Mar 25 2026
Artificial Intelligence

Generative AI Speeds Lending and Customer Service Across Finance

From instant document reviews to chatbots for complaints, financial firms are using generative artificial intelligence to cut wait times, boost agent productivity and deliver smarter insights.

Applying for a home equity line of credit isn’t supposed to be fun. The required pay stubs and tax returns, the bank statements and mortgage records — it all adds up to a whole lot of paperwork that many find to be stressful and tedious.

A company that knows this well is Figure Lending, the largest nonbank HELOC provider in the United States, which in 2024 originated about 30,000 HELOCs totaling almost $2.5 billion.

From its founding in 2018, Figure has used artificial intelligence and automation to make the lending process more palatable for homeowners. “Ultimately,” explains Chief Data Officer Ruben Padron, “the goal is to maximize ROI while driving and improving customer satisfaction.” He notes that Figure was recognized for this work by Experian last fall at the credit bureau’s 2025 Vision Awards. “AI is very much a force multiplier for us. It lets us empower our loan officers and customer service agents to be much more effective and efficient at what they do,” he says.

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At the center of the company’s AI efforts is a proprietary platform that runs off more than 90 customized machine learning models. Constructed around a flexible and vendor-agnostic architecture, the system leverages a mix of Figure’s own generative AI programs and models from providers such as Google and OpenAI.

The company’s marketing department uses the platform for customer prospecting, “with the intent of putting the right message in front of the right person at the exact right time,” Padron explains. And in terms of client services, “it’s behind almost everything, from document processing to direct-to-customer interactions.”

Lengthy legal documents that typically took agents up to 30 minutes to process are now completed with generative AI in less than six seconds with over 99.5% accuracy. Chatbots are available to provide real-time assistance to anyone struggling with applications online, and voice AI is used to automatically call and help customers who’ve started the process but failed to finish.

DIVE DEEPER: Get help breaking down financial services data silos.

Figure turned to Google Gemini, OpenAI, and other selected third-party solutions for its AI-powered chatbot in part because of the highly regulated environment in which the company operates, Padron notes. The solutions comply with its policy of zero data retention, and integrate with Figure’s established data security measures to prevent unauthorized access to customer information.

Padron adds that his team is now exploring how new AI models might be used for other business needs in the future. “In terms of what’s possible when we deploy AI responsibly, I think we’re only starting to recognize the positive impact it can have on customers,” he says. “We’re doing a lot with these technologies now, but I really believe we’re just scratching the surface.”

Why Generative AI Is a CX Catalyst in Finance

While it’s true that financial services companies such as Figure are still in the early stages of using AI to elevate customer experience (CX), many in the sector — like those in other industries — say they’ve already seen meaningful returns from their deployments.

One 2025 survey from Forrester Research asked senior leaders at more than 900 companies how their organization had been positively impacted by AI in the past 12 months. Respondents reported the technology had improved automated processes and employee productivity, and that the top external benefit involved improved CX. IT executives told the firm they were using AI to enable customer self-service, improve efficiency of customer-facing employees and conceive new customer experiences.

Forrester’s Kate Leggett, vice president and principal analyst for customer relationship management and customer service, says that insurers GEICO and AXA are just two examples of companies that have deployed AI-driven chatbots to great success.

UP NEXT: Why a tech partner can guide financial services towards foward-thinking solutions.

“They’ll find your documents, answer questions about coverage, and learn and adapt to what you need,” she says. “They’re reducing wait times and freeing up human agents to focus on the more complex cases.”

Robin Gareiss, CEO and principal analyst at Metrigy, agrees with Leggett that generative AI is quickly transforming how businesses interact with customers. And while Metrigy’s own research reveals AI-driven improvements in important CX metrics such as customer satisfaction, it’s also found the technology is helping companies generate revenue and cut costs.

“When your agents are more efficient, they’re saving time, which means you may not have to hire as many new agents,” Gareiss explains. Likewise, as AI helps representatives solve customer problems faster, “maybe now, you start to find more time and opportunities for upselling.”

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Moving From Raw Data to Intelligent Insight With AI

No upselling was required to convince S&P Global Energy (formerly S&P Global Commodity Insights) to use generative AI in its customer service operations. As a leading provider of expert analyses into the world’s commodities markets, the company produces more than a thousand news items and reports daily that are laden with timely, industry-specific data.

Until recently, S&P’s customers had to either download these reports or build custom interfaces to extract the insights they needed.

Looking to address concerns that the process was too technically complicated, S&P Global Energy decided to partner with Microsoft to optimize its data sets for AI and then to build an AI-enabled assistant to help customers surface the commodities information most important to them.

43%

Share of IT leaders at IT leaders at organizations that are developing AI-enabled customer service solutions who say that enabling customer self-service is a top use case

Source: Forrester, State of AI Survey, 2025

To make their content “AI-ready,” explains Priyanka John, vice president of digital product management, “the insights go through a digital transformation in which text is categorized into chunks and stored in a database with relevant metadata architecture.” Large language models then draw semantic relationships and summaries, a process that customers can facilitate in their AI ecosystem of choice.

This includes Microsoft 365, as well as solutions from Google, Amazon Web Services, Snowflake and others.

John points to a North American natural gas pipeline utility that uses S&P data to compile daily indicators on gas price sentiment, regional outages and storage levels. The organization then combines this information with regulatory agency reporting and its own proprietary performance metrics to get a comprehensive view of its asset base.

John adds that the “end goal” for S&P in its efforts to take advantage of generative AI has always been to build an unforgettable customer experience. “It’s about getting answers to their crucial questions as fast as possible so they can make confident decisions and stay a step ahead,” she says.

Jim Frazier/Theispot
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