Ask a chief data officer at a large US bank how many production AI systems the bank runs, and the answer is likely to run into the hundreds. Those are not pilots stuck in a lab. They are live systems that already touch documents, transactions, and client work.
The use cases cluster around a handful of repeatable categories:
- Document and research copilots
- Fraud and anti-money laundering (AML) detection
- Wealth-management productivity
- Agentic workflows that reach across both structured and unstructured data.
The easiest wins came from unstructured data. The harder, higher-value work is unlocking structured transaction data that sits across dozens of silos.
The banks with the most live use cases share one trait. They solved data access first. Without that, the use-case count stalls at pilots, no matter which model sits on top.
Key takeaways
- Banks now report AI use cases numbering in the hundreds, with one major bank running nearly 1,000, and the use cases cluster into four categories, including document and research copilots, fraud and AML detection, wealth-management productivity, and agentic workflows that span structured and unstructured data.
- The easiest wins came from unstructured data, such as summarization, transcription, and research copilots, while the harder, higher-value work is unlocking structured transaction data that sits across dozens of data silos.
- The results are already measurable. For example, Khan Bank saw 400x faster query performance, a global investment bank cut money laundering losses by millions of dollars, and more than 200,000 Bank of America employees now use AI-enabled tools every day.
- Banks that unify structured and unstructured data on one platform can move a new AI use case from pilot to production in about 90 days, without a full data-estate replatform.
- Governance, data sovereignty, and audit requirements apply to every AI system from day one, so banks that build access controls and lineage into the data platform can approve new use cases faster.
Conversational copilots for unstructured data are the fastest win
The easiest AI wins in banking start with unstructured data, including documents, meeting notes, and research reports that used to need a human reader. Summarization tools condense long filings and contracts in seconds. Transcription copilots turn client meetings into searchable notes. Research copilots help analysts scan earnings calls and filings for the details that matter.
According to Banking Dive, citing Bank of America’s Q2 2026 earnings call, more than 200,000 of the bank’s employees now use AI-enabled tools. Staff send more than 400,000 prompts a day. Banking Dive also reported that Bank of America has approved more than 300 AI use cases, including 114 generative AI projects, with 34 fully live in production. Wells Fargo has taken a similar path with what it calls its “AI Teammate,” an internal assistant built to help employees work through documents and routine tasks faster.
Fraud detection, AML, and risk analytics run in real time
Fraud and risk teams were early adopters of machine learning, and the field keeps moving. Banking Dive reported that one major bank now runs nearly 1,000 live AI use cases across risk, fraud, marketing, and document reading. That is one of the largest deployed footprints at any US bank.
Real-time AML monitoring is a good example of why the count keeps climbing. A global investment bank now runs transaction monitoring that accelerates money laundering detection and prevention, helping the bank flag suspicious activity before it becomes a multi-million dollar loss. For a plainer look at how this works, see this rundown of modern standards for anti-money laundering monitoring.
Risk analytics is moving toward conversational tools, too. At Santander Corporate & Investment Banking, analysts and front-office desks now converse with multi-petabyte data lakes in natural language through a Starburst’s conversational analytics interface, AIDA. The tool turns what used to be a multi-day data pull into a question typed into a chat window.
Customer-facing productivity and wealth management gain ground
Wealth management is where AI’s productivity gains show up most directly in client relationships. Many wealth-management advisers now use an AI tool that helps them pull client data from Salesforce customer relationship management (CRM) records far faster than manual lookups allowed. That means advisers spend more time with clients and less time hunting for account history.
Citigroup describes adoption in similarly broad terms. Banking Dive reported that nearly 9 out of 10 Citigroup employees now use AI tools, with the bank tying that adoption to gains in productivity, client experience, and growth. Financial firms with this kind of reach are increasingly using AI to help teams break down data silos and power smarter decision-making. That same foundation supports informed risk mitigation, revenue-generating, and process optimization decisions across the business.
The data problem behind agentic AI
Unstructured data was the easy win because LLMs excel at breaking it down, whether it be a document repository, an email archive, a transcript store. Structured data is a harder problem. Transaction records, account histories, and risk metrics are often spread across dozens of data silos, each with its own access rules and formats.
That’s the higher-value problem banks are now working through. The next generation of AI systems needs to reach both kinds of data at once. It has to route a single question across documents and transaction tables without forcing an analyst to query each system by hand. Banks are increasingly building agentic AI systems that route queries across structured and unstructured data. That is what turns a chat interface into something that can answer questions grounded in the bank’s own data.
Modernizing the data platform to support AI at scale
None of the use cases above work well if the underlying data platform can’t keep up. Query speed, source coverage, and time-to-insight all set the ceiling on what an AI system can do.
Khan Bank saw 400x faster query performance after modernizing its data stack, a change that turned reports that used to take hours into something closer to real time. That gave analysts a wider view of the bank’s data without new pipelines for every source. US Bank took the same approach to unify data access and accelerate AI-driven analytics, giving teams one place to query data that used to live in separate systems.
Governance and regulatory pressure shape every use case
Every AI use case in banking runs inside a regulatory perimeter. Data sovereignty rules, cross-border transfer restrictions, and audit requirements apply to an AI system exactly as they apply to any other production system. Banks need to design for that from the start rather than retrofit it later.
That means governance can’t be an afterthought bolted onto a finished project. Banks need a modern, AI-ready data foundation that works across existing systems. That way, access controls, lineage, and audit trails travel with the data no matter which team or model queries it. That kind of governed, federated access that enables fast, compliant analytics and AI means a bank can say yes to a new AI use case. It doesn’t need to reopen a compliance review from scratch each time.
From pilot to production
The path from a promising pilot to a production AI use case doesn’t need a full data-estate replatform. Banks that unify structured and unstructured data on one platform can launch production-ready AI use cases in just 90 days. They work with the systems they already run rather than ripping them out.
BNY frames this as more than a cost-saving exercise. Banking Dive reported that BNY’s leadership sees AI as a source of long-term value, tied to new products, new capabilities, and the platform data that makes both possible. That’s the common thread across every bank in this article. The ones with the most live AI use cases are the ones that solved data access first, before they worried about which model to run on top of it.
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FAQs
What are the most common AI use cases banks are deploying today?
Banks cluster their AI use cases into four groups: document and research copilots, fraud and anti-money laundering detection, wealth-management tools, and agentic workflows. Document and research copilots summarize filings and transcribe meetings, while fraud and anti-money laundering detection flags suspicious transactions in real time. Wealth-management tools help advisers pull client data faster, and agentic workflows route a single question across both structured and unstructured data.
How are banks using AI for fraud detection and anti-money laundering?
Fraud and risk teams use AI models to monitor transactions in real time, so a bank can flag suspicious activity before it turns into a large loss. One global investment bank now runs transaction monitoring that speeds up money laundering detection and prevention. Santander Corporate & Investment Banking analysts use a conversational AI agent called AIDA to query multi-petabyte data lakes in plain language instead of waiting days for a manual data pull.
What’s the biggest technical obstacle to scaling AI in banking?
Structured data is the harder problem. Transaction records, account histories, and risk metrics sit across dozens of data silos, each with its own access rules and formats. An AI system has to reach every one of those sources before it can answer a question with confidence. Banks that solve this by unifying access to structured and unstructured data on one platform tend to have the most live use cases in production.
How is generative AI different from earlier analytics tools in banking?
Earlier analytics tools mostly answered structured queries against clean, pre-modeled data. Generative AI can also read and summarize unstructured sources, such as contracts, filings, and meeting notes, turning documents that used to need a human reader into searchable, conversational data. That’s why Bank of America’s employees now send more than 400,000 AI prompts a day, according to Banking Dive. Most of those prompts aim at document-heavy work that older tools couldn’t touch.
Do banks need to move all data before deploying AI?
No. Banks that unify data access on one platform still leave data where it lives, whether that’s Amazon S3, an on-premises data warehouse, or a third-party system. They use that platform to query across sources instead of copying everything into a new location first. US Bank took this approach to unify data access and speed up AI-driven analytics, giving teams one place to query data that used to sit in separate systems.
How fast can a bank launch a new AI use case?
Banks that have already unified structured and unstructured data on one platform can launch production-ready AI use cases in about 90 days. They work with the systems they already run instead of planning a full data-estate replatform. Governance work, such as access controls, lineage, and audit trails, moves alongside the data from the start. A new use case doesn’t need a separate compliance review before it can go live.
