Optimising Credit Decisioning: Gaining a Competitive Edge in a Rapidly Evolving Financial Market

The UK lending market is poised for significant transformation. Growth is set to build steadily over the next two years, with total UK bank lending forecast to rise to 3.7% (net) this year, and further to 4.3% (net) by 2027¹ as interest rates stabilise and consumer appetite to borrow strengthens. Simultaneously, the Finance & Leasing Association (FLA) anticipates a 6% increase in total UK new consumer credit by value in 2025, with credit card finance projected to grow by 5%.²

As the economy responds to local and global shifts, lending is becoming increasingly complex, and challenges persist. Lenders continue to grapple with rising fraud risks, regulatory requirements, economic uncertainty, evolving consumer expectations, and the growing complexity around data accuracy and availability. Against this backdrop, the adoption of sophisticated credit risk decisioning platforms is no longer optional, its critical for lenders seeking to stay competitive and address the ever more intricate needs of today’s consumers.

At the same time, lenders need agility. They must make the smartest, most precise lending and underwriting decisions to help drive sustainable and profitable growth. From onboarding the right customers and customising offers to maximising the value of existing relationships and retaining the most profitable accounts, the path to long-term growth is complicated and dynamic. In other words: To stay competitive, lenders must innovate.

As credit decisioning becomes more complex and customised, lenders require decisioning capabilities that can deliver greater efficiency, speed and accuracy. With an ongoing wave of technology advancements, particularly in artificial intelligence (AI) and machine learning (ML), real-time data processing and cloud-based platforms are playing key roles in enabling this. These technologies are not only automating risk assessments but also enabling real-time decisioning, more accurate credit scoring models, better fraud detection, and adaptive risk management strategies that adjust to changing consumer behaviour.

Meanwhile, the growing emphasis on data-driven strategies is requiring lenders to use richer alternative data sources, such as Open Banking data, payment histories and behavioural insights to create more robust consumer profiles. This shift promotes fairness and transparency in credit assessments, helping lenders expand their customer bases and personalise offerings – while also demanding stronger data governance and analytical capabilities.

In short, lenders that modernise and continually optimise their credit decisioning processes will be best positioned to capture growth opportunities and succeed in a fast-evolving financial landscape.

In competitive markets, the ability to make faster and more accurate credit decisions is critical. That has created friction for lenders that rely on traditional  assessment methods. These legacy decisioning systems make it difficult to effectively optimise risk assessments, resulting in inconsistent risk evaluations, manual decisioning and siloed data sources. That in turn creates bigger problems, such as operational inefficiencies, increased risk exposure and potential missed revenue opportunities.

At the same time, consumer expectations are evolving. Customers now demand instantaneous decisions, as well as digital-first services that are delivered with minimal delays. According to one survey: one in seven British consumers expect financial providers to approve loan applications within minutes; a third want savings account applications to be processed within minutes; and a quarter expect decisions on insurance products to be processed in the same timeframe.³

However, legacy and internally developed decisioning platforms often restrict lenders from adapting quickly to such changing market dynamics because they’re rigid, not designed for digital experiences, hard to scale and costly to update. They also require significant support from IT teams and vendors, struggle to integrate with alternative data sources, and lack the flexibility to respond to new risks, consumer behaviours or support evolving requirements in real-time.