AI in financial services means using machine learning, language models and other AI tools to help banks, fintechs and payment firms spot fraud, check customers, review transactions and serve clients faster. It works inside a firm’s controls. It does not replace them.
At PRETpayments, we build U.S. banking infrastructure for global fintechs, MSBs, wallet platforms and stablecoin companies. Sponsor banks need to see and approve what happens in every program. That shapes how we read each new AI tool and each new AI rule. This guide covers where AI works, where it needs guardrails and what changed in 2026.
Key takeaways
- AI is now common in financial services. A Bank of England and FCA survey found 75% of firms already used it.
- The strongest uses are fraud detection, AML monitoring, credit decisions, customer service and document work.
- Treasury released a Financial Services AI Risk Management Framework in February 2026.
- Criminals use generative AI too, so identity checks need layered defenses.
What Is AI in Financial Services?
AI in financial services is the use of software that learns from data to support financial decisions and tasks. Four types do most of the work:
- Machine learning finds patterns in data. Firms use it to score fraud risk and spot odd account activity.
- Natural language processing reads and sorts text, such as documents, emails and chat messages.
- Generative AI drafts, summarizes and answers questions in plain language.
- Agentic AI links several steps together and takes actions with tools, usually under set limits.
Each type needs data, testing and an owner. The Bank of England and FCA define AI broadly in their 2024 survey. It covers any computer system that learns, decides or solves problems in ways that once needed people.
AI Applications in Financial Services
AI applications in financial services cluster around a few high-volume jobs. These jobs involve large data sets and many repeat decisions. The Bank of England and FCA found that operations and IT account for about 22% of all reported use cases, the largest share in their 2024 survey.
| Application | What AI does | Control to keep |
| Fraud detection | Scores transactions and logins for risk in real time | Human review of edge cases |
| AML monitoring | Ranks alerts and links related accounts | Clear audit trail for each decision |
| Identity checks | Flags altered documents and odd sign-up patterns | Layered verification, not one test |
| Credit decisions | Estimates risk from many data points | Explainable reasons for each outcome |
| Customer service | Answers routine questions and routes complex ones | Easy hand-off to a person |
| Document work | Reads forms, contracts and filings | Spot checks against the source |
Fraud detection and identity checks
Fraud teams use AI to score a payment or login in the moment. The model looks at account history, device and location, then returns a risk score. A rule or a person decides what happens next.
The same technology helps attackers. FinCEN issued alert FIN-2024-Alert004 on November 13, 2024. It said suspicious activity reports describing suspected deepfake media had increased since 2023. FinCEN noted that criminals alter or create fake identity documents to get past verification checks.
AML and transaction monitoring
Rule-based monitoring tends to flag a lot of harmless activity. AI can help rank alerts so analysts see the riskiest ones first. Regulators have been open to this for years. In a December 2018 joint statement, FinCEN and the federal banking agencies encouraged banks to consider and responsibly implement innovative approaches to BSA/AML. They noted that some banks were testing AI and digital identity tools.
The duty to report suspicious activity stays with the institution. AI can inform the decision. It cannot take over the responsibility.
Credit and risk decisions
Lenders use AI to estimate repayment risk from many signals. The hard part is explaining the result. The EU AI Act treats credit scoring as a high-risk use. Its high-risk deadline for stand-alone systems has moved from August 2, 2026 to December 2, 2027, under the final Digital Omnibus on AI, as Freshfields. The rules are delayed, not dropped.
Customer service and document work
Chat assistants handle routine questions. Document tools read forms and pull out key fields. Both free staff for work that needs judgment. Both need a clear path to a person when the tool is unsure.
AI and Automation in Financial Services
AI and automation in financial services are related, but they are not the same thing. Automation follows rules a person writes. AI learns patterns from data. Most firms use both. Automation moves a task along. AI helps decide which tasks need attention first.
In our model, compliance and bank visibility cover KYC/KYB, AML/BSA workflows, sanctions screening, transaction monitoring, RFI handling, reporting and audit trails. Automation makes these steps faster. It does not remove the need for the sponsor bank to see the full record. That is why we structure approved programs around bank sponsorship and clear program-level controls.
Automation also matters in money movement. Pay-ins, payouts and settlement follow set steps, and cross-border payment flows add checks at each stage. Firms that connect through a global payment gateway still need records that a bank can review.
Keep people in the loop
The Bank of England and FCA found that 84% of firms using AI had an accountable person for their AI framework. That is a sound baseline. Name an owner for each model. Define when a person must review the output. Log what the model did and why.
Where AI in Financial Services Stands in 2026
Three developments define the year.
Adoption is broad, and payments firms trail.
The Bank of England and FCA reported that 75% of surveyed firms already used AI. Another 10% planned to within three years. Financial market infrastructure, payments and related firms had the lowest current use at 57%. That is the most recent survey I could verify. It was published November 21, 2024.
The U.S. now has a sector framework.
On February 19, 2026, Treasury released an AI Lexicon and the Financial Services AI Risk Management Framework. Treasury said the framework adapts the NIST AI Risk Management Framework to financial services. It helps institutions evaluate AI use cases and manage risk across the AI lifecycle. The Treasury announcement points to the full deliverables from the Financial Services Sector Coordinating Council. Law firm analyses describe it as voluntary guidance rather than regulation. It keeps NIST’s four functions: Govern, Map, Measure and Manage, according to Taft.
The EU set a later date for high-risk rules. As noted above, credit scoring obligations now apply from December 2, 2027.
Real-World Examples of AI in Financial Services
U.S. Treasury payment integrity
Treasury’s Office of Payment Integrity reported that it prevented and recovered over $4 billion in fraud and improper payments in fiscal year 2024. That compares with $652.7 million in fiscal 2023. Treasury credited a mix of steps. It attributed $1 billion in recoveries to using machine learning AI to identify Treasury check fraud faster. The $4 billion total is not all AI. The Treasury release lists each source of savings.
Card network fraud scoring
Mastercard said its Decision Intelligence service scores 143 billion transactions a year for banks. In a February 2024 press release, the company said its initial modeling showed generative AI could raise fraud detection rates by 20% on average and by as much as 300% in some cases. These are the company’s own modeling results, not an independent study. Treat them as a sign of direction, not a benchmark.
What these examples show?
AI pays off where data is plentiful and decisions are frequent. Even so, the Treasury example mixes AI with screening and process changes. The better lesson is that AI works as part of a wider control system.
Risks to Manage Before You Scale AI
- Explainability. You should be able to say why a model flagged or declined something. Regulators and sponsor banks will ask.
- Bias and fairness. Models learn from past data, which can carry past gaps. Test outputs regularly.
- Third-party exposure. Many firms buy AI from vendors. You still own the outcome.
- Data security. AI tools handle sensitive customer and transaction data. Limit access and log use.
- AI-enabled fraud. Deepfakes and fake documents target onboarding. Use more than one verification layer.
- Over-reliance. A confident wrong answer is still wrong. Keep human review for high-impact decisions.
How We Think About AI at PRETpayments
We are an orchestration layer between sponsor banks, customer programs, payment vendors, liquidity providers and compliance partners. Our job is to keep the bank informed, the program controlled and the customer moving. We are not a card acquirer, a checkout product or a generic payment processor.
That frame guides how we judge any AI tool. A tool should make activity easier for a bank to see, not harder. Sponsor banks stay in control. If a tool cannot show its work, it does not belong in a regulated program.
A Practical Checklist for Evaluating AI Tools
Use these questions before you adopt a tool. They follow the Govern, Map, Measure, Manage structure.
- Govern: Who owns this model, and who signs off on changes?
- Map: What decision does it influence, and who is affected?
- Measure: How do we test accuracy, bias and drift, and how often?
- Manage: What happens when the model is wrong or unavailable?
- Visibility: Can our sponsor bank see inputs, outputs and decisions?
- Records: Do we keep an audit trail for every automated action?
Frequently Asked Questions
1. What is AI in financial services?
It is the use of machine learning, language models and related tools to support tasks such as fraud detection, AML monitoring, credit decisions, customer service and document processing.
2. What are the main AI applications in financial services?
The most common are fraud detection, AML and transaction monitoring, identity verification, credit and risk scoring, customer service assistants and document processing.
3. How do AI and automation differ in financial services?
Automation runs fixed rules. AI learns patterns from data and can rank, predict or summarize. Most workflows combine the two.
4. Is AI in financial services regulated?
Existing rules still apply to the activity, including Bank Secrecy Act reporting duties. Treasury’s 2026 Financial Services AI Risk Management Framework is voluntary guidance. In the EU, credit scoring is a high-risk AI use, with obligations now due from December 2, 2027.
5. How are criminals using AI against financial institutions?
FinCEN reported a rise in suspicious activity reports describing deepfake media used to alter or create identity documents and get past verification checks.
6. Does AI replace compliance teams?
No. Institutions remain responsible for their decisions and reports. AI can help prioritize work, and people review and own the outcome.
7. How should a fintech start with AI?
Pick one narrow use case. Name an accountable owner. Agree the controls with your sponsor bank. Measure results, and expand only when the data supports it.
Ready to build with sponsor-bank visibility?
Planning a fintech, wallet, MSB or stablecoin program that needs U.S. banking access? Our team can walk you through how we structure approved programs. You can reach us through our contact page, or begin with our corporate application.