5 Ways AI Is Changing Financial Services
*Updated 22 September 2026*
Artificial intelligence is no longer an experimental technology for financial services.
The latest published Bank of England and FCA industry survey found that 75% of responding financial firms were already using AI, with another 10% planning adoption within three years. Large UK and international banks reported particularly extensive use, while firms expected the median number of AI use cases to more than double.
The discussion has therefore changed.
Banks, lenders, wealth managers, payment providers and other financial institutions are no longer simply asking where AI could be used. They need to decide where it creates measurable value, how it integrates with existing systems and how decisions remain secure, explainable and accountable.
That distinction is important because the technology itself is only part of the transformation.
An AI model operating in isolation may save somebody a few minutes. AI integrated with customer data, communications, workflows and business systems can change how the organisation operates.
Britannic's view is that successful AI adoption in financial services comes down to three things: useful data, operational integration and appropriate human control.
The following five developments show where that shift is already happening.
| AI Development | Potential Business Value | What Firms Need To Control |
| Operational AI |
|
Data access and accuracy |
| Customer AI |
|
Human escalation and customer outcomes |
| Fraud and security AI | Detect unusual behaviour earlier |
|
| AI-assisted decisions |
|
Explainability, fairness and accountability |
| Agentic AI |
|
Permissions, resilience and third-party risk |
1. How Is AI Changing Everyday Financial Operations?
Some of the most valuable AI applications in financial services are not customer-facing at all.
The latest Bank of England and FCA survey found that optimising internal processes was already the most common AI use case, reported by 41% of respondents. Firms also expect operational efficiency, productivity and cost reduction to be among the areas where AI's benefits grow most significantly.
This is a considerable change from the original article's focus on traditional robotic process automation.
Modern AI can work with less structured information.
It can summarise conversations, classify emails, extract information from documents, identify themes, prepare employees for customer interactions and trigger workflows based on what it finds.
For financial organisations, this could include:
- Summarising a customer conversation before follow-up
- Categorising incoming correspondence
- Identifying the reason for customer contact
- Extracting actions from calls and meetings
- Analysing complaints for recurring issues
- Highlighting information that requires specialist review
- Reducing manual reporting and administration
Britannic's AI Engine applies this principle to unstructured information such as emails, chats, reviews and surveys, turning interaction data into themes, sentiment and actionable insight.
The important question is not simply what can AI automate?
Financial firms should ask which processes consume significant employee time without requiring significant human judgement?
Those are often the strongest starting points.
2. How Is AI Changing Customer Service In Banking And Finance?
AI is increasingly becoming part of the customer journey rather than a separate chatbot sitting alongside it.
A customer may want an immediate answer to a straightforward question, an update on a transaction, help completing a process or access to a specialist when circumstances become more complex.
AI can help determine what happens next.
Modern conversational AI can understand intent, retrieve relevant information, complete defined tasks and hand the interaction to an employee when necessary. AI can also support the employee during the conversation with relevant information, next-best actions, transcription and automated summaries.
Platforms such as the Five9 Intelligent CX Platform now combine contact centre capabilities with AI, automation, interaction analytics and agent assistance rather than treating them as separate technologies.
The value comes from how those capabilities connect with customer information and existing systems.
Britannic's work with fintech company Caxton is a useful example. Its Five9 contact centre was integrated with Zendesk, HubSpot and Microsoft Teams so employees could access customer history and context when interactions arrived. Better reporting and insight helped Caxton reduce call handling times by 21% and waiting times by 8% while maintaining an NPS above 70 during its busiest period.
This is an important distinction for AI investment.
The objective is not to automate every interaction.
It is to determine which interactions can be resolved automatically, where AI can help employees and where customers still need human expertise.
Britannic's wider Financial Services proposition brings these areas together across customer communications, automation, contact centres and data.
3. How Is AI Improving Fraud Detection And Financial Security?
Fraud detection remains one of AI's strongest financial-services applications.
AI can identify patterns across large volumes of transactions and interactions that would be difficult to detect manually, helping organisations recognise potentially unusual behaviour faster.
The Bank of England and FCA found that fraud detection was already being supported by AI at 33% of responding firms, while data and analytical insight, anti-money laundering and combating fraud were among the areas where firms saw the greatest current AI benefits.
However, AI also changes the threat environment.
The same industry survey ranked cybersecurity as the greatest potential systemic AI risk, with third-party dependencies another significant concern. The Bank of England's July 2026 Financial Stability Report warned that advances in frontier AI could increase cyber and operational vulnerabilities across financial services.
Financial institutions therefore have to consider both sides of AI.
It can strengthen fraud prevention while simultaneously creating new attack surfaces, dependencies and opportunities for criminals.
Customer communications are part of that trust problem too.
As fraud and impersonation become more sophisticated, customers may become less willing to trust unexpected calls or messages. Britannic's Branded Calls can display verified business identity and the reason for calling on supported devices, while Branded Messages provides verified branded messaging. These technologies do not replace fraud controls, but they can help legitimate financial communications become easier for customers to recognise.
The wider principle is that AI innovation and communications security need to develop together.
4. How Is AI Changing Financial Decision-Making?
AI is increasingly influencing decisions rather than simply analysing information.
The Bank of England and FCA found that 55% of financial-services AI use cases already involved some level of automated decision-making. However, only 2% were fully autonomous, while 24% were described as semi-autonomous and retained human oversight for critical or ambiguous decisions.
That balance is significant.
AI can support areas such as:
- Credit assessment
- Risk modelling
- Fraud investigation
- Customer segmentation
- Underwriting
- Financial advice
- Investment research
- Compliance monitoring
Yet greater automation also raises questions around explainability, bias, accountability and customer outcomes.
This is particularly important where an AI-assisted decision could affect someone's access to credit, financial product, insurance or advice.
The industry is already responding. 81% of firms using AI reported employing some form of explainability method, while 84% had an accountable person responsible for their AI framework.
The FCA reinforced this approach in June 2026. Rather than introducing a separate set of AI-specific regulations, it said financial firms remain subject to existing frameworks including the Consumer Duty, Senior Managers and Certification Regime and expectations around governance and controls.
This changes how financial organisations should think about AI.
A technically accurate model is not automatically a suitable business process.
Firms also need to understand:
Who owns the decision?
Can the reasoning be explained?
When must a person intervene?
How is the outcome monitored?
What happens when the AI gets something wrong?
Human oversight should therefore be designed into the process rather than added once the technology has been deployed.
5. What Will Agentic AI Mean For Financial Services?
The next major development is AI moving from providing information to taking action.
Agentic AI systems can potentially interpret an objective, decide which steps are required and interact with multiple systems to complete parts of a process.
In financial services, that could eventually mean an AI agent helping a customer compare options, gather required information, initiate a process, organise follow-up actions or coordinate tasks across multiple systems.
The FCA's Mills Review, published on 6 July 2026, identified agentic AI as one of the technologies that could significantly change retail financial services. FCA research found that approximately one in five UK adults, equivalent to around 11 million people, were likely to use AI capable of acting autonomously within predefined goals.
HM Treasury's July 2026 Financial Services AI Adoption Plan also highlights agentic payments as an area requiring further development and coordination.
This is considerably more advanced than the chatbot model described in the original article.
However, autonomy also increases the importance of controls.
The latest published Bank/FCA survey found that one third of existing AI use cases were already supplied through third parties, while 46% of firms said they had only a partial understanding of the AI technologies they used.
Before giving AI permission to perform actions, financial firms therefore need clear answers around:
- What systems can it access?
- What data can it use?
- Which actions can it perform independently?
- Which actions require approval?
- How are its decisions logged?
- What happens when an external AI provider fails?
- Can employees override or stop the process?
- How will customers know when they are interacting with AI?
The next generation of financial AI will not simply be judged by how intelligent the model is.
It will be judged by whether the organisation can control what it does.
What Should Financial Firms Review Before Scaling AI?
The strongest AI strategy starts with the business process rather than the technology.
Britannic would recommend assessing five areas before moving an AI use case beyond experimentation.
Outcome
Define the problem first. What should improve in measurable terms such as customer effort, processing time, cost, fraud detection, employee productivity or service quality?
Data
Identify the information the AI requires, where that data resides and whether it is sufficiently accurate, accessible and governed.
Integration
Consider what happens after the AI produces an answer. If employees still need to manually copy information between systems, much of the potential value remains unrealised.
Control
Define ownership, permissions, escalation, human oversight, security and what happens when the system produces an incorrect or unexpected result.
Measurement
Compare the outcome against the original baseline. AI should demonstrate operational or customer value rather than simply increase the number of AI tools being used.
This is particularly important because financial-services organisations are becoming increasingly dependent on external AI providers. The Bank/FCA survey identified third-party dependency, model complexity and hidden models among the risks expected to grow most significantly.
The question for leadership should therefore move from:
“Where can AI be introduced?”
to:
“Where can AI produce a measurable outcome that the organisation can safely govern?”
AI In Finance Is Moving From Experimentation To Operations
The five biggest AI developments in financial services are increasingly linked.
Operational AI reduces manual work.
Conversational AI changes customer service.
Machine learning strengthens fraud detection and analysis.
AI increasingly contributes to financial decisions.
Agentic AI could begin taking actions across processes and systems.
The common requirement across all five is integration and governance.
The UK Government's 2026 Financial Services AI Adoption Plan describes scaling AI as strategically important to the sector, but also emphasises responsible adoption, resilience, skills and appropriate regulatory oversight.
For financial institutions, the next competitive advantage is therefore unlikely to come from simply having access to an AI model.
It will come from understanding where AI belongs within the operating model, connecting it to the right information and systems, and maintaining appropriate human control.
Britannic supports financial-services organisations across AI, automation, customer communications, contact centres, analytics, security and systems integration, helping firms operationalise technology around defined business outcomes rather than introducing isolated tools.
Financial organisations assessing where AI could deliver measurable value can speak to Britannic about identifying suitable use cases, understanding integration requirements and establishing how AI should work alongside existing processes, platforms and people.
