AI in fintech: Use cases of Artificial Intelligence in financial services

Fintech has always depended on software. AI changes what that software can do.

A payment platform can score transactions for fraud before approving them. A lender can examine more signals during credit scoring. An investment app can summarize portfolio information. Customer support systems can interpret questions, retrieve account information, and prepare responses. AI agents can carry out longer financial operations across several systems.

These are no longer isolated experiments.

McKinsey reported that the global fintech industry generated about $650 billion in revenue in 2025, up roughly 21% year over year, while AI-enabled fintech firms emerged as one of the forces shaping the sector’s next stage.

AI adoption is also moving from proofs of concept into production. Gartner’s 2026 banking research says spending on generative AI continues to rise, although measurable returns remain uneven. Only 38% of banking organizations reported financial gains from AI, according to its June 2026 research.

The important question is no longer whether fintech companies can use artificial intelligence. It is where AI produces enough business benefit to justify its cost, risk, and operating requirements.

Avenga’s AI services cover AI engineering, governance, data, model integration, and production systems for regulated industries.

Artificial Intelligence in fintech industry: Key takeaways

• AI in fintech already covers core financial workflows. Fraud detection, credit scoring, customer support, analytics, underwriting, investment management, and compliance are common areas.
• Machine learning and generative AI serve different purposes. Machine learning is strong at prediction and classification, while generative AI works well with language, documents, and conversational interfaces.
• Fraud remains one of the clearest AI use cases. Financial institutions combine rules, machine learning, behavioral data, and other signals to identify suspicious transactions.
• AI agents add autonomy. They can coordinate several steps across financial operations, but that increases governance and audit requirements.
• AI adoption does not equal business return. Financial institutions need defined metrics for cost, risk, revenue, customer experience, and decision quality.
• Regulation and governance matter from the beginning. Data quality, privacy, explainability, model risk, and accountability need to sit inside the technical design.

What is AI in fintech?

AI in fintech means applying artificial intelligence to financial technology products, operations, and decisions.

The term includes several AI technologies:

• Machine learning
• Generative AI
• Natural language processing
• Predictive analytics
• Computer vision
• Conversational AI
• AI agents
• Deep learning

Artificial intelligence in fintech can be part of the customer-facing product or work behind the scenes.

A consumer may interact directly with an AI assistant inside a banking app. The same financial institution might use machine learning in the background for fraud prevention, credit risk, or transaction monitoring.

This is why the role of AI extends across both product and operational functions.

Use cases of AI in fintech

There is no single dominant use case. The strongest applications usually start with a specific financial problem and enough reliable data to evaluate the result.

Fraud detection and fraud prevention

Fraud detection is one of the longest-running applications of machine learning in financial services.

AI analyzes large volumes of transaction and behavioral data to identify activity that differs from expected patterns.

Signals can include:

• Transaction amount
• Merchant
• Device
• Location
• Account history
• Login behavior
• Transaction sequence
• Network relationships

Financial institutions rarely depend on one AI model.

Gartner’s 2026 fraud research found that higher-performing banks combine business rules, machine learning models, behavioral biometrics, device intelligence, and shared threat intelligence. It also found that more than one-third of banks detect fewer than 60% of fraudulent transactions before losses occur.

This layered approach can improve fraud prevention without forcing every unusual transaction into manual review.

Avenga has also examined AI fraud detection in banking, including behavioral analysis, transaction scoring, and human review.

Credit scoring and underwriting

Traditional credit scoring depends heavily on predefined variables and credit history.

AI models can examine larger datasets and more complex relationships between variables.

Fintech companies use machine learning to support:

• Default prediction
• Credit risk segmentation
• Underwriting
• Affordability assessment
• Limit decisions

This does not remove the need for explainability.

A lender still needs to understand how a decision was reached, especially when an AI-driven decision affects access to credit.

Explainable AI becomes particularly important where regulation requires financial institutions to justify decisions to customers or regulators.

Customer support and conversational AI

Chatbots have been part of digital banking for years.

Generative AI changes what they can handle.

Modern AI assistants can interpret more natural questions, retrieve information from several sources, summarize documents, and prepare contextual responses.

Potential applications include:

• Account questions
• Transaction explanations
• Product information
• Payment support
• Internal employee assistance
• Financial education

Gartner reported in March 2026 that customer appetite for generative AI banking features remains mixed, even while digital customer experience continues to influence switching between providers.

That makes customer experience a better starting point than simply adding an AI interface because competitors have one.

Personalized financial products

AI can help financial institutions analyze customer data and identify patterns related to spending, saving, investing, or borrowing.

Possible personalized financial services include:

• Savings recommendations
• Spending alerts
• Investment suggestions
• Product recommendations
• Cash-flow projections

The benefit depends heavily on consent, data quality, and how the recommendation is presented.

Personalization should not become opaque financial steering.

Risk management

AI in financial services is increasingly used to support risk teams.

Machine learning can identify patterns across:

• Credit risk
• Market risk
• Operational risk
• Fraud
• Financial crime
• Portfolio exposure

Generative AI can also help analysts summarize reports, policies, incidents, and model documentation.

McKinsey reported in July 2026 that banks are adapting model risk management as machine learning, large language models, and agentic AI become part of more financial processes. Fewer than 30% of surveyed European banks had incorporated generative and agentic AI models into their model risk management approaches.

Financial operations and automation

AI can automate parts of back-office financial operations.

Examples include:

• Document classification
• Invoice processing
• Reconciliation
• Data entry
• Compliance checks
• Reporting
• Exception handling

This type of automation is often less visible to customers but can remove repeated manual work.

The strongest use cases still keep people involved where financial judgment or accountability is required.

Generative AI in fintech

Generative AI works particularly well with unstructured information.

Financial institutions process large volumes of text:

• Contracts
• Policies
• Research
• Customer correspondence
• Transaction descriptions
• Regulatory documents
• Analyst reports

Gen AI can summarize, classify, search, or generate text from these sources.

A banking employee might ask an AI assistant to gather relevant documents for a case. An investment analyst might use it to summarize research. A compliance team might use it to compare policy language.

Gartner expects more than 80% of banks to have adopted generative AI during 2026, though its newer research also stresses that ROI remains difficult to prove.

This distinction matters.

Adoption measures activity. It does not measure business return.

Agentic AI and the next stage of fintech automation

Agentic AI goes further than a chatbot responding to a prompt.

AI agents can perform sequences of actions toward a defined goal.

For example, an agent could:

1. Receive a disputed transaction case.
2. Retrieve the account history.
3. Examine relevant transaction data.
4. Check internal fraud rules.
5. Prepare an investigation summary.
6. Route the case to the appropriate employee.

The attraction is clear: fewer manual handoffs.

The risk is equally clear: the system is making more decisions and taking more actions.

Deloitte’s 2026 work on agentic AI in banking argues that controls, evidence, governance, and accountability need to be designed before autonomous workflows reach scale.

AI agents therefore increase the importance of architecture rather than reducing it.

Avenga’s agentic AI services cover agent design, integration, governance, and production deployment.

Build AI for financial workflows with governance, data, and engineering considered from the start.

Learn more

Benefits of AI in fintech

The benefits of AI depend on the use case.

Faster analysis

AI can process more transactions, documents, and customer interactions than a human team can examine manually.

More automation

Fintech firms can automate repetitive work while routing exceptions to people.

Better fraud detection

Machine learning can identify combinations of signals that simple rules may miss.

More responsive customer experience

AI assistants can support customers outside traditional service hours and help employees find information faster.

More consistent decision support

AI provides a repeatable method for analyzing defined inputs, provided the underlying model and data remain suitable.

Gartner’s May 2026 research found that 66% of finance organizations using AI cited productivity and efficiency as a leading benefit. At the same time, 63% said AI implementation had taken longer than expected during 2025.

The benefits of AI in fintech are therefore real, but implementation effort should not be underestimated.

Risks of using AI in financial services

Financial services combines sensitive data, regulated decisions, money movement, and cyber risk.

That makes careless AI implementation particularly expensive.

Bias

AI algorithms can reproduce bias present in historical financial data.

Credit scoring is an obvious example.

Teams need to test whether model behavior differs unfairly across relevant customer groups.

Weak explainability

A highly accurate model may still be unsuitable if nobody can explain why it made a decision.

This is especially important for lending, risk management, and compliance.

Data privacy

AI systems may process financial data, customer data, identity information, or transaction histories.

Access and retention rules should be explicit.

Hallucination

Generative AI can produce confident but incorrect answers.

A financial assistant cannot be treated as authoritative simply because its response sounds plausible.

Model risk

Models change.

Data changes.

External model providers change their systems.

Fintech companies must monitor AI systems after deployment, not only during development.

Deloitte’s 2026 regulatory outlook found that 94% of surveyed financial services firms planned to increase AI investment, while AI risk management and regulatory obligations remained two of the leading barriers to return.

How fintech companies can integrate AI

Successful AI implementation usually begins with a narrow business problem.

Choose the use case

Define what needs to change.

Fraud losses? Review time? Customer support volume? Credit decision speed?

Measure the baseline

Record current performance before introducing AI.

Assess the data

An AI solution cannot compensate indefinitely for incomplete or unreliable financial data.

Define human responsibility

Decide which outputs can be automated and which require review.

Build governance early

Responsible AI should cover data, models, access, security, bias, auditability, and accountability.

Measure production results

Track the business result after launch.

Gartner’s June 2026 survey found that 84% of finance organizations had implemented or planned to implement AI, but only 7% reported high or very high business impact.

This is a useful reminder for fintech organizations: AI implementation is not the finish line.

AI in financial services has to earn trust at the same time it earns a return. A model can be technically impressive, but if the institution cannot explain its decisions, monitor its behavior, or connect it to a measurable financial result, it is not ready to sit inside a critical financial process.

Richard Hairsine, VP of BFSI at Avenga

Richard Hairsine currently leads BFSI work at Avenga and has more than 20 years of experience across financial services and consulting.

The future of financial services with AI

AI is transforming fintech, but the next phase is unlikely to be defined by adding more standalone AI tools.

The larger shift is toward AI embedded inside existing financial workflows.

Fraud teams will use AI during investigations. Credit teams will use models during underwriting. Customer-service employees will work with AI assistants. Software teams will embed AI features directly into fintech software. AI agents will handle defined sequences across financial operations.

The fintech and AI relationship is also moving toward greater scrutiny.

The fintech market has matured, and financial institutions increasingly need AI systems that can satisfy both commercial and regulatory requirements.

AI is reshaping the technology layer. It is not removing the need for financial judgment.

FAQ

What is the role of AI in fintech?

AI in fintech helps financial institutions analyze data, detect fraud, assess risk, automate work, support customers, and improve financial products. Machine learning, generative AI, predictive analytics, and AI agents support different parts of these processes.

Does fintech involve AI?

Yes. Many fintech companies use AI for fraud detection, credit scoring, customer support, predictive analytics, investment tools, compliance, and automation. AI is one technology within financial technology rather than a requirement for every fintech product.

What is the 30% rule in AI?

There is no standard or formally recognized u002230% rule in AIu0022 for fintech or financial services. The phrase appears in different business discussions with different meanings, so any claim using it should be checked against its original source rather than treated as an accepted AI principle.

Which three jobs will survive AI?

There is no credible basis for claiming that exactly three jobs will survive AI. In financial services, roles involving accountability, regulation, human relationships, domain judgment, and oversight are likely to continue changing alongside AI rather than disappearing according to a fixed list.

Conclusion: Fintech AI needs a business case, not just a model

AI is revolutionizing some parts of financial technology, but the useful question is more specific: which financial problem can AI solve better than the current approach?

Fraud detection has a clear metric. Credit scoring can be evaluated against repayment performance and fairness requirements. Customer support can be measured through resolution time and satisfaction. Automation can be measured through cost, error rates, and processing time.

That is where AI development should begin.

Financial institutions should define the use case, measure the current process, build appropriate controls, and test the result in production.

AI for fintech becomes valuable when models, data, software, governance, and human responsibility work as one system.

For fintech companies and financial institutions planning AI development, agentic systems, fraud platforms, or other AI-driven financial products, contact Avenga to discuss the engineering requirements.