AI for Financial Services

Artificial intelligence is becoming an increasingly important part of the financial services sector.

Banks, insurers, lenders, accountants, investment firms and financial advisers are exploring how AI can improve customer service, detect fraud, analyse information and reduce the amount of time employees spend on repetitive administration.

The adoption of AI is already widespread. A Bank of England and Financial Conduct Authority survey found that 75% of responding financial-services firms were using AI, with another 10% planning to adopt it within three years. However, the same research highlighted growing reliance on third-party providers and incomplete understanding of some AI systems.

AI can create significant benefits, but financial organisations handle some of the most sensitive and valuable information held by any business.

A poorly controlled AI system could expose personal data, produce unfair outcomes, provide inaccurate information or create new cybersecurity and regulatory risks.

Financial-services organisations therefore need to approach AI as a controlled business transformation rather than simply another software purchase.

What Is Artificial Intelligence?

Artificial intelligence describes technology that can perform tasks normally associated with human intelligence.

Depending on the system, AI may be able to:

• Interpret written language
• Analyse large volumes of information
• Recognise patterns
• Produce predictions
• Generate documents
• Summarise conversations
• Classify transactions
• Recommend actions
• Automate parts of a process
• Communicate with customers

Traditional financial software normally follows specific rules created by a developer.

An AI system may identify patterns from previous information and use those patterns to produce an output, recommendation or prediction.

Generative AI can create new content, such as text, reports, summaries and customer communications.

Agentic AI can go further by planning and completing several steps towards an objective, potentially using connected applications and business information.

Why Is AI Becoming Important in Financial Services?

Financial organisations process large volumes of information.

This may include:

• Customer applications
• Transaction records
• Insurance claims
• Financial statements
• Identity documents
• Compliance records
• Investment information
• Emails and telephone calls
• Contracts
• Risk assessments

Employees may spend significant amounts of time reviewing, categorising and transferring this information.

AI can help process information more quickly, identify patterns that may otherwise be missed and provide employees with a clearer starting point for their work.

The Government’s Financial Services AI Adoption Plan, published in July 2026, supports a principles-based and outcomes-focused approach to AI adoption while recognising that emerging technologies may require targeted clarification from regulators.

The objective should not be to introduce AI because competitors are doing it.

It should be to identify genuine business problems that AI can help solve safely and measurably.

1. Improving Fraud Detection

Financial fraud can involve large numbers of transactions, accounts, devices and communication methods.

AI can help analyse activity and identify patterns that may indicate:

• Account takeover
• Payment fraud
• Identity fraud
• Unusual transaction behaviour
• Repeated failed logins
• Suspicious device activity
• Changes in customer behaviour
• Connections between accounts
• Attempts to bypass controls

Traditional fraud systems often rely on fixed rules.

For example, a transaction may be flagged when it exceeds a particular value or originates from an unexpected country.

AI can supplement these controls by identifying combinations of behaviour that appear unusual even when no single transaction breaks a fixed rule.

This may help financial organisations investigate suspicious activity earlier.

However, an AI-generated fraud alert should not automatically be treated as proof of wrongdoing.

False positives could delay legitimate payments, restrict customer accounts or create an unfair experience.

Human review and clear escalation procedures remain important.

2. Supporting Anti-Money Laundering Processes

Anti-money laundering and financial-crime teams often review large volumes of customer and transaction information.

AI may assist with:

• Transaction monitoring
• Customer risk classification
• Identifying unusual patterns
• Reviewing adverse information
• Connecting related entities
• Prioritising alerts
• Summarising case information
• Preparing investigation timelines

This may allow employees to focus their attention on higher-risk cases rather than manually reviewing every alert in the same way.

AI should support the investigation rather than make the final regulatory decision without suitable oversight.

The organisation must be able to explain how alerts are generated, how cases are prioritised and how employees prevent inaccurate assumptions from becoming part of a customer record.

3. Automating Document Processing

Financial-services organisations receive information in many different formats.

These may include:

• Bank statements
• Payslips
• Tax records
• Insurance documents
• Application forms
• Identity documents
• Contracts
• Invoices
• Valuation reports
• Customer correspondence

Employees may need to extract information from these documents and enter it into another system.

AI can help identify relevant information, categorise the document and prepare data for review.

For example, an AI system could extract income figures from a submitted document and place them into an application workflow.

This can reduce repetitive data entry and speed up processing.

However, the extracted information must be validated.

A misplaced decimal point, incorrectly identified date or confusion between two customers could create serious consequences.

High-impact financial information should not be accepted solely because an automated system appears confident.

4. Improving Customer Service

AI-powered chat and support tools can help customers access information more quickly.

A system might answer questions about:

• Application progress
• Opening hours
• Required documents
• Product features
• Payment dates
• Claim processes
• Account procedures
• Contact information

AI can also support employees by summarising previous customer interactions or identifying relevant internal guidance.

This can reduce the time customers spend waiting for routine information.

However, financial products can be complex, and an inaccurate answer may influence an important financial decision.

Customer-facing AI should therefore have clear limits.

The system should recognise when a question requires an authorised employee and transfer the customer without pretending to provide advice it is not qualified or permitted to give.

The FCA’s approach continues to apply existing requirements, including Consumer Duty, which expects firms to design suitable products, communicate effectively and provide appropriate customer support.

5. Producing Meeting and Communication Summaries

Employees in financial services may attend several customer, compliance and internal meetings each week.

AI can help:

• Summarise a meeting
• Identify agreed actions
• Prepare follow-up notes
• Draft customer communications
• Find areas requiring clarification
• Compare discussions with previous records
• Create an initial case summary

This can reduce administrative work and allow employees to focus more attention on the customer.

Meeting summaries should still be checked before they are added to an official customer or compliance record.

An AI system may misunderstand a speaker, confuse two figures or incorrectly describe a provisional discussion as a confirmed decision.

6. Assisting With Financial Analysis

AI can help employees explore large amounts of financial information.

Potential uses include:

• Identifying trends
• Comparing performance between periods
• Highlighting unusual values
• Producing an initial forecast
• Summarising management accounts
• Analysing customer behaviour
• Reviewing portfolio information
• Identifying operational costs
• Preparing charts and reports

This can make analysis more accessible to employees who are not advanced data specialists.

However, the quality of the output depends on the quality of the underlying information.

Incomplete, duplicated or incorrectly classified data may produce misleading conclusions.

AI should be used to assist professional analysis rather than replace the experience and judgement required to interpret financial information.

7. Supporting Insurance Claims

Insurers may use AI to assist with the early stages of a claim.

It may help:

• Categorise the claim
• Check whether required information is present
• Summarise submitted evidence
• Identify possible duplication
• Prioritise urgent cases
• Detect unusual patterns
• Estimate which specialist team is required
• Draft initial customer updates

This could reduce delays and improve the consistency of routine claims processing.

However, claims can involve unusual circumstances that do not fit standard patterns.

A system trained on previous claims may also reflect historic inconsistencies or biases.

Customers should have access to meaningful human review, particularly when a decision could affect whether a claim is accepted or rejected.

8. Helping With Credit and Risk Assessments

AI can analyse more information than a traditional manual assessment.

This may help lenders identify patterns relating to:

• Affordability
• Repayment behaviour
• Default risk
• Application fraud
• Changing financial circumstances
• Portfolio exposure

Used responsibly, this could help financial organisations make faster and more consistent assessments.

However, credit decisions can significantly affect an individual.

Using inappropriate or inaccurate information could produce unfair outcomes.

A model may also identify correlations without understanding whether they are appropriate to use.

The ICO’s AI guidance emphasises applying data-protection principles to AI systems and being able to explain decisions that are made or assisted by AI. The ICO also provides a risk toolkit to help organisations assess potential effects on individual rights and freedoms.

Financial organisations should test models for fairness, accuracy and unintended discrimination before relying on their output.

9. Improving Regulatory and Compliance Work

Compliance teams regularly review policies, records, transactions and communications.

AI may help:

• Search regulatory material
• Compare policies
• Summarise rule changes
• Identify missing information
• Categorise compliance cases
• Prepare audit evidence
• Review recorded communications
• Find inconsistencies in documents
• Draft an initial compliance report

This may reduce the amount of time employees spend searching through information.

However, AI should not be treated as a substitute for legal or regulatory advice.

Regulatory requirements can depend on the specific product, customer, firm and circumstances.

AI-generated interpretations should be reviewed by appropriately qualified employees.

10. Increasing Employee Productivity

Many early financial-services AI projects focus on internal productivity rather than fully automated customer decisions.

AI tools can help employees:

• Draft emails
• Summarise documents
• Prepare presentations
• Find internal information
• Create meeting agendas
• Compare contracts
• Generate initial reports
• Rewrite technical information
• Identify tasks from conversations
• Analyse spreadsheets

The Bank of England has identified internal process optimisation, customer support and financial-crime prevention as leading near-term AI use cases for financial firms.

These uses may provide a lower-risk starting point because an employee can review the result before it affects a customer.

The organisation should still define which information employees may enter into an AI tool and which platforms are approved.

The Benefits of AI in Financial Services

When introduced properly, AI may provide several advantages.

Faster Processing

AI can review information more quickly than employees can process it manually.

This could reduce delays in applications, claims, compliance reviews and customer enquiries.

Reduced Repetitive Administration

Employees can spend less time copying data, categorising documents and preparing routine summaries.

This creates more time for customer service, professional judgement and complex cases.

Better Use of Information

AI can help identify patterns across large datasets that may be difficult to recognise manually.

This may improve fraud detection, financial analysis and operational planning.

More Consistent Processes

A properly designed system can follow the same workflow each time.

This may reduce variation caused by different employees completing the same task in different ways.

Improved Customer Availability

AI-assisted support can help customers obtain routine information outside normal working hours.

This should supplement rather than eliminate access to appropriate human support.

Greater Scalability

An AI-enabled process may handle increasing volumes without requiring the same increase in manual administration.

This can help growing financial organisations manage demand more efficiently.

The Risks of AI in Financial Services

The potential benefits should be balanced against the possible risks.

Inaccurate Information

Generative AI can produce information that sounds convincing but is incorrect.

It may:

• Misstate a figure
• Invent a source
• Confuse two customers
• Misunderstand a document
• Omit an exception
• Use outdated information
• Apply the wrong policy
• Make an unsupported assumption

Employees should verify important outputs against an approved source.

AI-generated information should not be sent directly to customers or entered into official records without suitable controls.

Bias and Unfair Outcomes

AI systems learn from data.

If historic information reflects inconsistent or unfair outcomes, the model may reproduce or amplify them.

Bias may affect:

• Lending
• Insurance
• Fraud monitoring
• Customer prioritisation
• Recruitment
• Complaint handling
• Financial advice

Testing should examine whether particular groups receive systematically different outcomes.

The organisation should also provide a process for customers to question or challenge important AI-supported decisions.

Lack of Explainability

Some AI systems can be difficult to interpret.

The organisation may receive a recommendation without a clear explanation of which information influenced it.

This can create problems when:

• A customer asks why a decision was made
• An employee needs to challenge the result
• A regulator requests evidence
• The model produces an unexpected outcome
• A complaint is investigated

Financial organisations should understand the level of explanation provided by each model before using it for an important decision.

A system may be unsuitable when the organisation cannot explain or defend its output.

Data Protection and Confidentiality

Financial organisations hold information including:

• Identity details
• Bank information
• Credit records
• Income
• Investments
• Insurance information
• Employee data
• Customer communications

Employees should not enter this information into an unapproved consumer AI service.

Before adopting a platform, the organisation should understand:

• Where data is processed
• How prompts are retained
• Whether information is used for model training
• Who can access the records
• Which subcontractors are involved
• How information is deleted
• Which security certifications apply
• Whether international data transfers occur

A data-protection impact assessment may be required for uses that create a high risk to individuals.

Cybersecurity Risks

AI can help defend organisations, but it can also strengthen attackers.

Criminals may use AI to create more convincing phishing emails, identify vulnerabilities and automate parts of a cyberattack.

In May 2026, HM Treasury, the Bank of England and the FCA warned that advanced AI capabilities could increase the speed and scale of cyber threats. They highlighted the need for board-level understanding, stronger vulnerability management, secure access controls, third-party oversight and tested response and recovery capabilities.

Financial organisations should make sure their core cybersecurity controls are strong before increasing their use of AI.

These controls should include:

• Multi-factor authentication
• Managed endpoint protection
• Security patching
• Network security
• Email protection
• Vulnerability management
• Data protection
• Security monitoring
• Incident response
• Tested backups

AI should not be used as a replacement for security fundamentals.

Third-Party Provider Risks

Many organisations will use AI supplied by an external provider rather than develop their own models.

The 2024 Bank of England and FCA survey found that a third of reported AI use cases involved third-party implementations. It also identified concentration among major cloud, model and data providers.

Relying on external technology creates questions about:

• Service availability
• Supplier security
• Data handling
• Model updates
• Cost changes
• Vendor lock-in
• Subcontractors
• Incident reporting
• Business continuity
• Exit arrangements

The business remains responsible for understanding the risk even when the technology is outsourced.

Supplier contracts should clearly define responsibilities, data ownership, security requirements and what happens when the relationship ends.

Operational Resilience

An AI system may become part of an important business process.

If the service becomes unavailable, employees may no longer know how to complete the process manually.

Organisations should consider:

• What happens if the AI platform fails
• Whether a manual process exists
• How long the business can operate without it
• Which information needs to be recovered
• Whether employees retain the necessary skills
• How the supplier communicates incidents
• How changes are tested
• Whether alternative providers exist

Critical financial services should not depend on one AI system without appropriate resilience and recovery planning.

Overreliance on Automation

Employees may trust an AI system because it is usually correct.

Over time, they may stop checking its output properly.

This is known as automation bias.

A review process is only effective when the employee genuinely considers the result rather than automatically approving it.

Employees should be trained to:

• Check sources
• Verify calculations
• Question unusual results
• Identify missing information
• Record corrections
• Escalate uncertainty
• Understand the model’s limitations

Professional accountability remains with the organisation and its employees.

Shadow AI

Employees may begin using AI platforms without approval because they appear convenient.

They might paste customer information into a public chatbot, upload a document to an unknown service or create an automated workflow without involving IT.

This is sometimes described as shadow AI.

It can create risks including:

• Data leakage
• Unapproved processing
• Missing audit records
• Inaccurate outputs
• Unknown suppliers
• Unexpected costs
• Lack of accountability

Businesses should provide approved tools and a clear AI policy.

Simply banning AI may encourage employees to use it secretly.

The safer approach is to explain which platforms are permitted and how they can be used responsibly.

What Does the FCA Expect?

The FCA currently applies its existing regulatory framework to the use of AI rather than introducing a completely separate set of AI rules.

Its approach is principles-based and outcomes-focused, with existing requirements relating to Consumer Duty, governance and senior-management accountability remaining relevant.

This means an organisation cannot avoid responsibility by explaining that an outcome was produced by an AI system.

Senior management still needs to understand:

• Where AI is being used
• What risks it creates
• Who is accountable
• How customers are affected
• How outputs are tested
• Which suppliers are involved
• How failures are identified
• How decisions can be challenged

The FCA’s July 2026 Mills Review also describes a move towards more continuous and delegated AI-enabled financial services. It notes that AI may increasingly recommend actions, initiate transactions and execute decisions within agreed parameters, while potentially amplifying fraud, cybersecurity and consumer-harm risks.

How to Introduce AI Safely

Financial organisations should take a structured approach.

Begin With a Specific Business Problem

Avoid beginning with the question:

“How can we use AI?”

Start with:

“Which process is creating the greatest delay, cost or risk?”

A suitable early use case should have:

• A clear objective
• An identified owner
• Measurable outcomes
• Suitable information
• Defined boundaries
• Manageable consequences if it fails

Internal productivity tasks may provide a safer starting point than automated customer decisions.

Create an AI Inventory

The organisation should record every approved AI system.

The inventory should include:

• The system name
• Its purpose
• Business owner
• Supplier
• Information processed
• Connected applications
• User groups
• Decision-making role
• Risk classification
• Review date
• Exit process

This helps prevent unapproved or forgotten systems from remaining active.

Establish Governance and Accountability

Every AI use case should have an accountable business owner.

The owner should understand:

• What the system does
• What information it accesses
• What decisions it influences
• How accuracy is measured
• What happens when it fails
• Which employees review its outputs
• Who approves changes
• When the system will be reviewed

High-risk projects may also require input from IT, cybersecurity, compliance, legal, HR and data-protection teams.

Classify the Information

Before connecting AI to financial data, identify which information it will process.

Consider whether it contains:

• Personal information
• Special-category data
• Payment information
• Customer financial records
• Confidential business information
• Regulatory records
• Employee data
• Commercially sensitive information

The business should restrict the system to the minimum information required.

An AI platform should not be given access to an entire document library simply because configuring narrower permissions takes more work.

Apply Human Oversight

Human approval should be retained for high-impact actions.

This may include:

• Credit decisions
• Claim rejections
• Payment changes
• Customer complaints
• Investment recommendations
• Fraud accusations
• Employee decisions
• Regulatory submissions
• Publication of financial information
• Changes to customer accounts

The person reviewing the result must receive enough information to make an informed decision.

Test Before Wider Deployment

An AI system should be tested using realistic scenarios.

Testing should include:

• Correct information
• Missing information
• Conflicting documents
• Unusual customers
• Poor-quality scans
• Incorrect user instructions
• Attempts to access restricted data
• Unexpected system failures
• Biased or misleading inputs
• Cybersecurity attacks

Testing should not focus only on whether the system works in normal conditions.

It should also examine whether it fails safely.

Complete Supplier Due Diligence

Before choosing an AI supplier, ask:

• Where is information stored?
• Is customer data used for training?
• How is information encrypted?
• Which employees can access it?
• Which subcontractors are involved?
• How are security incidents reported?
• What audit information is available?
• How are model changes communicated?
• Can our information be deleted?
• How can we move to another provider?
• What happens if the service closes?

A well-known supplier should not automatically be assumed to meet every financial-services requirement.

Secure the Wider IT Environment

AI security depends on the systems around it.

Businesses should strengthen:

• Identity management
• Multi-factor authentication
• Conditional Access
• Device security
• Patch management
• Email protection
• Network security
• Data-loss prevention
• Information classification
• Backup and recovery
• Logging and monitoring

A secure AI platform can still expose information when the underlying user permissions are too broad.

Train Employees

Employees should understand:

• Which AI tools are approved
• What information may be entered
• How outputs should be checked
• When human approval is required
• How to report an error
• How to identify suspicious AI-generated content
• Who is accountable for the final decision
• When AI should not be used

Training should be specific to the employee’s role.

A compliance officer, adviser, finance employee and customer-service agent will have different risks and responsibilities.

Create an AI Usage Policy

The policy should explain:

• Approved systems
• Prohibited information
• Acceptable use
• Review requirements
• Customer communication
• Data protection
• Copyright
• Record keeping
• Supplier approval
• Incident reporting
• Accountability
• Consequences of misuse

The policy should use practical examples rather than vague instructions to use AI responsibly.

Monitor Results

AI performance should be reviewed throughout its use.

Measures may include:

• Time saved
• Accuracy
• False-positive rates
• Customer complaints
• Employee corrections
• Biased outcomes
• Security incidents
• Processing costs
• Service availability
• Regulatory issues

A system that worked well during a pilot may become less reliable as information, customers or models change.

Regular review is therefore essential.

Is AI Right for Every Financial Organisation?

Most financial organisations are likely to find some useful applications for AI.

However, not every process should be automated.

AI may be suitable when the task is:

• Repetitive
• Information-heavy
• Based on clear boundaries
• Easy to review
• Supported by reliable data
• Low risk when it fails

It may be less suitable when:

• The decision has serious consequences
• Source information is poor
• The process changes frequently
• The outcome cannot be explained
• Human judgement is essential
• Suitable oversight is unavailable

The objective should not be maximum automation.

It should be responsible improvement.

AI Needs Strong IT Foundations

Artificial intelligence will not correct an insecure or disorganised technology environment.

Before adopting AI, financial organisations should make sure they have:

• Secure user accounts
• Appropriate permissions
• Managed devices
• Current software
• Reliable backups
• Accurate information
• Clear data ownership
• Security monitoring
• Tested incident response
• Suitable IT documentation

AI can amplify a well-managed organisation’s capabilities.

It can also amplify weaknesses that have been overlooked.

How Hamilton Group Can Help

Hamilton Group helps financial-services organisations introduce AI securely and practically.

We can review your Microsoft 365 environment, cybersecurity controls, information permissions and proposed AI use cases.

Our services can include:

• AI readiness assessments
• Microsoft 365 Copilot
• AI governance support
• Microsoft 365 security reviews
• SharePoint and OneDrive permission reviews
• Multi-factor authentication
• Conditional Access
• Microsoft Purview guidance
• Sensitivity labels
• Data-loss prevention
• Managed endpoint protection
• Security patching
• Cloud backups
• Cybersecurity monitoring
• AI usage policies
• Employee training
• Incident response planning
• Managed IT support

We can help your organisation identify suitable use cases, select the right technology and apply the controls needed to protect customers, employees and confidential financial information.

At Hamilton Group, we aim to make first contact on IT support requests within 15 minutes, helping your employees receive assistance quickly when a technology or security problem occurs.

Call Hamilton Group today on 0330 043 0069 to discuss how your financial-services organisation can use artificial intelligence securely, responsibly and productively.

This article provides general information and should not be treated as financial, legal, regulatory or data-protection advice. Organisations should obtain appropriate professional guidance for their specific use of AI.