This Is How Generative AI Is Redefining Financial Assessment

Generative artificial intelligence (AI) is reshaping the financial landscape, emerging as a powerful tool in redefining how credit risk is assessed. By moving beyond traditional linear models, generative AI can analyze vast amounts of unstructured data — including payment histories and digital behavior — to capture subtle signals that enhance predictive accuracy. The technology not only improves the quality and depth of risk assessment but also streamlines manual, time-consuming processes, especially in handling complex corporate documentation. As a result, financial institutions can operate with greater speed and efficiency in an increasingly competitive environment.

This video highlights how banks have long relied on credit scoring models that work with linear relationships. However, generative AI’s ability to incorporate unstructured data significantly increases the predictive power of these models.

Experts Tiago Rodrigues de Freitas, a partner and head of data and analytics Iberia at Oliver Wyman, and Ignasi Barri, global head of AI and data at GFT Technologies, emphasize that beyond improving quality and discrimination, generative AI accelerates manual tasks, reduces human error and saves time. They also point out challenges, including the rapid pace of technological change causing client hesitation, the need for explainable AI models to build trust, and the importance of upskilling the workforce to adopt these solutions effectively.

As a practical example of generative AI’s application, the experts briefly mention the Credit Risk Assistant developed by GFT and Oliver Wyman. This tool helps credit analysts efficiently process diverse data sources across multiple languages.

Looking ahead, they express strong confidence that AI will evolve from merely supporting credit processes to becoming the central enabler of their re-engineering — signaling an inevitable and exciting transformation in the financial sector.

Ignasi Barri: Tiago, we're hearing a lot lately, around generative AI, but in particular generative AI for credit worthiness assessment. Do you think that this is a potential way to improve that process?

Tiago Rodrigues: Absolutely, banks have relied for decades on credit scoring and rating models that, while reasonably good at discriminating clients, operate through very linear relationships between variables and outputs; what we’re seeing with generative AI is the ability to incorporate unstructured data — such as payment history, transactions, and behavior on Amazon and e-commerce platforms — which can provide subtle signals that generative AI can capture, thereby increasing the predictive power of credit models, improving quality and discrimination, while also accelerating processes related to manual work, especially in the corporate world where extensive documentation must be reviewed.

Ignasi: Benefits always come with certain barriers, so what do you think are the major challenges that banks and financial institutions face when trying to adopt these technologies in their processes?

Tiago: I think the first challenge is the speed of change, which is so rapid that it makes clients defensive and hesitant to fully embrace new technologies, waiting to see a final product even though the process is continuously evolving; the second challenge involves explainability — understanding what the technology delivers — which has improved significantly over the past year through techniques we’ve developed and applied in solutions like the credit risk assistant to help clients interpret outputs, though some resistance remains; and the third challenge is the level of adoption, as the workforce needs time to acquire new skills to understand and work with these solutions, a hurdle that persists today but is likely to diminish over the next two to three years.

Ignasi: To increase adoption, demonstrating prior experience is essential; that’s why GFT and Oliver Wyman created the generative AI-based credit risk assistant — can you tell us more about it?

Tiago: I can. This credit risk assistant is a fantastic example of how generative AI can be rapidly applied to help credit analysts process large volumes of information, such as annual reports, technical statements, and transactional data, extracting valuable signals and insights for client assessment; tasks that were previously done manually, prone to human error, and time-consuming are now streamlined, delivering appreciated results, and notably, despite initial concerns about language barriers, it has been successfully implemented in French, Arabic, Japanese, Spanish, and Portuguese without any issues.

Ignasi: That’s wonderful. Looking ahead, we will see the credit sector completely evolve and transform through the use of AI?

Tiago: I believe we are only seeing the tip of the iceberg, as current use cases like the one I described involve implementing AI within existing processes, but moving forward, these processes will continue to evolve and be reengineered so that generative AI is not just a support tool but the central enabler, and based on the evidence I’ve seen, I am convinced this transformation is inevitable.

Ignasi: So, exciting times ahead, right?

Tiago: Absolutely.

Ignasi: Thank you, Tiago, for sharing these valuable insights and for participating in our quarter.

Tiago: No, thank you for inviting me. As you can see, I am a passionate believer in this topic, and I truly think it will change our future.

Ignasi: Thank you again.

Generative artificial intelligence (AI) is reshaping the financial landscape, emerging as a powerful tool in redefining how credit risk is assessed. By moving beyond traditional linear models, generative AI can analyze vast amounts of unstructured data — including payment histories and digital behavior — to capture subtle signals that enhance predictive accuracy. The technology not only improves the quality and depth of risk assessment but also streamlines manual, time-consuming processes, especially in handling complex corporate documentation. As a result, financial institutions can operate with greater speed and efficiency in an increasingly competitive environment.

This video highlights how banks have long relied on credit scoring models that work with linear relationships. However, generative AI’s ability to incorporate unstructured data significantly increases the predictive power of these models.

Experts Tiago Rodrigues de Freitas, a partner and head of data and analytics Iberia at Oliver Wyman, and Ignasi Barri, global head of AI and data at GFT Technologies, emphasize that beyond improving quality and discrimination, generative AI accelerates manual tasks, reduces human error and saves time. They also point out challenges, including the rapid pace of technological change causing client hesitation, the need for explainable AI models to build trust, and the importance of upskilling the workforce to adopt these solutions effectively.

As a practical example of generative AI’s application, the experts briefly mention the Credit Risk Assistant developed by GFT and Oliver Wyman. This tool helps credit analysts efficiently process diverse data sources across multiple languages.

Looking ahead, they express strong confidence that AI will evolve from merely supporting credit processes to becoming the central enabler of their re-engineering — signaling an inevitable and exciting transformation in the financial sector.

Ignasi Barri: Tiago, we're hearing a lot lately, around generative AI, but in particular generative AI for credit worthiness assessment. Do you think that this is a potential way to improve that process?

Tiago Rodrigues: Absolutely, banks have relied for decades on credit scoring and rating models that, while reasonably good at discriminating clients, operate through very linear relationships between variables and outputs; what we’re seeing with generative AI is the ability to incorporate unstructured data — such as payment history, transactions, and behavior on Amazon and e-commerce platforms — which can provide subtle signals that generative AI can capture, thereby increasing the predictive power of credit models, improving quality and discrimination, while also accelerating processes related to manual work, especially in the corporate world where extensive documentation must be reviewed.

Ignasi: Benefits always come with certain barriers, so what do you think are the major challenges that banks and financial institutions face when trying to adopt these technologies in their processes?

Tiago: I think the first challenge is the speed of change, which is so rapid that it makes clients defensive and hesitant to fully embrace new technologies, waiting to see a final product even though the process is continuously evolving; the second challenge involves explainability — understanding what the technology delivers — which has improved significantly over the past year through techniques we’ve developed and applied in solutions like the credit risk assistant to help clients interpret outputs, though some resistance remains; and the third challenge is the level of adoption, as the workforce needs time to acquire new skills to understand and work with these solutions, a hurdle that persists today but is likely to diminish over the next two to three years.

Ignasi: To increase adoption, demonstrating prior experience is essential; that’s why GFT and Oliver Wyman created the generative AI-based credit risk assistant — can you tell us more about it?

Tiago: I can. This credit risk assistant is a fantastic example of how generative AI can be rapidly applied to help credit analysts process large volumes of information, such as annual reports, technical statements, and transactional data, extracting valuable signals and insights for client assessment; tasks that were previously done manually, prone to human error, and time-consuming are now streamlined, delivering appreciated results, and notably, despite initial concerns about language barriers, it has been successfully implemented in French, Arabic, Japanese, Spanish, and Portuguese without any issues.

Ignasi: That’s wonderful. Looking ahead, we will see the credit sector completely evolve and transform through the use of AI?

Tiago: I believe we are only seeing the tip of the iceberg, as current use cases like the one I described involve implementing AI within existing processes, but moving forward, these processes will continue to evolve and be reengineered so that generative AI is not just a support tool but the central enabler, and based on the evidence I’ve seen, I am convinced this transformation is inevitable.

Ignasi: So, exciting times ahead, right?

Tiago: Absolutely.

Ignasi: Thank you, Tiago, for sharing these valuable insights and for participating in our quarter.

Tiago: No, thank you for inviting me. As you can see, I am a passionate believer in this topic, and I truly think it will change our future.

Ignasi: Thank you again.