AI in fintech: Transforming customer experience and operational efficiency

Why AI—and why now?

After two years of explosive progress in generative models, artificial intelligence (AI) has become the defining force behind innovation within financial services.

According to NTT Data, a remarkable 91% of banking boards now have generative AI (Gen AI) initiatives on their agendas - a level of executive sponsorship unmatched by any other technology wave in decades. The benefits of AI in banking are two-fold: sharper, more human-centred customer experiences, and radically leaner operations.

1. Re-imagining the customer experience

24/7, human-grade service

From the basic FAQ widgets of the past, banking chatbots have matured to the sophisticated, conversational advisors in use today.

This technology can execute transactions and escalate complex cases, saving institutions an estimated US $7.3 billion in annual service costs, according to Juniper Research, and freeing up resources that banks can redeploy to higher-value client work.

Large-language-model agents already handle document queries and policy explanations at near-human levels of comprehension; research prototypes such as the CAPRAG hybrid RAG pipeline show how banks can blend different data (vector and graph) retrieval methods for even deeper context.

Hyper-personalisation at scale

Machine-learning algorithms continuously analyse individuals’ spending patterns, lifestyle signals and financial goal trajectories to determine “next-best actions”. Recent studies on AI-based personalisation have revealed impressive prediction accuracy above 88 % for recommending credit-risk-aware products.

In open-banking markets, the scope of these insights is widening. By aggregating data from multiple accounts, banks can enable richer, self-driven ways of managing money, such as automated bill-splitting, just-in-time savings sweeps and tax-loss harvesting, long before the consumer even makes a request.

2. Quiet revolutions in the back office

Document and contract intelligence

JPMorgan’s COIN platform parses commercial loan agreements in a matter of seconds - work that previously took lawyers an estimated 360,000 hours a year. Similar natural-language models now generate regulatory reports, reconcile payments and draft marketing copy —slashing turnaround times from days to minutes.

Real-time risk and fraud controls

Seven in ten financial institutions already lean on AI to police faster-payments fraud and synthetic ID schemes, often using third-party platforms that monitor billions of signals in the cloud. Even financial regulators are adopting these tools - Germany’s BaFin reported that AI added to its market-abuse alert system last year has “substantially improved hit rates”, raising the odds of catching offenders.

3. Governance and ethics: holding the trust line

As models move deeper into functions such as credit approvals, portfolio advice and surveillance, then bias, explainability, and privacy become existential.

The forthcoming EU AI Act will designate many financial-risk models as “high-risk”, meaning they will require rigorous documentation, fairness testing and human-override channels before the 2026 enforcement date. Firms that embed model cards, counterfactual explanations and privacy-preserving learning methods such as federated or synthetic-data pipelines into their development lifecycle will be in a stronger position for global compliance.

4. What comes next? A look to the future

1. Agentic finance (2025-2027) - Expect “level-3” autonomous finance — systems that automatically move idle cash to best performing accounts, refinance debt when interest rates dip and negotiate utility contracts. Frameworks outlining the six levels of autonomous finance suggest mainstream adoption of self-driving money within 24 months.
2. Embedded AI and open finance - Secure APIs are dramatically shortening the distance between data, model and moment. Early results from Citizens Bank’s open-banking platform show a 95 % drop in traditional screen-scraping incidents and pave the way for real-time credit scoring via external apps .
3. Edge and on-device LLMs for privacy - As compute footprints shrink, ‘small’ frontier models will run directly on mobile secure enclaves. This will keep biometric spending signatures local while still supporting federated learning updates to the cloud.
4. Continuous assurance tooling - Expect AI-for-AI: dedicated validation models that watch production systems for drift, hallucination and unfair impact. Regulators are  likely to mandate such controls as a condition for using GenAI in regulated financial advice.
5. Human-in-the-loop evolution - The most successful fintechs will treat AI as a teammate, not a replacement. Roles will shift from rote processing to model stewardship. This will entail curating data, auditing outputs and designing empathetic intervention pathways — a skillset already highlighted by BaFin’s experience and echoed in global surveys of bank leadership).

AI is no longer a laboratory curiosity; it has become the new operating system of finance. Institutions that harness its power responsibly — balancing radical automation with transparent oversight — will define the next era of customer trust and operational excellence.

At the Gillmore Centre, our research agenda centres on these twin pillars: unlocking AI’s generative potential while engineering the guardrails to ensure finance remains fair, explainable, and human-centric.

The next 18 months will separate early experimenters from AI-native leaders; the next five years will decide the competitive map of global financial services. The time to scale, audit and govern is now.

The Gillmore Centre series features authors from the Gillmore Centre of Financial Technology at Warwick Business School as they explore new innovations in fintech from an academic perspective. Keep an eye out for more articles from the Gilmore Centre to learn more about new developments in the field.