Navigating AI Integration in Credit Risk Management: A Strategic Pivot

Choosing between leveraging established AI models and developing custom solutions using foundational AI models is a pivotal decision for any organisation aiming to integrate AI into their credit risk management processes. This decision is so important, it can shape how quickly and effectively AI is integrated into their target operating model.

At Inia AI, we faced this critical choice when developing our software application. We carefully considered the implications of both options, weighing factors such as time-to-market, cost, accuracy, and scalability. Ultimately, our decision would shape the future of our product and its ability to meet the evolving needs of our clients in the financial industry.

Defining the Future Landscape

In the realm of credit risk management, we envision a future where tasks currently performed by credit analysts will be executed collaboratively by both humans and AI. Just as banks employ credit analysts as permanent staff, they will likely seek to integrate AI as an integral, long-term component of their credit risk management capabilities, working alongside human analysts. This vision led us initially to the conclusion that development had to be conducted in-house so that the models can be licenced exclusively to our clients.

As we delved deeper into the subject, it became evident that developing an AI solution for credit risk management requires a multifaceted approach, involving various tools and models tailored to specific tasks. For instance, we recognized the need for multi-modal vision models to parse and prepare information for analysis, which could then be conducted by an advanced large language model acting as an AI assistant to the credit analyst.

This realisation sparked a debate over whether in-house development was necessary for both components.

The Crucial Step of Data Collection

Every risk manager will recognize the paramount importance of accurate data collection. Precise data serves as the foundation that empowers credit analysts to make well-informed decisions. For Inia AI, this crucial step underpins the integrity of our entire value chain, underscoring the vital role that meticulous data collection plays in achieving meaningful insights and driving successful outcomes.

However, what may be less clear is that extracting data from non-standardised documents, tables, and charts presents a formidable technological hurdle. Prior to the breakthroughs in generative AI, the accurate retrieval of information relevant to credit analysts from voluminous documents, was not feasible. Recent advancements in multi-modal AI technology have finally provided a solution to this longstanding challenge. However, only a handful of the most cutting-edge AI models currently available possess the ability to perform this task with the precision and efficiency that credit analysis requires.

Acknowledging the necessity for unparalleled accuracy and recognising the sophistication of the technology required, we made the strategic decision to employ established multi-modal models for our data collection processes. This choice was based on pragmatic considerations of cost, time, and reliability, as well as the alignment with our core competency in credit risk management.

Time & Cost: Developing a custom vision model from foundation models would be time-consuming and resource-intensive. Opting for established multi-modal models optimised our development process and resource allocation.
Accuracy: Rigorous testing revealed significant disparities among established models. Achieving consistently high precision, especially in the upper 90th percentile, would be uncertain with newly developed models.
Capabilities & Future-Proofing: Our vision is to expand services, capture increasingly granular data, and enhance processing speed while maintaining accuracy, all as part of an unparalleled user experience for credit analysts. Established models, extensively trained on diverse datasets and regularly updated, offer the necessary scalability and future-proofing, allowing us to focus on our core competencies in credit risk management.

We have also recognised the importance of prioritizing client data protection through anonymization, encryption, and collaboration with reputable organisations. This aspect was covered in our previous blog post titled “Safeguarding Client Data in AI-Driven Credit Risk Management”.

By choosing established multi-modal models for our data collection processes, we have struck a balance between leveraging the advantages of these models and ensuring data protection.

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

In the upcoming blog, we will explore the next level, focusing on the models used to process the structured data we’ve extracted. This stage delves deeper into information analysis, our approach and the rationale behind it. By examining the specific models and techniques employed, we aim to provide insight into the sophisticated process of converting raw data into actionable insights.

Conclusion

In conclusion, the integration of AI into credit risk management represents for us a thoughtful blend of strategic decision-making, technological innovation, and practical implementation. As we continue to evolve and refine our approach, the potential of AI to enhance credit risk management becomes increasingly tangible.

At Inia AI, our journey is not simply about adopting new technologies; it’s about pioneering a transformative approach to credit risk management that will shape a more intelligent, resilient, and future-ready financial industry.

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