The principal finding of the inaugural report of the CFIT Open Finance Coalition was that substantially more lending could be made to small businesses, and significant increases in lending acceptance rates made possible, with the timely provision of more high-quality, reliable data to lenders.  

Let’s put it out there right from the start, your Customer Lifecycle Management (CLM) journey has become increasingly complex, expensive, and difficult to navigate.

To buy or to build? That’s the debate in today’s evolving landscape.  

Recent sentiment shows that SMEs are feeling more optimistic about their prospects in 2025.  

On Wednesday January 15, BIBA launched their 2025 Manifesto at the houses of parliament. The key theme in 2025 is “Partnering to deliver value” and as a BIBA Facility provider, we couldn’t agree more!

As we look towards 2025, many analysts and industry experts are sharing observations on the current state of artificial intelligence (AI) and their predictions for the future. As AI use cases continue to evolve across industries, it’s critical for financial institutions (FIs) to evaluate their own AI journeys. Are they ahead of, in step with, or falling behind their competitors?

In a world where the financial industry continues to evolve rapidly, financial institutions need to do the same. One way to maintain your edge is by embracing a balance of automation and augmentation. This approach enhances operational efficiency, optimizes risk management, and revolutionizes the experience for both banker and client.

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.

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.