Banks have used scoring models for credit risk management for more than half a century, and card networks have run machine learning–based fraud detection models for years. What's changed is the scope. Artificial intelligence (AI) models now handle unstructured data, generate human-readable outputs, and operate across workflows that previously required experienced human judgment. AI in financial technology, or fintech, refers to the use of machine learning models, large language models (LLMs), and statistical pattern recognition to power financial products, services, and infrastructure. The shift is changing how financial products are built, priced, and delivered.

Below, we’ll discuss the core use cases for AI in fintech, where payments and fraud detection fit in, and what data, security, and regulatory constraints you need to account for.

Highlights

• AI in fintech spans three distinct categories: predictive models, generative models, and decision automation systems. Each model is built, evaluated, and regulated differently.

• Fraud detection is one important application. Network-level machine learning models can identify coordinated fraud patterns that no single business could catch on its own.

• Successful AI adoption in financial services depends on data quality, governance structure, and an astute assessment of where a proprietary build creates advantage.

What is AI in fintech?

AI in fintech spans a wide range of applications from fraud models that flag suspicious card transactions to underwriting systems that can price a loan without requiring a human reviewer. AI augments financial systems by helping institutions make faster, more accurate decisions at scale.

How does AI in fintech work?

The technical details vary by application, but AI systems in financial services tend to follow a similar architecture. They use various models to score or predict new events in real time.

Here are three common examples.

Fraud detection

A payment provider collects transaction signals such as purchase amount, merchant category code, device fingerprint, location, transaction speed, and account age. These signals feed into a model trained on hundreds of millions (or billions) of historical transactions, including confirmed cases of fraud. The model then produces a risk score. A decision engine uses that score to determine whether to approve the transaction, decline it, or route it to manual review, typically within the card authorization window.

Credit underwriting

Lenders combine credit bureau data with other signals such as bank transaction history and borrower behavior during the application process. Models such as neural networks estimate the probability that a borrower will default. That prediction informs loan approval and pricing.

LLMs

LLMs can be used to automate workflows that involve reading or writing text. In financial services, they can assist with document analysis (loan applications, insurance claims, and regulatory filings), customer support, and internal research. Because LLM outputs are based on probability and hard to audit, they create new challenges in regulated environments where interpretability and accountability are necessary.

Why is AI transforming financial services?

The financial services industry is well suited to machine learning because it combines large datasets with a constant stream of decisions. Here’s a closer look at aspects of the industry that make it a good fit for AI improvements:

• Data density: Banks, lenders, and payment networks process enormous volumes of transactions, account histories, and market data. This abundance of structured information gives machine learning models unusually rich training data.

• Decision volume: Financial institutions make millions of small decisions every day, whether that’s approving transactions, evaluating loans, routing claims, or monitoring activity. Even small improvements in decision accuracy compound quickly into cost savings and revenue gains.

• Efficiency: Processes such as mortgage underwriting and claims review have historically required extensive manual work. AI-assisted workflows can minimize labor costs and turnaround times.

• Fraud prevention: Better models help reduce false positives (i.e., fewer legitimate transactions declined) and false negatives (i.e., fewer fraudulent transactions approved). Each improvement directly affects revenue, business costs, and customer trust.

• Unstructured data: Earlier automation systems worked well with structured inputs such as numbers and categories. Newer models can interpret documents, conversations, and other unstructured data, which enables automation in areas that previously required human judgment.

What are the core AI use cases across the fintech industry?

Across the fintech industry, AI tends to concentrate in a few domains where large datasets and high decision volumes make automation particularly valuable. Here are some of the main use cases.

Credit and underwriting

Traditional credit scoring relies heavily on bureau data, which excludes people who are “credit invisible” and underserves others with thin files. AI models trained on alternative data, such as bank transaction patterns, rent payments, and payroll records, can expand access to credit while maintaining accurate risk predictions.

Fraud prevention

Fraud detection is one of the more mature applications of machine learning in finance. Modern systems identify patterns such as account takeover attempts, synthetic identities, and coordinated fraud rings that static rules might struggle to detect.

Customer operations and support

LLM-powered assistants can handle many routine customer interactions, including account questions, transaction disputes, and onboarding guidance. Effective systems automate straightforward requests and escalate difficult or sensitive cases to human agents.

Regulatory compliance

Anti-Money Laundering (AML) monitoring generates a large number of alerts, many of which are false positives. Machine learning models can help distinguish genuine suspicious activity from unusual but legitimate behavior, thereby decreasing investigation workloads. AI can also accelerate Know Your Customer (KYC) processes by extracting identity data from documents.

Market analysis and risk management

AI expands the capabilities of quantitative models by analyzing unstructured information such as earnings calls, news, satellite imagery, and aggregated transaction data. This can provide additional signals for trading and risk monitoring.

How does AI apply to payments, fraud, and risk management?

Payments, fraud, and risk management are some of the most labor-intensive and consequential aspects of financial services. Payments infrastructure places some of the strictest real-time requirements on AI systems, and fraud is the most immediate cost of a wrong decision.

Here’s what businesses should be aware of when they use AI to solve problems across payments, fraud, and risk.

Payments and fraud

Fraud models that drive transaction authorization decisions must be both fast and precise. Declining legitimate purchases can frustrate customers, while missed fraud might create direct financial loss. Tools like Stripe Radar use machine learning models trained on transaction data across broad networks to score each transaction for fraud risk. This exposes the models to fraud patterns that no single business could observe on its own. Their signals can help spot a fraud ring that attacks one business in a network before it escalates. Businesses can customize these tools’ behavior with their own rules on top of the underlying model, which is continually updated as new fraud patterns emerge.

Credit and risk management

Credit outcomes depend on changing economic conditions, borrower life events, and inherent uncertainty. AI doesn't remove that uncertainty, but it can extract more predictive signals from available data. This enables more fine-tuned risk pricing. However, because credit models learn from historical data, they can reproduce existing biases if left unchecked. Responsible deployment requires fairness testing, careful feature selection, and ongoing monitoring to ensure models don’t systematically disadvantage protected groups.

What are the data, security, and regulatory considerations for AI in financial services?

Financial services AI connects several overlapping compliance regimes and constraints. Deployment and compliance will require deep knowledge of how these pieces fit together.

Keep the following in mind:

• Data privacy and residency: Financial data is highly sensitive. Regulations such as the EU’s General Data Protection Regulation (GDPR) give individuals rights related to automated decision-making, which create interpretability and transparency obligations for AI-driven credit or insurance decisions. Various US states have their own privacy laws with specific requirements.

• Model interpretability: Financial regulators often require lenders to explain adverse decisions to applicants. This is straightforward with a rule-based system, but more difficult with multifaceted machine learning models.

• Model risk management: Frameworks such as the US Federal Reserve’s SR 11-7 guidance require financial institutions to validate models, document their assumptions, and monitor performance over time. AI systems are subject to the same oversight requirements as traditional models.

• Security and adversarial risk: Machine learning systems introduce new attack surfaces. Fraudulent actors might probe fraud detection models to discover their weaknesses, while others might extract information from trained models. Securing AI infrastructure is part of broader financial cybersecurity.

• Vendor risk: Many financial institutions license AI models from third-party vendors. Effective risk management requires understanding how those models were trained, how they’re monitored, and what safeguards are in place if they fail.

Is your organization ready to adopt AI in financial services?

Successfully adopting AI depends largely on your organization’s data quality and governance. Before you begin, you should have realistic expectations of how it will help and how it can be applied.

These are the steps to adoption:

• Start with data: Improve your data infrastructure. AI systems are only as reliable as the data they're trained on. Poorly labeled transactions, fragmented customer records, or biased historical decisions will produce flawed models.

• Establish governance: You must define clear ownership for model performance and oversight. Teams should know who monitors model drift, evaluates fairness, and responds when systems behave unexpectedly.

• Decide whether to build or buy: Building proprietary models makes sense when a company has unique data or specialized needs. Buying or licensing models is often faster when the problem is common and well understood.

• Try a narrow use case first: Rather than attempt a broad AI transformation, you can begin with one high-volume decision process where you have clear historical data and success metrics. Running models in parallel with existing workflows helps measure performance and build confidence.

• Use existing infrastructure: Stripe's infrastructure, including Radar for fraud and the data signals available through the Stripe application programming interface (API), gives businesses a starting point that doesn't require building everything from zero. Every organization must decide where to apply AI and how to govern and monitor it, but this kind of tool can handle payments and fraud.

How Stripe Payments can help

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