Thought Leadership
With market forces already in motion, 2026's real differentiator will be the technology trends financial institutions choose to act on—and how fast they move.
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ThinCats, a leading alternative finance provider supporting mid-sized SMEs across the UK, has achieved remarkable digital transformation through its five-year partnership with nCino.
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The building society sector continues to demonstrate remarkable resilience and momentum, accounting for 72% of mortgage market growth between April and September 2024.
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We're excited to launch our new Substack blog, where technical experts share what we’re building, learning, and solving in cloud banking technology.
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To buy or to build? That’s the debate in today’s evolving landscape.
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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?
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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.
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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.
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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.
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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.
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