Top AI use cases in financial services

If you’re a bank, insurer, or fintech, you’re probably under pressure to "do something with AI." Board decks are full of pilot proposals, vendors are lining up with demos, and somewhere in your organization, someone is building a chatbot that will never see production.

The gap between AI experimentation and AI value is wider in financial services than almost any other industry. Most pilot projects stall because of regulated environments, legacy infrastructure, and risk-averse cultures.

Meanwhile, a handful of institutions are quietly generating billions in value from AI systems that actually work. Gartner found in 2024 that 58% of finance functions already use AI, which was 56% year-over-year growth. Companies are adopting the tech, but adoption isn't the same as ROI.

We've tracked the use cases that are actually driving returns in financial services. Here's what separates the wins from the hype.

Why AI in finance is important

Before we get to the use cases, you should know that there are three forces pushing AI from "nice to have" to "we needed this yesterday":

Fraudsters evolve faster than your rules engine. In early 2024, a finance worker in Hong Kong transferred $25 million after a video call with what appeared to be his company's CFO and colleagues, but all of them were deepfakes. GenAI-enabled fraud losses are on track to reach $40 billion by 2027 in the U.S., a 32% compound annual growth rate. This is an arms race, and rule-based fraud detection can't keep up with attackers who generate new tactics daily.

Customer expectations have shifted. Customers compare your digital experience to the best apps they use daily, not to other banks. When their food delivery app knows what they want before they do, they start to expect the same from their bank.

Compliance costs keep climbing. Smaller community banks devote 11 to 15% of payroll costs to compliance, nearly double what larger institutions spend. At the same time, an IIF-EY Survey shows 100% of surveyed financial institutions increased their AI investment in 2024. Everyone's moving use AI because they have to.

With that heads-up out of the way, let’s get to the use cases.

Use case 1: Fraud detection & prevention

Think about how annoying it is when your credit card gets declined for a legitimate purchase. Now multiply that frustration across 40 million customer accounts. HSBC achieved a 60% reduction in those false positives while actually improving suspicious activity detection by two to four times. For context, they were monitoring 900 million transactions monthly.

That false positive reduction also translates to productivity gains for analysts and fewer angry customer calls. JPMorgan Chase generated nearly $1.5 billion in cumulative savings through fraud prevention, personalization, trading, and operational efficiencies combined.

Use case 2: Customer support & virtual assistance

Most customer service inquiries are simple requests like balance checks, transaction histories, or payment status questions. But each one costs money. Industry research put the average cost of a human-handled banking call at around $8, while digital self-service interactions cost pennies. Multiply that across millions of monthly inquiries, and you're looking at a massive operational expense for questions that don't really need a human.

That's the business case for virtual assistants. Bank of America's Erica proves virtual assistants work at scale. Since launch, the bot has helped nearly 50 million people across 3 billion interactions. It now averages 58 million interactions per month

But the technology isn't the hard part. A survey of 2,027 banking customers found that 37% have never interacted with banking chatbots, and only 27% trust AI for financial advice. The pattern we've seen is that chatbots work better for questions and insights than transactions.

Use case 3: Faster & fairer credit decisions

Traditional credit scoring leaves money on the table. Thin-file applicants (people with limited credit history) get rejected even when they're creditworthy. Manual underwriting takes days, and by then, the customer has gone to a company that approved them in minutes. Meanwhile, your default rates on approved loans suggest your models aren't as predictive as they could be.

That's the opportunity. Peer-reviewed research confirms that machine learning credit scoring models outperform traditional models when it comes to predicting borrowers' losses and defaults. Gradient Boosting, for example, achieved 88% accuracy in risk assessment tests, which puts it ahead of traditional methods which typically performs closer to 81% in similar studies.

Before we get carried away, remember that implementing something like this requires solid model risk management. There’s strict federal oversight in place, and the Federal Reserve, OCC, and CFPB are all conducting reviews focused on AI use. You have to build fairness monitoring and explainability frameworks from day one, not as an afterthought.

Use case 4: Compliance & regulation

When the 2008 financial crisis hit, regulators responded with the most complex financial legislation in American history. Since then, the rules have kept coming, even as banks scramble to comply with the ones already on the books.

The result is that compliance costs for banks have increased by more than 60% compared to pre-crisis levels. Employee hours dedicated to compliance grew 61% between 2016 and 2023. Nearly half of management and board time now goes to regulatory matters rather than strategy. It's even worse for small banks because fixed compliance costs don't scale, so they're spending a larger share of payroll just to keep up.

That’s the bad news. The good news is that there are multiple places where institutions can benefit from using AI to streamline their compliance and regulation processes:

• Industry implementations show that AI-driven name screening achieves a 50% reduction in false positives, with some providers reporting even higher gains with advanced matching techniques
• Advanced NLP shows higher precision, with BERT-based models hitting 93.2% precision when classifying regulatory risks
• Regulators are on board, meaning financial institutions may use new approaches to meet compliance obligations

The risk mitigation value is massive. Every false negative you catch before regulators do is money saved.

Use case 5: AI-driven personalization

Your customers get personalized recommendations from Netflix, Spotify, and Amazon every day. Then they open your banking app and see the same generic product offers everyone else sees. That gap is costing you money.

Personalization isn't just about better UX. When a customer who just paid off their car loan sees a generic credit card offer, that's a missed opportunity. But they’re much more likely to convert if they see a tailored offer for an auto savings account or their next vehicle purchase. The same logic applies across the board: mortgage refinancing for homeowners when rates drop, investment products for customers with growing balances, or insurance bundles that actually match their life stage.

Banks implementing AI-driven personalization can expect to see improvements across the board: higher cross-selling conversion, better customer retention, and increased product adoption. Mobile apps with personalized recommendations can expect to see significantly higher click-through rates than generic offers.

The infrastructure behind high-performance finance AI

Look at these use cases, and you'll spot the pattern: real-time infrastructure powers every single one. HSBC's fraud detection system monitors 900 million transactions monthly across 40 million accounts. Bank of America's Erica handles 58 million interactions every month.

Batch-processing infrastructure that runs on hourly or daily cycles can't compete. Latency represents a critical failure mode for enterprise AI, and you don’t get to optimize it somewhere down the line.

The use cases we went over all share common infrastructure requirements:

• Sub-millisecond data access for fraud scoring and session management
• Vector search for AI-powered recommendations and semantic matching
• High-throughput ingestion for transaction monitoring and compliance screening
• Always-on availability because downtime means missed fraud, lost customers, and regulatory exposure

Redis handles all of this in a single product. Financial institutions use Redis for real-time fraud detection. And for AI-driven personalization, Redis' vector database delivers sub-10ms similarity search across millions of customer profiles and product embeddings.

Redis’ session management capabilities that power conversational AI assistants can also handle the authentication and user context your compliance systems need. And Active-Active Geo Distribution means your fraud detection doesn't go down when a region fails, which can be critical for institutions operating across time zones.

If you're evaluating real-time infrastructure for your AI stack, try Redis free or book a demo to see how it handles your specific use case.

What to do next

Let’s talk next steps. The way we see it, you have three priorities:

• Start with use cases where you've got clean data and clear success metrics
• Build model risk management and fairness monitoring frameworks from day one
• Test vendor claims before you roll out new systems

We recommend picking one use case where you've got clean data and clear metrics, deploying a pilot with rigorous measurement, and scale what works. The financial institutions already using AI aren't waiting for perfect conditions. Neither should you.