April 9, 2025 - By 2028, banking, insurance, capital markets, and payments companies will have invested a staggering $126 billion into AI — nearly four times what they spent in 2023. This massive increase signals an industry-wide shift, and already, major players are launching pilots into production.

The biggest names in finance aren’t waiting. Morgan Stanley’s wealth management group has adopted an OpenAI-based notetaking system, and their institutional securities group has launched an internal research assistant. JPMorgan Chase deployed “LLM Suite,” an AI assistant designed to help employees summarize complex documents, draft emails, and build their own models. Goldman Sachs has rolled out “GS AI” to 10,000 bankers, traders, and asset managers and plans to distribute the assistant to all knowledge workers by December 2025.

This article explains how other financial services companies use AI to drive better results for their companies and clients.

5 AI Use Cases Transforming Financial Services

Automate Repetitive Work

Outsourcing your firm’s low-impact work to AI frees up your talent (and your budget) to work on strategic initiatives. Integrating AI technologies into day-to-day workflows decreases employees' time to process documents, research trends, prep pitchbooks, and generate reports.

According to a survey by NVIDIA, over 60% of financial services professionals say AI has reduced total annual costs by 5% or more. AI also yields better returns. With less time spent on busy work, employees can spend more time deepening client relationships and shepherding high-value transactions. But automation isn’t just about saving time – it’s also sharpening how firms communicate.

Sharpen Messaging

Most financial services providers are already using some form of AI to surface relevant investment opportunities and product offerings to their customers. But AI can also help you deliver the right opportunity, message, or offer to investors and partners.

“As employees test out messages on the road, they can share investor and advisor feedback with AI to continuously improve their narrative,” says Danny McGuire. They can distribute that new messaging to the entire company. Similarly, companies now often use AI to analyze past earnings calls and media coverage to understand how to tailor their message.

Your team can also feed your financial data and customer insights into a closed-loop, secure LLM service and ask it to generate a list of potential acquirers, buyers, and sellers. If you know which company or investor you pitch to, the LLM can help refine the materials to speak to what they care about.

Better Calculate Risk

Catching patterns early with AI empowers financial services businesses to make smarter decisions at scale. Lenders are taking a magnifying glass to every borrower’s financial history, using AI to flag signs of poor creditworthiness early in the approval process.

“Firms are building 360-degree risk profiles,” says Citrin Cooperman Partner Danny McGuire. “They’re tracking behavior in real-time to evaluate borrower default risks more accurately depending on changes to customers’ physical, geographical, emotional, and financial well-being.”

Other financial services companies are using AI to monitor their liquidity. While analysts keep a close eye on the news, machine learning algorithms can pick up on market sentiment changes as quickly as they appear. Combining that real-time feed with your company’s ERP system and CRM data can help you predict events that impact cash flow — in time to move around capital.

Prevent Fraud

Cybercriminals increasingly use generative AI to produce deepfake images, avatars, and synthetic identification documents. This makes it more challenging for financial services firms to vet new customers, spot fraudulent transactions, and comply with federal and state regulations.

Companies that adopt an end-to-end approach to cybersecurity with AI can position themselves to uphold customer trust. Banks and lenders use machine learning models to detect suspicious activity and automatically block transactions or request additional verification. Some institutions use AI to generate synthetic data for model training for greater accuracy, less customer stress, and less downtime.

Others use AI to support manual case reviews instead. AI-powered chatbots retrieve relevant policy documents or customer data to inform fraud specialists’ decisions.

Faster Digital Innovation

AI is speeding up software development in financial services, not just by writing code but by eliminating inefficiencies across the entire development cycle. AI-powered tools like Tabnine and GitHub Copilot now assist engineers in real-time, reducing coding errors and surfacing vulnerabilities before they become costly breaches.

Other tools instantly surface bugs and vulnerabilities— some even recommend code fixes. AI guidance dramatically reduces debugging and testing time, pushing new apps and platform releases to production faster. AI testing tools can also preemptively suggest ways to improve the user experience that can help generate revenue.

AI Adoption Requires Thoughtful Planning

Financial services companies that integrate AI into their workflows are seeing increased upsides. At the same time, regulators are taking notice. The EU AI Act and recent U.S. House Reports underscore growing concerns about AI deployment, particularly within the financial sector.

These challenges should not deter you from driving efficiency and innovation with AI. However, they should encourage your team to document carefully and apply it thoughtfully. In many cases, a knowledgeable advisor who has done this before can help you identify the most impactful AI pilots for your business and implement them responsibly.

Citrin Cooperman’s Financial Services Industry Practice is here to help you explore how AI can shape your financial services organization’s future. Please contact your Citrin Cooperman representative.