The financial technology sector is witnessing a significant shift as legacy infrastructure faces the disruptive force of generative artificial intelligence. Fuse, a startup dedicated to modernizing the backbone of the lending industry, announced on Monday that it has successfully raised $25 million in a Series A funding round. This investment, led by prominent venture capital firms including Footwork, Primary Venture Partners, NextView Ventures, and Commerce Ventures, marks a pivotal moment for the company as it seeks to replace aging Loan Origination Systems (LOS) that have dominated the banking landscape for decades.

Founded by Andres Klaric, a Bolivian native, and Marc Escapa, a Spanish immigrant, Fuse represents a strategic pivot from the founders’ original venture. In 2023, after three years of developing an automotive lending startup, the duo recognized a more profound opportunity. They observed that while consumer-facing fintech apps had become sleek and user-friendly, the underlying "plumbing" of the lending world—the systems used to process, underwrite, and manage loans—remained mired in outdated, cumbersome software. The emergence of Large Language Models (LLMs) provided the catalyst for Klaric and Escapa to reimagine this infrastructure from the ground up, resulting in the birth of an AI-native LOS.

The Critical Role of Loan Origination Systems in Modern Banking

To understand the impact of Fuse’s entry into the market, one must first grasp the central role of the Loan Origination System. In the hierarchy of financial technology, the LOS serves as the primary system of record for lenders. It is the engine that drives the entire loan life cycle, beginning with the initial consumer application and moving through complex stages of document verification, credit underwriting, compliance checking, and finally, the disbursement of funds.

For most financial institutions, particularly the more than 4,000 credit unions operating across the United States, the LOS is as vital to daily operations as a Customer Relationship Management (CRM) platform is to a sales team or an Enterprise Resource Planning (ERP) system is to a manufacturer. However, despite its importance, the technology currently serving this sector is often decades old.

Legacy LOS providers, such as the publicly traded nCino and the private-equity-backed MeridianLink, have long faced criticism for their lack of agility. Traditional systems can take upwards of a year to integrate into a bank’s existing workflow. Furthermore, these incumbents often lock institutions into multi-year, expensive contracts that make it financially prohibitive for smaller lenders to upgrade their technology. According to Klaric, these barriers have stifled innovation within community-focused financial institutions, leaving them at a disadvantage compared to "Big Tech" banks and agile digital lenders.

Chronology of Innovation: From Automotive Lending to AI-Native Infrastructure

The journey of Fuse began not as an infrastructure play, but as a direct-to-consumer lending solution. In 2020, Klaric and Escapa identified inefficiencies in the automotive loan market. However, by 2023, the rapid advancement of LLMs such as GPT-4 and Claude suggested a different path. The founders realized that the most significant bottleneck was not the lack of loan products, but the technical inability of lenders to process those products efficiently.

The pivot was swift. By leveraging AI, Fuse aimed to automate the manual, labor-intensive tasks that define traditional underwriting. Instead of loan officers manually reviewing pay stubs, tax returns, and bank statements—a process prone to human error and bias—Fuse’s AI agents can ingest and analyze this data in seconds. This allows lenders to process significantly higher loan volumes without a corresponding increase in headcount, effectively lowering the cost per loan.

By late 2023 and early 2024, the company began onboarding its first batch of customers. Today, Fuse boasts a portfolio of over 100 clients, primarily consisting of credit unions and mid-tier financial institutions. The recent $25 million Series A is intended to accelerate this growth, fund further research and development into autonomous underwriting, and expand the company’s sales and support teams.

The Five Million Dollar Rescue Fund: Overcoming Legacy Lock-in

One of the most innovative aspects of Fuse’s market entry strategy is the establishment of what it calls a "rescue fund." Recognizing that many credit unions are trapped in long-term contracts with legacy vendors, Fuse has allocated $5 million to facilitate the transition for new clients.

Under this program, the first 50 qualifying credit unions to sign with Fuse will receive free access to the platform until their existing contracts with legacy LOS vendors expire. This move addresses a major pain point in the industry: the "double-pay" problem, where an institution wants to modernize but cannot afford to pay for two systems simultaneously during a transition period.

"It’s not just a marketing gimmick," Klaric emphasized during the announcement. He explained that the financial health of credit unions is often tied to their ability to manage overhead. By removing the immediate financial burden of switching providers, Fuse is lowering the barrier to entry for institutions that serve the American middle class—a demographic that Klaric believes is currently underserved by modern financial technology.

Supporting Data and Market Analysis

The market opportunity for Fuse is substantiated by the scale of the credit union sector in the United States. According to the National Credit Union Administration (NCUA), as of early 2025, there are over 4,000 credit unions serving more than 135 million members. These institutions hold trillions of dollars in assets but often operate on razor-thin margins compared to national banking giants.

The demand for AI integration in this sector is high, yet the implementation gap is wide. A recent industry survey suggested that while over 70% of credit union executives view AI as a "high priority" for the coming three years, fewer than 15% have successfully integrated AI into their core lending workflows. This discrepancy highlights the potential for an "AI-native" system like Fuse to capture significant market share.

Nikhil Basu Trivedi, co-founder and general partner at Footwork, noted that the investment in Fuse was driven by the clear need for a technological overhaul in community banking. Basu Trivedi compared the current state of the LOS market to the pre-cloud era of enterprise software. He noted that while swapping out a core system is historically difficult, the speed and ease of adoption offered by AI-native platforms are changing the ROI calculus for bank boards.

Competitive Landscape and Industry Reactions

Fuse is not alone in its quest to modernize the lending stack. The startup enters a competitive field that includes other well-funded challengers such as Casca and Glide. These companies are also vying to replace the incumbents by offering more modular, cloud-first, and AI-enhanced solutions.

However, the incumbents are not standing still. Companies like nCino have begun integrating AI features into their existing platforms through partnerships and internal development. The competition is expected to center on "time-to-value." While legacy players offer stability and a long track record, startups like Fuse argue that their systems can be deployed in a fraction of the time, allowing lenders to see immediate improvements in their "look-to-book" ratios—the percentage of loan inquiries that turn into funded loans.

Industry analysts suggest that the entry of Fuse and its peers will likely trigger a wave of consolidation or rapid feature adoption among legacy providers. "The credit union space is the last frontier of digital transformation in finance," says one fintech analyst. "If Fuse can prove that an AI-native LOS reduces default rates while increasing speed, the pressure on legacy vendors will become existential."

Broader Implications for the Financial Ecosystem

The mission of Fuse extends beyond mere software sales; it touches on the democratization of financial services. Credit unions are member-owned cooperatives that often provide better rates and more personalized service than commercial banks. By providing these institutions with the same level of technological sophistication as a global bank, Fuse is effectively leveling the playing field.

"Credit unions and smaller financial institutions have everything required to win," Klaric said. "They have the local presence, the local focus, and a great member experience. The only thing they don’t really have is the right technology."

The broader implications of this shift include:

1. Reduced Costs for Borrowers: As the operational cost of processing a loan drops, credit unions can pass those savings on to members in the form of lower interest rates or reduced fees.
2. Faster Credit Decisions: In an era of instant gratification, the ability to provide a "yes" or "no" on a loan application in minutes rather than days is a significant competitive advantage.
3. Enhanced Risk Management: AI models can analyze a broader array of data points than traditional credit scoring models, potentially allowing lenders to safely extend credit to individuals with "thin" credit files who were previously deemed unbankable.

As Fuse begins the rollout of its "rescue fund" and expands its reach among American credit unions, the fintech industry will be watching closely. The success of this $25 million Series A will serve as a litmus test for whether AI can truly replace the deeply embedded legacy systems that have served as the foundation of the global financial system for the past thirty years. For Klaric, Escapa, and their team, the goal is clear: to ensure that the technology powering the middle class’s financial future is as advanced as the smartphones in their pockets.