AI in financial services: Banking on the power of AI agents in customer experience

Financial services firms face unique challenges when it comes to delivering a consistently exceptional customer experience (CX). Customers today expect prompt, personalized responses to their queries or, increasingly, proactive notifications about account status or other pertinent information, securely delivered. To them, issues such as data silos, system complexity, and regulatory compliance are of little concern.

Fortunately, artificial intelligence (AI) provides a means of elevating CX to meet customer expectations while at the same time preventing challenges from disrupting service deliverables. Financial services firms aren’t shying away from using it, as Metrigy has found in its AI for Business Success: 2025-26 global research study conducted earlier this year. In fact, the study reveals that the financial services sector leads other industries in adopting AI in general and is demonstrating measurable performance gains. 

Not only have most financial services companies studied widely adopted the use of AI for customer and employee interactions, but 80.6% are also applying AI to their customer interaction channels. Only a slight percentage, 5.4%, are holding off on employing AI for customer-facing use cases until 2026 or later.

Customer-facing virtual AI voice and text agents—particularly advanced conversational and generative AI agents—are proving significantly transformative, moving financial services companies beyond simple Q&A functionality. 

Sitting between complex legacy systems and the customer, AI agents can enable a thoroughly modern experience without requiring a complete and costly overhaul of contact center infrastructure. Metrigy’s study reveals robust adoption of virtual AI agents among participating financial services companies: 67.7% have already implemented AI text agents, and 59.1% are using AI voice agents, with virtually all of the remainder having both agents on their roadmaps or under evaluation.

Virtual AI agents can be great at deflecting calls and other live agent engagements, but they’re also good for handling complex, transactional tasks that directly impact functions such as account management, sales, and security. Common use cases in financial services for customer-facing virtual AI agents include the following:

• Automated customer service and account management: This foundational use case dramatically reduces call center load and costs. Virtual agents instantly handle routine inquiries, such as checking account balances, recent transaction history, and branch locations. They also facilitate secure, simple transactional assistance, including paying bills, transferring funds between accounts, and scheduling recurring payments. For cardholders, a virtual agent can manage requests to lock or unlock a debit or credit card, report a lost or stolen card, and change a PIN. 
• Personalized financial guidance and sales: Virtual agents can deliver tailored advice proactively by leveraging machine learning and customer data. They can provide spending and budgeting insights by analyzing transaction history and can offer tips to help customers meet savings goals. Crucially for sales, virtual AI agents can generate highly relevant, personalized product recommendations, whether a high-interest savings account or a new credit card, based on the customer’s profile and spending habits. Proactive alerts about unusual spending or impending credit card payments are also common.
• Streamlining complex processes: At many financial institutions, virtual AI agents simplify paperwork-heavy processes. For example, a virtual agent can guide loan and mortgage applicants through the initial steps of their applications, answering basic eligibility questions while collecting necessary documents in real-time for pre-qualification. Similarly, virtual agents can assist insurance customers in starting a claim, checking the status, and obtaining answers to policy-related questions.
• Security and fraud prevention: In this case, virtual AI agents can communicate instantly with customers about potential security threats. This includes proactive fraud alerts via outbound voice or text agents when suspicious activity is detected, guiding the customer to verify or block the transaction. AI also performs secure multi-factor identity verification before granting access to sensitive account features.

With such capabilities, virtual agents enable financial services companies to resolve 42.5% of their contact center interactions without live agent support.

Financial services firms reported some of the highest improvements across the four business success metrics Metrigy evaluated in the study, demonstrating that leveraging AI results in top-tier CX and substantial financial upside in this sector. Among financial services companies measuring the success of AI, the performance metrics and associated improvements are:

• 78% of companies measure CSAT improvements from their AI implementations for CX, realizing a 41% increase, on average
• 72% of companies measure sales growth, reporting a gain of 36%, on average
• 71% of companies measure employee productivity gains, showing a 39% boost, on average
• 63% of companies measure operational costs, with a 29% decrease on average 

Additionally, financial services companies are quickly realizing tangible returns. Just shy of 72% report seeing a positive return on investment (ROI) from AI now, with another 13.5% expecting ROI within the next 12 months. This rapid realization of value underscores the urgency and success of AI integration in this sector. Nearly two-thirds of financial services companies are already characterizing their AI use as extensive—i.e., they are using it across all or most business units, with customer service/contact center among the top functional areas benefiting from AI today. 

Ultimately, AI technology purchase decisions within most financial services companies studied rest on the shoulders of directors or C-level executives, suggesting the mission-critical nature of these initiatives. 

To shepherd positive change and drive efficiency across the enterprise, 44.1% of financial services companies already have an AI Center of Excellence (CoE), and 53.8% are planning one. Establishing an AI CoE is a particularly effective practice for financial services companies to maximize the usefulness of AI across the organization. It can help ensure compliance through shared expertise, common goals, and well-articulated governance.

Clearly, companies in the financial services sector are already realizing the value of AI, including virtual voice and text agents, in helping create exceptional customer experiences. Just as clear from Metrigy’s AI for Business Success research is that there is no end in sight: AI spending is on an upward trajectory and will be into 2026. Our research shows that AI tech spending in the vertical is expected to grow by 29.2% in 2025 and 36.1% in 2026. Virtual agents are targeted areas for growth, as well as agent assist, agentic AI, and conversational analytics, among others.

While most financial services companies today see greater value in task-oriented roles for their virtual agents, we can expect a shift away from virtual agents reacting to customer queries. They will become more proactive AI agents that can autonomously plan and execute the complex, multi-step financial workflows discussed above, including processing loan applications and resolving fraud cases end-to-end.

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