Exploring the Digital Transformation of Credit Risk Management in Europe
In today’s financial landscape, digital transformation has become an essential priority for financial institutions (FIs), and credit risk management is no exception. Despite economic challenges, many banks are making significant investments in advanced technologies to fast-track their digital transformation. While cost pressures are considerable, the potential of technology to enhance automation and efficiency continues to drive these investments forward.
Financial institutions face a complex dual challenge: navigating heightened regulatory scrutiny while responding to increasing competition. To stay ahead, they must modernise their technology infrastructure to foster innovation and unlock new business models. For credit risk management, this transformation demands substantial resources, particularly for managing the entire credit lifecycle. The approach often varies depending on the institution’s size and sector, but it typically requires dedicated credit personnel and the integration of multiple systems—such as credit and fraud databases—to manage the process effectively.
The rise of artificial intelligence (AI) is transforming the credit risk management sector. The birth of generative AI (GenAI) highlighted the tangible capabilities of AI, spurring significant investment and streamlining the approval of AI-driven business initiatives—including those within credit risk management. GenAI is poised to play an increasingly critical role in legacy modernisation efforts as organisations recognise its potential to drive innovation and digital transformation.
In response to the rise of AI-driven corporate strategies, Omdia, on behalf of nCino®, surveyed 220 financial services professionals from European FIs and conducted in-depth interviews with 11 senior executives responsible for credit risk management within corporate banks. The study designed to understand their key priorities, challenges, and areas of investment in credit risk management systems. This report offers valuable insights into how European financial institutions conceptualise and address the challenges of credit risk management. It explores the opportunities and limitations of existing systems, processes, and tools, while also identifying the obstacles they face. The markets surveyed include Belgium, Denmark, Finland, France, Germany, Ireland, Luxembourg, Netherlands, Norway, Spain, Sweden, and the UK. This report provides a strategic understanding of credit risk management trends and innovations throughout the European sector.
To read more about the evolution of credit risk management for European FIs, click here and download the full report.
Artificial intelligence (AI) and machine learning (ML) are transforming how financial institutions approach many traditional banking processes. When it comes to AI in the financial services industry, the concept of explainability—i.e., the ability to clearly communicate the process behind AI’s decision-making and understand the model’s inner workings—is of the utmost importance.
Being a leader in the financial services industry requires a seamless approach to innovation and efficiency. One of the crucial elements to success is the integration of automation and augmentation into credit portfolio management, as these tools have the ability to transform operations, manage risk, and improve both banker and client experiences.
The mortgage industry has seen some significant changes in recent years with the introduction of digital technology solutions. Organizations are increasingly turning to technology for avenues of competitive advantage, leading many companies into a state of flux as they adjust their business model and digital mortgage strategies to create and maintain profitability with long-term success.
In the rapidly evolving lending landscape, caused in part by the bank failures of 2023, credit portfolios are facing significant stress and heightened challenges, including rising default rates, fluctuating interest rates, and economic uncertainty. Coupled with strict regulatory demands for risk differentiation and portfolio diversification, these pressures are exposing the limitations of current credit portfolio monitoring processes, which are often static, reactive and subjective. As a result, financial institution (FI) leaders are rethinking their credit portfolio management practices.
Artificial intelligence (AI) and machine learning (ML) are transforming how financial institutions approach credit decisions and traditional banking processes. By incorporating AI into the credit decisioning process, financial institutions can help create a more inclusive, intuitive, and impactful financial services landscape.
