AI Implementations That Are Transforming Financial Services in Real Time 

Last year, the financial services industry concentrated its efforts on the experimentation of AI—its predictive capabilities and automation potential. This year, we are already seeing organizations shift their focus from that experimentation to execution, while also keeping in mind the inevitable risks that come with large-scale implementations. 

Leaders are bringing AI deeper into their operations to support the teams who keep everything moving—helping them react quicker, strengthen safeguards, and create customer experiences that feel genuinely helpful and intuitive.

According to a survey by Gallup, 40% of employees across the finance sector reported using AI regularly, among one of the highest rates across all industries. Whether in fraud prevention, underwriting, or customer service automation, the use of AI in finance has already proven to help lift the weight off complex data and provide clarity and actionable insights across all functions of the business. 

What AI Means for Finance Today

When organizations talk about artificial intelligence and financial services, they typically refer to a blend of machine learning, predictive analytics, natural language processing, and workflow automation.  

According to a survey by Gartner, 59% of finance leaders reported the use of AI in their core financial services operations in 2025, and adoption is only expected to accelerate in areas like fraud detection, customer service, and investment analytics.  

However, it’s safe to say that the impact has not been confined to any one department. Now more than ever, AI applications in finance have truly reached into every corner of the business. An external-facing representative is no less likely to encounter AI than an internal risk manager.  

Where AI Is Driving Value  

Whether organizations got a headstart before 2025 or are now just getting started, the impact of AI across financial services is widely seen as significant. Let’s dive into a few ways that impact is being felt: 

• Fraud detection: While all industries have faced an increase in AI-powered attacks over the past year, a study by BountyBench highlights that AI is proving to be an even stronger force for cyber defense—even more than cyber offense. This knowledge helps organizations detect, patch, and neutralize threats faster than attackers can exploit them. 
• Customer service automation: Since its launch in 2018, Bank of America’s AI virtual assistant, Erica, surpassed 3 billion client interactions and 1.7 billion proactive, personalized customer insights. This resulted in 98% of users successfully finding the information they need and a decreased volume for call centers volume, allowing financial specialists to spend time focusing on more complex financial conversation with clients. In fact, NVIDIA notes that in 2025, businesses using AI have already seen customer experiences improve by 26%. 
• Middle- and back-office efficiency: According to the Federal Reserve, AI has long been used in financial institutions and regulators, and it is increasingly improving efficiency and labor productivity, particularly as models become cheaper and more capable. And much of this impact occurring behind the scenes in core operations. 

Why Institutions Are Investing in AI Solutions 

As day‑to‑day operations become more complex, AI can give financial institutions the support they need to handle high‑volume tasks, cut down on manual effort, and surface insights that used to be buried in data.  

With that extra lift, leaders are recognizing the advantage of using AI to strengthen already existing systems and helping them get one step ahead in a very competitive industry. For some, that may mean making their anti-money laundering operations more vigilant, and for others it may grant an opportunity to deliver unparalleled customer experiences. 

In short, we’re seeing more and more that AI has become less of a “nice to have” and more of a practical, reliable way to stay resilient and keep the organization moving forward. Some of the ways AI is being implemented in financial services includes:  

• Personalized experiences: In a survey done by MX Technologies, it was found that 54% of respondents actually want financial providers to leverage their customer data to personalize their experience. 
• Anti-Money Laundering & Know Your Customer operations: Napier claims that Worldwide, the U.S. stands to gain the most from AI-powered financial crime compliance solutions, potentially saving financial institutions $23.4 billion on compliance costs, followed by Germany ($14.2 billion), and France ($11.08 billion). 
• Investor relations and capital markets: A new area of impact emerging in 2025–2026 is investor relations (IR). Gartner research released in January 2026 finds that the rise of AI-powered investor research tools is reshaping how CFOs engage with the markets. 
  • 35%+ of CFOs reported increases in the volume, frequency, and time sensitivity of investor communications from 2024 to 2025. 
  • As institutional investors adopt AI at scale, organizations are facing higher expectations for speed, transparency, and narrative control. 
  • Gartner notes that the same finance AI capabilities accelerating investor analysis can be leveraged by IR teams, helping them enhance message discipline, monitor market sentiment, and reduce manual workload. 
• Risk and compliance: Federal financial regulators have reported to the U.S. Government Accountability Office (GAO) that, as they continue to prioritize and assess AI risks, they may refine guidance and update regulations to address emerging vulnerabilities. This is in addition to already existing AI-specific guidance, such as on AI use in lending, or conducted AI-focused examinations. 
• Claims and policy servicing: 45% of insurers are already using AI in claims, primarily to improve customer responsiveness and resolution speed in high‑frequency claims, according to a 2024 survey conducted by Gallagher Bassett, and that rate is sure to rise going into 2026. 

Challenges Leaders Need to Navigate for AI for Financial Services 

Adopting AI in finance also often comes with responsibilities, particularly around ensuring systems remain fair, secure, and well‑governed. As institutions deepen their use of AI tools, we’re seeing them strengthen transparency and oversight to make sure AI supports human decision‑making in safe, consistent, and accountable ways.  

This is largely due in part to the findings of public and private sectors alike, urging leaders to stay informed and vigilant. For example, in 2024, the U.S. National Institute of Standards and Technology (NIST) issued a GenAI Profile to its AI Risk Management Framework, outlining security risks and prescribing considerations for AI deployments. 

Beyond governance regulations, the depth and breadth of AI initiatives should be approached with discernment and with humans in the loop. From compliance and data stewardship to workforce readiness and talent development, organizations are responsible for ensuring that AI is deployed with integrity and resilience.  

We have seen the most success from financial institutions who prioritize the reinforcing of trust among employees, customers, and regulators alike. 

• Data privacy and security: The previously mentioned U.S. NIST is a U.S. federal, sector‑agnostic benchmark widely referenced by financial regulators and market participants. Their GenAI Profile specifically maps privacy and security risks (e.g., data privacy, information security, data leakage, de‑anonymization) and prescribes governance, controls, and risk‑management actions for AI deployments.
• Regulatory compliance: In March 2024, the U.S. Securities and Exchange Commission brought its first AI‑washing enforcement actions, penalizing two investment advisers $400,000 for false statements about AI use—signaling that claims about AI must be accurate, documented, and auditable. 
• Bias and transparency: Supervisors are elevating explainability and fairness expectations in model risk frameworks. The Bureau of Labor Statistics Financial Stability Institute (Dec 2024) calls out the need for transparency, explainability, governance, and data‑quality controls for AI used in banking and insurance. 
• Operational resilience: Model performance can drift as data and behavior shift—degrading accuracy if not monitored continuously. Regulators and standards bodies underscore ongoing monitoring, logging, and retraining as core to AI resilience and governance; the Bureau of Labor Statistics Financial Stability Institute (Dec 2024) and the Federation of Small Businesses (Oct 2025) highlight monitoring practices and third‑party concentration risks in GenAI supply chains.
• Talent and adoption: Independent tracking shows adoption rising—and skills are the bottleneck. The Stanford Institute for Human-Centered AI 2025 AI Index reports that 78% of organizations used AI in 2024, with evidence of productivity gains where employees are trained to use the tools effectively.  

Putting Your Best Foot Forward 

Strong governance will be essential as AI becomes more embedded in financial operations, shaping how reliably these systems perform over time. Institutions we work with are taking a structured, forward-looking approach to oversight, ensuring their AI initiatives are grounded in clear standards, consistent review, and the kind of disciplined management that supports long-term trust and stability. 

Some things for you to consider as you look at AI governance for financial enterprises:  

• Have you established a formal model for risk framework covering development, validation, deployment, and retirement? 
• How will you document data lineage, model assumptions, validation results, and decision policies to ensure auditability? 
• How will you implement real-time monitoring for performance, drift, and fairness with alerting and remediation paths? 
• What explainability tools to support internal reviews and regulatory examinations will you use?  
• How will teams adopt privacy-by-design principles with encryption, access controls, and minimization of sensitive data exposure  
• How are you going to bake in human oversight for high-impact decisions? 

From Potential to Practice 

AI is becoming a powerful catalyst for progress across financial services, helping many institutions  operate with greater clarity, speed, and confidence. As leaders strengthen their data foundations and governance practices, they may find that they can scaling AI can be done both effectively and responsibly. With the right strategy and teams in place, AI can do more than enhance existing workflows—it can create new opportunities for growth and transformation across the entire organization. 

The future of AI in finance is taking shape in real time, and the focus now is on scaling these capabilities in a way that is measured, intentional, and built for long‑term impact. How will you tackle these challenges to keep your financial institution competitive and robust into the future? Insight Global can help with sourcing expert AI professionals, business transformation through layering AI into your human workforce flows, building and tuning agents, and much much more. Contact us to find out more.