Artificial Intelligence (AI) has emerged as one of the most transformative technologies of our time, reshaping industries and creating new possibilities for businesses.
In the financial services sector, AI and Machine Learning (ML) are revolutionising credit risk management, offering lenders more accurate insights, greater efficiencies and the potential to boost profitability.
We are seeing businesses putting additional focus on leveraging AI and ML, recognising that these technologies can give them a competitive edge in the rapidly evolving financial landscape, with those yet to embark on their AI journey seeking support to help them unlock its potential.
Increasing Efficiency Through AI
One area of particular interest is the use of generative AI, which is providing significant efficiency savings for lenders. This technology is being applied across a wide range of tasks, from optimising coding for credit risk analysts using AI-powered tools like CoPilot, to underwriters employing bespoke AI solutions for speeding up document reviews.
AI tools can quickly extract key information from complex documents, allowing lenders to process more applications in less time. The result is increased productivity, faster decision-making and reduced operational costs – a win-win for lenders looking to enhance their performance in a competitive marketplace.
ML Modelling in Credit Risk
Machine learning models, meanwhile, are making a significant impact by enabling greater model accuracy in assessing various credit risk outcomes, such as loan defaults, fraud detection, and pricing sensitivities.
Unlike traditional statistical models, ML algorithms can handle far more complex and larger data sets, allowing for the incorporation of alternative data sources. For example, Open Banking data can be combined with traditional Credit Bureau data, providing a more holistic view of an individual’s financial behaviour and circumstances.
These sophisticated models have also contributed to substantial model uplift. Lenders using ML models are better positioned to increase profitability, whether by lending more confidently to riskier borrowers or by reducing losses while maintaining lending volumes.
However, despite the clear advantages AI and ML offer, there are notable challenges – especially for smaller lenders.
One of the most significant obstacles is the skills gap within these organisations. Many lenders lack the advanced statistical modelling expertise required to build effective ML models, as well as the technological capabilities to securely deploy them in real-time. In some cases, lenders bring in external data scientists, but these experts may lack the industry-specific knowledge required to build and fine-tune models for credit risk applications.
Broadstone is uniquely positioned to bridge this gap. With over 20 years of credit risk modelling experience, Broadstone has developed and validated multiple bespoke ML models across various asset types. We can also offer support for model deployment and can provide a real-time hosted model solution, ensuring seamless integration into lenders’ workflows.
A Regulatory Focus
Regulatory compliance presents another major challenge. ML models, by nature, are often more complex than traditional models, such as Logistic Regression. While the underlying principles of robust governance, explainability and monitoring remain the same, applying these principles to ML models can be more difficult due to their intricacies.
Broadstone has extensive experience in addressing these challenges. Its comprehensive model monitoring service is designed to help lenders understand how their models are performing and identify any deviations from expected outcomes. This service can be deployed on existing models, offering clients a clear view of model performance and enabling proactive interventions when necessary.
Regulatory bodies such as the Bank of England (including the Prudential Regulation Authority) and the Financial Conduct Authority are also playing a key role in shaping the future of AI and ML in the financial sector. Both institutions have recognised the potential for these technologies to enhance efficiency across the industry, and they have been actively working to provide guidance to firms on how best to implement them.
As part of this effort, regulators are stressing the importance of maintaining strong governance principles, ensuring transparency, and applying rigorous monitoring processes to AI and ML models. For lenders, this heightened regulatory focus underscores the importance of adopting these technologies responsibly, with a strong emphasis on compliance and ethical use.
What’s Next?
For lenders that have yet to fully embrace AI, there has never been a better time to act. The benefits of AI and ML, from enhanced efficiency to increased profitability, are too great to ignore. Those that fail to keep up risk falling behind their competitors, while those that invest in these technologies now will be well-positioned to capitalise on their full potential.
As the financial landscape continues to evolve, lenders should seize the opportunity to harness AI’s capabilities. With the right tools and expertise, they can transform their credit risk management processes, improve decision-making, and drive long-term growth.
Broadstone’s Credit Risk team is here to support lenders on this journey, helping them navigate the complexities of AI and ML to achieve their business goals. If you’re ready to take the next step, get in touch with Broadstone today and discover how AI can revolutionise your operations.
