You don’t need to dig all that deep to find a financial institution’s AI use cases.
Banks are deploying generative AI to detect fraud, speed up loan underwriting, help automate compliance review processes, and improve customer service. According to a 2025 EY-Parthenon study, 77% of banks already have launched or soft-launched generative AI applications, and nearly nine in 10 banking executives expect the technology to deliver major business benefits within the next two years.
The challenge is making AI work reliably at enterprise scale inside organizations where customer information, transaction data, regulatory reporting, and business processes have evolved across decades of systems and acquisitions. As banks move beyond pilots, many are discovering that this is where many AI initiatives stall.
Industry’s biggest players show what’s possible
JPMorgan Chase estimates AI is generating roughly $2 billion in annual business value across nearly 1,000 use cases. Bank of America also reported that more than 90% of employees use its internal AI assistant, reducing IT service desk calls by more than 50%.
Financial institutions are focusing on tightly governed enterprise use cases that improve employee productivity, strengthen customer service, and simplify operations while keeping people in the loop for critical decisions. That approach is shaping the next phase of enterprise AI across financial services.
Instead of asking how quickly they can deploy AI, finance leaders should determine how to scale it across the enterprise without creating new risks around data quality, governance, security or regulatory compliance.
AI exposes a longstanding data problem
Financial institutions don’t lack data. If anything, it’s quite the opposite.
Core banking processes, payment systems, loan servicing applications, wealth management services, fraud detection, customer relations, and regulatory reporting generate enormous volumes of information. As systems evolved independently over the years, organizations are left with inconsistent, fragmented data.
They are challenged with understanding what that data means, how it relates to the business, and whether it can be trusted enough for AI to make recommendations that withstand the scrutiny of executives, regulators, and auditors. Metadata, lineage, permissions, and governance become just as important as the data itself.
Paul Lilford, Director Product Strategy, AI Data Platform and Analytics, believes that’s where many organizations now find themselves.
“Enterprise data often lacks the context AI needs to produce consistent, explainable results,” Lilford says.
Business definitions vary across systems, governance policies differ by department, and years of integrations have made it difficult to establish a single, trusted view of enterprise information.
For chief data officers, that means improving data quality and governance while making enterprise information AI-ready. And chief financial officers look to ensure that AI investments improve productivity without increasing operational or regulatory risk.
They may have different concerns, but both executives ultimately want to deploy AI that the business can trust.
Why Oracle sees AI as a data platform opportunity
If enterprise AI has become a data problem, Oracle believes the solution starts with a different kind of platform. Unlike companies that focus primarily on analytics or AI models, Oracle brings together the operational systems, data infrastructure, governance, and business applications where much of the enterprise’s institutional knowledge already resides.
Consider a finance executive trying to identify unpaid commercial invoices that could affect quarter-end cash flow.
The information may exist across multiple systems—a core banking platform, an ERP application, customer correspondence, payment records and supporting documents. Finding the answer often requires analysts to gather data from multiple sources, reconcile conflicting information and determine whether a delayed payment represents a billing dispute, a customer issue or simply an invoice that hasn’t been processed.
Oracle AI Data Platform is designed to let AI work across those existing systems while preserving the governance and security controls already in place. Rather than requiring organizations to move data into another platform first, AI can use trusted enterprise data, business definitions and lineage to help identify outstanding invoices, explain why they’re overdue, summarize related customer communications and recommend next steps—all while operating within established enterprise policies.
AI delivers value when it understands the business
Banks are using AI to accelerate business challenges that once took hours if not days to resolve because they had to dig through data across multiple systems and connect information across systems.
- Fraud detection
- Summarize loan documentation
- Automate compliance reviews
- Improve customer service
- Help employees quickly locate information
The productivity gains are becoming measurable. The US Federal Reserve Bank reported in 2025 that workers using generative AI are saving roughly 5.4% of working time, with experienced users saving “considerably more.”
Lilford adds that AI should let employees spend more time doing the job they were hired to do, not preparing to do the job. Whether it’s an underwriter evaluating a commercial loan, a finance team reconciling accounts or a risk analyst investigating suspicious activity, AI can create significant value when it allows them to focus on the decisions only people can make.
It needs trusted enterprise data, business context and business semantics that explain what the data means, how it’s related to customers, accounts and financial processes, and which governance policies apply. Without that enterprise context, AI can’t return an answer. With it, AI is far more likely to return an answer the business can trust.
A practical modernization strategy
Rather than waiting years for a complete technology transformation, financial institutions can begin by cataloging enterprise data, establishing governance, connecting structured and unstructured information, applying lineage and security controls, and deploying AI into existing operational systems. Broader infrastructure modernization can continue over time while AI initiatives deliver business value sooner.
A bank’s chief data officer can create a trusted enterprise data foundation that supports analytics, machine learning, and AI from the same governed environment. And CFOs can pursue AI initiatives with clearer business outcomes, lower implementation risk, and greater confidence that enterprise controls remain intact.
Giving AI business context
Enterprise data contains information, but it doesn’t explain it to AI. Business semantics provides the context and meaning that enable AI to find, interpret, and use that information correctly.
That difference becomes increasingly important as AI moves into highly regulated industries like banking.
A customer may appear differently across multiple systems. Financial metrics can have different definitions depending on the business process. Regulatory calculations often depend on policies and relationships that exist outside the data itself.
Without business context, AI will return an answer. But with that context, AI is much more likely to return the right answer.
Deep business semantics helps separate enterprise AI from general-purpose AI. Rather than asking organizations to redefine their business for AI, Oracle AI Data Platform grounds AI in the trusted business definitions already embedded across Oracle Fusion applications and Oracle industry applications, and any other system contributing data and context.
Through a unified data and AI catalog, those definitions extend across enterprise data, AI models and agents, helping AI use the same business context that finance, risk, and operations teams already use every day.
Lilford sees governance as a natural extension of that philosophy.
“You want your platform to orchestrate AI, not AI orchestrating your platform,” he says. “You want to drive the rules of your platform, the guardrails around it to be in line with your enterprise, policies, procedures, people, and processes. AI for a finance person is very different than AI for a doctor.”
The next competitive advantage
The banking industry has spent decades building trusted systems of record. Every payment, loan, balance sheet, and regulatory filing depends on data that is accurate, governed, and auditable.
As AI becomes part of everyday operations, those same principles remain just as important for enterprise data. The next phase of AI in financial services likely will have less to do with the models that organizations choose than the foundation they build beneath them.
Oracle AI Data Platform is designed to provide that foundation by bringing together trusted enterprise data, deep business semantics, and AI in a single governed environment designed for production. Rather than asking financial institutions to choose between modernization and operational stability, the platform is designed to help them extend the value of existing enterprise systems while making them AI-ready.
Trust has always been the bedrock of banking’s business model. It ultimately may become the defining characteristic of its enterprise AI.
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