Advancements in automation, AI-enabled affordability assessments, and digitised borrowing applications have had a marked impact on the financial services sector, not least for short-term loan providers, who increasingly need to focus on efficiency and speed to satisfy consumer expectations.
Amid a world of rapidly evolving regulation and legislation covering data protection and consumer rights, lending criteria, and responsibility requirements, these technologies have become a fundamental necessity for many, but challenges remain.
Cashfloat, a fully regulated and experienced direct lender within the Western Circle Group, explains what these innovations mean for consumer experiences, why investments in tech have become a major priority across the space, and why independent decision models are a best practice approach.
Evaluating the adoption of AI-powered technologies by UK-based financial services providers
The Bank of England (BoE) and the Financial Conduct Authority (FCA) analysed this specific area to quantify how AI usage has penetrated the regulated lending industry over recent years. They discovered that 75% of companies are already utilising AI in some form, and that an additional 10% expect to invest in AI by 2027.
Surpassing previous projections, around 55% of companies using AI within day-to-day operations are incorporating automation into decision-making, with around half of those decisions comprising partial automation backed by human oversight.
While this showcases the extent of AI’s adoption in the UK lending sector, there are also reservations and risks, despite 62% of lenders stating that AI has a low material impact on their decisions or functions.
For example, the study found that around 30% of all AI use involves third-party integrations, often due to a lack of expertise or resources. In effect, that means a considerable proportion of financial services providers are outsourcing tech provision to data providers and cloud-based services, which is considered a concern for data protection and consumer anonymity.
In addition, almost half of financial firms using AI say they only partially understand the technologies that underpin the services or decisions they provide. The data points towards issues around complex models that aren’t well understood, and the potential for AI to include hidden models that aren’t clearly known.
The benefits of wide-scope AI adoption in fintech
AI is, of course, a very new technology with mixed opinions about how its use will evolve over time. Still, there are positive outcomes, such as the ability for firms to leverage AIโs ability to tackle prevalent cybersecurity threats, one of the most prominent challenges for the sector.
Cybersecurity is deemed the most serious and systemic threat to the financial sector, with increases in this assessed risk due to the widespread reliance on third-party tech weโve mentioned. AI can combat this by conducting rapid, high-capacity analytics to identify potential cases of fraud, deliberate cybersecurity attacks, and transactions that may be in breach of anti-money laundering legislation.
Other advantages are consumer-centric, with lenders able to operate more efficiently by processing more transactions or applications without the time constraints of manual handling and data entry, thereby delivering better productivity outcomes and a more streamlined customer experience.
Use cases of AI in fintech to augment consumer and lender safety
There is little doubt that maintaining a balance is critical: firms must continue to lend and advise customers responsibly and fairly, without sacrificing the importance of risk assessment or the possibility of algorithmic biases that can unfairly disadvantage some consumer demographics.
One real-world application that has proven effective in supporting that balanced approach is the use of automation for routine tasks where outcomes are very likely or definite, whether a human or an AI-supported program completes the task.
Those with greater nuance or complexity, or where there is any ambiguity about whether a professional would have reached the same conclusion as a digital tool, can be referred to qualified advisers to review and ensure they use human insight and judgement to reach the right decision.
This approach ensures that everyday tasks such as processing approved transactions and conducting underwriting checks are fast, secure, and safe, without any room for error, while simultaneously enabling human control and regulatory compliance.
How financial services regulators are adapting to an AI-powered world
Regulators in most jurisdictions, including the UK’s FCA, have voiced concerns, and the report weโve mentioned covers this area, noting constraints on the use of AI where privacy and data protection are core priorities.
Other barriers to regulator-approved AI systems include a lack of information about system resilience, access to data managed by third-party systems, and the inability to demonstrate that AI systems meet companies’ duties of care to their consumers.
Going forward, the FCA is focusing on outcomes, rather than developing new AI-specific rules, but continues to conduct analytics and issue guidance as its own position evolves and the use of AI becomes better established.
The importance of independence and governance within AI-assisted fintech advancements
While still subject to speculation, there is a consideration around the way algorithms react to changes in financial markets or trends in transaction volumes.
This may have the potential to create โherdingโ where models follow each other. That outcome may pose a serious issue for companies dependent on third-party technologies that could exaggerate upward and downward swings or provide biased decisions without this necessarily being immediately apparent.
Likewise, as demonstrated by the CrowdStrike global outage last year and the more recent short-lived but significant AWS crash, third-party risks can affect multiple providers and financial institutions simultaneously, creating unmeasured vulnerabilities.
In an ideal world, all fintech companies would seize the advantages of AI and counteract the risks associated with outsourcing tech provision by developing in-house resources. This ensures they have full oversight over how data is accessed, stored, and analysed, who has the ability to influence automated or semi-automated decision-making, and how consumersโ rights to confidentiality and protection of sensitive financial data are protected.
This solution, while resource-dependent, enables providers to ensure transparency in how lending decisions are made, without relying on third-party AI models that can’t be properly tracked, which poses issues for both consumer trust and regulatory compliance.
About Cashfloat
Cashfloat is one of the UKโs leading direct lenders. We provide online loans for people with all credit scores. Cashfloat is fully authorised and regulated by The Financial Conduct Authority.
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