Artificial intelligence (AI) has moved beyond being a back-office efficiency tool in financial services. In 2025, AI is becoming embedded into the very infrastructure of banks, lenders, FinTech’s, and wealth managers.
From reshaping lending decisions to enabling hyper-personalised experiences and strengthening fraud detection, AI promises a financial system that is faster, smarter, and more inclusive. Yet it also raises new risks around bias, explainability, and trust.
AI is Reshaping Lending and Credit Decisions
Lending has always depended on data, but AI has supercharged the process by analysing far more signals than traditional credit scoring ever could. Instead of relying solely on repayment histories and income verification, AI now incorporates open-banking data, behavioural patterns, and even smartphone metadata.
“AI is revolutionising the consumer lending landscape, embedding advanced capabilities throughout the lending lifecycle,” explained Andrew Wright, Senior Director at Slalom. “From predictive analytics that assess creditworthiness using open-banking data, to real-time fraud detection and process automation, AI models expedite approvals and reduce human error.”
For borrowers with thin or non-existent credit files, this shift is potentially transformative. As Michele Tucci, Co-founder and Chief Strategy Officer at Credolab, tells Silicon UK: “AI is gradually shifting lending from static scoring to more dynamic decisioning. The real shift happens when AI doesn’t just replace rules but works alongside traditional data to uncover new layers of risk and opportunity.”
In practice, this means gig workers, freelancers, or those in cash-based economies may finally be recognised as creditworthy. Siyar Isik, CEO, Transkriptor, emphasised that AI, “has opened the door to faster and more flexible lending… This shift is helping people who were locked out of the old system, like freelancers and gig workers. Approval can happen in minutes instead of days.”
Yet there is a flip side. AI systems are only as fair as the data used to train them. If underserved populations are underrepresented in those data sets, AI may unintentionally replicate exclusion. Wright warns: “These communities are often underrepresented in traditional data sources, meaning AI systems may lack the visibility or the necessary context to assess them fairly. This raises the risk of algorithmic bias, where AI unintentionally replicates systemic inequalities from past financial decisions.”
To address this, many institutions are adopting a “human-in-the-loop” approach to detect and correct bias, validate outcomes, and provide recourse for customers who feel unfairly treated.
Why Human Oversight Remains Essential
Despite rapid advances in automation, experts agree that humans must remain part of financial decision-making. Shaun Hurst, Principal Regulatory Advisor at Smarsh, says: “Human oversight is absolutely a necessity for AI deployment in financial services. AI excels at pattern recognition and scale, but humans provide contextual judgment and handle edge cases outside training parameters.”
This is particularly important in sensitive areas such as lending, fraud detection, or debt collection. DK Sharma, President and COO at Kore.ai, tells Silicon UK: “Money is a deeply personal matter. That’s why people still matter: they bring judgment, empathy, and accountability to the parts of finance where algorithms alone can’t.”
Examples of this hybrid approach are already visible. A large Middle Eastern bank deployed modular AI agents to handle complex workflows while seamlessly escalating to human agents for sensitive cases. Sharma explained: “The combination of AI and human expertise can deliver speed and personalisation at scale, while ensuring decisions in sensitive matters are fair, compliant, and sensitive to real-life circumstances.”
Dan Kellett, Director of Lending and Data Analytics at Carmoola, echoes this view: “Human oversight is critical. Automation adds efficiency, but human oversight adds judgement. Machines are powerful, but they still need guardrails.”
This human presence is not just about compliance — it also reassures customers. When something goes wrong, people want to know a qualified professional is monitoring outcomes and correcting errors. As Wright emphasised, “In high-stakes domains… it is the lived experience of professionals that provides the critical lens for nuance, empathy and most importantly, fairness.”
Personalisation is Becoming a Defining Feature of FinTech
Beyond risk scoring, AI is powering a wave of hyper-personalised financial experiences. FinTech start-ups, unburdened by legacy systems, are leading the charge. Instead of segmenting customers into broad categories, AI systems now dynamically adapt in real time to individual behaviour, preferences, and even emotional cues.
Sharma explained: “Today’s FinTech’s are pushing far beyond rule-based segmentation, embedding AI across the entire customer journey, from onboarding and credit assessment to servicing and retention. The most forward-looking players are leveraging agentic AI to create proactive, emotionally and contextually intelligent agents. They bring empathy into automation.”
Some of the most innovative applications include AI agents that tailor debt collection reminders with empathetic messaging, multilingual interfaces that expand access, and apps that analyse spending behaviour to nudge users towards saving or investing.
Revolut, for instance, now functions as an intelligent financial coach, offering adaptive saving plans that evolve with a user’s behaviour. Petal uses alternative data and machine learning to personalise credit decisions, while Starling Bank deploys AI for real-time spending insights and automated budgeting.
As Carmoola’s Dan Kellett tells Silicon UK, “AI is making it possible to deliver hyper-personalised finance — think credit offers tailored to a person’s unique profile, or dynamic affordability checks that adapt in real time. We want finance tailored to the individual, not just their credit score band.”
Personalisation is not just about convenience; it is also about inclusion. Husnain Bajwa, Technology Evangelist at SEON, says: “FinTech startups are using AI personalisation to balance security with a seamless user experience, particularly in fraud prevention. Dynamic friction, for example, adjusts authentication requirements in real-time based on user behaviour.” This ensures that security does not come at the cost of accessibility.
Trust, Regulation and the Future of Financial Institutions
As AI becomes the “face” of financial advice, maintaining trust will be the defining challenge. Trust depends on transparency, fairness, and the ability for customers to understand — and challenge — decisions that affect their finances.
Chris Jones, Managing Director at PSE Consulting, explained: “Imagine asking your AI assistant to help you buy a £500 TV. In the near future, the AI wouldn’t just find the best product, it could also surface financing options before you commit. Yet, this convenience comes with new challenges. Consumers must believe the AI is recommending the right option for them, not just the one with the highest commission.”
Shawn Hurst at Smarsh emphasised that “trust can only be maintained through transparent processes and consistent outcomes. AI decisions must remain explainable, auditable and bias-free. Clear disclosure, robust testing across customer segments, and seamless human escalation paths are essential.”
Regulators are already responding. The EU’s AI Act classifies credit scoring as a high-risk application, requiring strict standards of transparency, fairness, and human oversight. Andrew Wright explained that “across jurisdictions, the message is clear: AI in credit must be responsible by design. Financial institutions that embrace this shift will be better positioned to innovate with integrity.”
This regulatory backdrop is accelerating a deeper transformation: banks are evolving into hybrid tech-finance entities. As Transkriptor’s Isik puts it, “Banks don’t see themselves as just banks anymore. They’re building internal tech teams, buying up FinTech startups, and shifting their infrastructure to the cloud. By 2025, most large financial players will look more like tech companies that also happen to move money.”
Ultimately, AI is not just reshaping processes — it is redefining the very identity of financial institutions. As Kellett says, “the winners will be data-driven, agile, but still grounded in trust and financial responsibility.”
2025, will be inseparable from financial services. It will enable faster, fairer lending decisions, create more personalised customer experiences, and turn banks into technology-driven entities. Yet it will also demand vigilant oversight, inclusive design, and transparent governance.
The future of money and machines is not one where humans disappear from the loop. Rather, it is one where AI and human judgement complement each other — scaling efficiency without sacrificing fairness, empathy, or accountability. Done right, AI will not only transform how money moves but also how trust in finance is built and sustained.
