?? Machine Learning in Credit Scoring with Datrics ??

Hello ,

Welcome (back) to The AI Treasury Insight Newsletter! ?

In this week's?edition, we discuss Credit Scoring with Machine learning.? And we will do so by taking the example of Datrics.ai, who has?created an end-to-end data science platform enabling the creation of customized AI apps and models for all business aspects, including AI in credit scoring.

Let's break down their solution and see how AI is used?already today. Please note that this article is not sponsored, we just found their solution interesting and worth talking about!?

Reminder of Machine Learning... And Credit Scoring

Credit Scoring: A Quick Refresher

Credit scoring is a statistical method that lenders use to quickly assess the creditworthiness of an individual or business applying for a loan. This score is based on the data available on their credit history, including their previous and current loans, repayment behavior, and any late payments or defaults. The financial health of a company also contributes to its credit scoring. Metrics such as?Profitability,?Liquidity,?Solvency,?Operational Efficiency,?Cash Flows and?Business Risk are thoroughly looked at.?

A higher score indicates a lower risk for the lender.

Machine Learning: A Quick Refresher

Machine Learning, a subset of Artificial Intelligence (AI), is a method of data analysis that automates analytical model building. In simpler terms, it is a system that can learn from data, identify patterns, and make decisions with minimal human intervention. Machine Learning algorithms use computational methods to “learn” information directly from data without relying on a predetermined equation as a model.

When AI meets Credit Scoring;

Unlike traditional methods that rely heavily on a borrower's past performance, AI-based scoring systems pay heed to real-time aspects of a borrower's financial status, such as current income level, employment prospects, and potential earning capability. As a result, even individuals with limited or no credit history can gain access to credit, provided they show promising financial potential. Consequently, this allows lenders to make more precise profit predictions based on intelligent AI models.

How can Rating Agencies and Banks leverage that?

If we take this a step further, we can clearly make the parallel with Corporate Rating!

Here is how AI can revolutionize corporate credit rating:

1. Advanced-Data Analysis with AIAI can efficiently analyze vast and diverse datasets, including traditional financial metrics and unstructured data like news articles, social media sentiments, industry trends, and macroeconomic indicators. For example:?It can process real-time news or market sentiments about a company, which might affect its creditworthiness but may not be captured in traditional financial statements.
2. Real-time Corporate Credit Assessment Traditional corporate credit ratings are often static and rely on periodic financial statements. AI allows for real-time updates, factoring in the most recent financial data, market conditions, and even operational changes. For example:?If a company just won a large contract or suffered an unexpected loss, AI can quickly adjust the credit rating to reflect these developments.
3. Predictive Modeling in Credit Rating AI uses machine learning to generate predictive models, which forecast a company's financial behavior based on historical data and trends. This predictive capability can anticipate potential credit risk shifts and defaults. For example:?AI could predict a downturn in a company's sector leading to increased credit risk, helping treasurers make more informed decisions.
4. Customized Risk Profiling with AI AI provides a more tailored risk assessment by taking into account each corporation's unique circumstances, industry, and behavior patterns. For example:?A?tech startup might have different risk factors compared to an established manufacturing firm, and AI can customize the credit rating process accordingly.
5. Efficiency Boost with AI AI application speeds up the credit rating process, leading to faster decision-making. For example:?instead of waiting weeks for manual credit assessment, AI can provide results in a matter of minutes or hours. This not only improves treasury operations but also enables corporate treasurers to manage their credit exposure more effectively.
6. AI for Better Risk Management AI's ability to predict future financial behavior, identify trends, and provide real-time credit assessments helps corporate treasurers manage their risk more effectively. For example:?by predicting a potential default, AI can help treasurers proactively adjust their credit portfolios and avoid high-risk exposures.

What are the downsides of using AI in Corporate Rating?

1. Data Privacy and Security Concerns: AI models require large amounts of data to function effectively. In the process of gathering this data, companies need to ensure they comply with data protection laws and maintain the confidentiality of sensitive information. Any breaches could lead to substantial financial and reputational damage.
2. Model Transparency and Explainability: AI models, particularly those based on deep learning, can be "black boxes," with internal workings that are hard to interpret. Regulators and stakeholders often demand transparency in credit rating decisions, which can be challenging with AI models.
3. Data Quality and Accuracy: The effectiveness of AI is highly dependent on the quality and accuracy of the data it's fed. Inaccurate or biased data can lead to flawed credit ratings, possibly leading to wrong financial decisions.
4. Regulatory Compliance: Regulatory standards for credit ratings are strict and often don't fully account for AI methodologies. Companies must therefore navigate a complex regulatory landscape when implementing AI in credit ratings.
5. Dependency and Over-reliance on AI: Over-reliance on AI models can lead to complacency in risk management. While AI can provide valuable insights, it's important for humans to maintain oversight, corroborate AI findings with independent analysis, and ensure decisions are sound.
6. Cost and Implementation Challenges: Implementing AI in an existing credit rating process can be complex and expensive. The cost includes not just the development and maintenance of the AI system, but also potential training for employees and the need for continuous adjustments as business needs evolve.

If you want to dive deeper into Treasury-related topics or have specific areas you'd like us to cover in future editions, we encourage you to visit our website.

There, you'll find a wealth of resources and can also reach out to us directly to share your feedback and suggestions.

Reach out to Hussam Ali or Guillaume Jouvencel and we will answer you!