysis of financial disclosures, filings, and news articles. The capability improves the agency's ability to assess sentiment and identify potential risks, providing more accurate and timely insights about credit risk. 

 

Pandya explained how AI enabled CareEdge to improve its assessment of the operating margin of a tyre manufacturer. The agency wanted to enhance the quality of its credit ratings by leveraging AI and ML to predict the impact of commodity price fluctuations, particularly rubber, on the operating margins of the company. CareEdge gathered extensive data on global economic indicators, rubber prices, and the manufacturer’s financial performance. This data was integrated into a centralised system for comprehensive analysis.

 

Key factors influencing rubber prices were identified using ML algorithms. These included supply chain disruptions, geopolitical events, and demand changes in the automotive industry. Then advanced ML techniques, such as regression analysis and time-series forecasting, were employed to predict future rubber prices. These models were continuously refined with new data to enhance their accuracy.

 

A simulation model was developed to assess how predicted rubber price changes would affect the tyre manufacturer's operating margins. Various scenarios, such as price spikes or supply shortages, were analysed to understand potential impacts. These steps helped rating analysts and a committee to ascertain the maximum impact on operating margins and cash flow for tyre manufacturers. The result was improved decision-making for internal assessments and support in the quality of ratings. AI and ML models were later integrated into a real-time monitoring system to provide ongoing updates and alerts about significant changes in macroeconomic conditions or commodity prices. 

 

Pandya says that credit rating agencies rely heavily on financial statements, historical data and expert judgment to assess credit risk. While these methods provide valuable insights, they are often constrained by the static nature of the data and human bias. The integration of AI and ML tools has revolutionised the rating process by enabling the analysis of vast and diverse datasets in real time, and therefore enhancing the accuracy and timeliness of credit ratings. According to Pandya, these models can continuously learn and adapt, improving their predictive accuracy over time. For instance, AI can identify patterns and correlations that may not be evident to human analysts.

 

Use of GenAI, a type of AI that can create new content, such as text, images, videos, or music, for credit rating is being adopted globally. A McKinsey Survey of chief credit risk officers found rising interest in AI. According to the survey, 20 per cent of such respondents had already implemented at least one GenAI use case in their organisations, and a further 60 percent expect to do so within a year. The survey said that even the most cautious of these executives believe that GenAI will be part of their companies’ credit risk work in two years.

 

The survey emphasised that due precautions must be taken to ensure that valid data is used and the algorithms must be designed fairly to minimise biases.