The integration of ESG factors into credit risk functions is reshaping the way financial institutions assess and manage credit risks. Firms need to understand the impact ESG considerations can have on the creditworthiness and performance of borrowers.

The credit market landscape is analytics driven. Success hinges on the ability to obtain and leverage sophisticated analytics effectively. Having an edge in analytics can be the differentiating factor.

In light of emerging legislation centred around ESG, financial firms must employ robust risk strategies to address the multifaceted challenges posed by the ESG landscape.

The value of any technology solution is dependent on the team put in place to implement, maintain, and support your investment.

As banks confront the many challenges of the ever-evolving landscape of finance, understanding and effectively managing risks have become paramount for success and sustainability.

Financial institutions are increasingly relying on artificial intelligence and machine learning (AI/ML) for critical decision-making and efficient client services. However, validating these models presents unique challenges beyond traditional validation practices. This blog provides an overview of additional requirements for model validation in the context of AI/ML.

The transition from Libor to SOFR has presented several challenges in the market. During the transition, financial institutions and market participants have been working to develop and adopt new products and contracts based on SOFR as a replacement for those previously linked to Libor.

In an era of economic uncertainty and rapid technological advancements, staying ahead of credit risk is essential for financial institutions to protect their assets and maintain stability.

When implementing an MRM policy, the core questions that arises centres around how much external assistance is required to support governance and validation.