Beyond Data Collection: A Deep Dive into AI-Driven Credit Risk Analysis

In our previous article, “Navigating AI Integration in Credit Risk Management: A Strategic Pivot,” we explored the choice between using established AI models and developing custom solutions with foundational AI models for credit risk management. We highlighted the importance of accurate data collection and our strategic decision to use established multi-modal models, considering factors such as time, cost, and accuracy.

Today’s article moves to the next phase: data analysis. Here we argue that CROs ought to consider furnishing credit analysts with AI tools that allow them the flexibility to construct their analysis step by step, rather than opting for fully automated solutions.

The Semi-Automatic Advantage

Consider the analogy of coffee machines. At one end of the spectrum, there is the Nespresso, which delivers coffee at the push of a button with minimal user input. At the other, manual machines demand a hands-on approach and a deep understanding of the brewing process. Positioned between these extremes are semi-automatic machines like the Profitec. These allow users to adjust critical brewing variables such as water temperature, pressure, and shot duration. They also feature automated elements like a rotary pump, which manages boiler pressure and facilitates pre-infusion, enhancing the coffee’s flavour extraction. This blend of flexibility and support makes semi-automatic machines ideal for both experienced baristas who seek precise control and novices who appreciate some assistance.

At Inia AI, we apply a similar semi-automatic approach when advising our clients on implementing AI for credit review tasks. The software we are developing empowers users by allowing them control over their credit analysis, enabling precise customization of output. Our goal is to enable credit analysts to conduct faster, more thorough analyses with consistently accurate and up-to-date information, not to replace them in the credit review process.

Beyond Data: Enhanced Analysis, Rating, Portfolio Monitoring

Building upon the semi-automatic approach, you can leverage AI models to enhance various aspects of the credit analysis process beyond data extraction. Here are three ways to use them effectively:

Credit Rating Analysis: The models can ingest a credit rating scorecard, comparing it against counterparty information, and generating a category score. While doing so, they can provide the rationale in the output, enabling analysts either override or accept the results.
Enhanced Scenario Analysis: Transformer models within large language models predict sequential words based on probabilistic reasoning. By using tailored prompts, credit analysts can swiftly enhance their analysis of counterparty credit risk. For example, they can run iterations to uncover potentially overlooked aspects in credit evaluations, leading to more comprehensive and accurate assessments.
Advanced Filtering for Portfolio Monitoring: The technology can also be used to automate the periodic review of client reports with great precision. AI can quickly identify and flag situations that require immediate attention or action. This targeted approach effectively directs the analyst’s focus to the most critical issues, enhancing both efficiency and risk management capabilities.

By integrating these capabilities, you can enable faster and better credit analysis, beyond just data collection.

Faster Drafting, Clearer Communication

Finally, employing AI in credit risk management not only speeds up the drafting process but also leads to clearer outputs. Credit reports often remain unread due to their length, complexity, or technical nature. By integrating AI into the credit review process, the drafting becomes simpler for analysts, producing outputs that busy individuals can easily comprehend.

Don’t Fear the Clone: How the Right AI Architecture Enhances Rather Than Homogenises Credit Analysis

While AI models have the potential to enhance credit analysis and streamline the drafting process, some may worry that their widespread adoption could lead to a homogenisation of credit reports across financial institutions. However, this is not the case.

By employing the right architecture, you can ensure that the models do not train on the data they process. This way, the insights generated for one credit report do not influence the output for another report, within and across different institutions. Each analysis remains unique, specifically tailored to the context and requirements of each financial institution and its credit analysts.

Conclusion

In conclusion, AI-driven credit risk analysis tools empower analysts to conduct faster, more comprehensive analyses by leveraging diverse data sources, enhancing scenario analysis, and streamlining communication. A semi-automatic approach balances the benefits of AI with the need for analyst control. As the financial industry evolves, adaptable AI platforms will play a crucial role in supporting well-informed decision-making and effective credit risk management.

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