NextFin News - Bank of America is betting that artificial intelligence can do something that has long challenged municipal bankers: find more issuers, faster, in a market fragmented across thousands of states, cities, school districts and authorities. Matthew McQueen, who oversees the bank’s public finance department, said the firm is using AI to expand its underwriting reach across the country, a push that could reinforce a franchise that has already sat near the top of the municipal league tables.
The logic is straightforward. Municipal borrowers typically seek bankers through requests for proposals, and the pool is enormous. Bank of America’s own municipal banking material says the firm works with issuers and investors across all segments of the municipal marketplace and employs more than 250 municipal finance professionals nationwide. In other words, the bank is not trying to reinvent the muni business; it is trying to industrialize the way it hunts for it.
That matters because the municipal bond market is still highly relationship-driven, but it is also structurally diffuse. Thousands of issuers come to market over the course of a year, often with varying financing needs, different calendars and uneven access to advisory attention. A large platform can already compete on scale, distribution and balance sheet. AI adds a second layer: it can help prioritize targets, surface new relationships and speed the response process when issuance windows open.
Bank of America’s advantage is not just ambition. It is starting from a strong position. The firm remained No. 1 in municipal underwriting in the first quarter of 2026. That standing gives the bank something many rivals do not have: the ability to feed data and machine-learning tools into a franchise already rich with issuance history, client touchpoints and mandate flow.
In the municipal business, scale compounds. The more transactions a bank runs, the more it learns about pricing patterns, issuer behavior, regional calendars and the kinds of proposals that tend to win. AI can accelerate that learning curve, but only if the underlying franchise is already broad enough to produce meaningful signals. Bank of America fits that description. It has a nationwide public-finance footprint, a large roster of professionals and a top-tier place in the underwriting rankings.
What makes McQueen’s comments notable is not the suggestion that machines will replace bankers. It is the implication that the next edge in muni underwriting may come from better coverage of the market’s long tail. Large issuers still matter, but the opportunity in munis is spread across a wide array of smaller and mid-sized borrowers that can be hard to reach efficiently without more automation. If AI can help the bank identify which of those issuers are likely to come to market, who is likely to accept a proposal and what kind of financing structure is most likely to fit, then the technology becomes a commercial tool rather than a branding exercise.
Why AI Matters More In Munis Than In Many Other Credit Businesses
The case for AI in municipal underwriting is stronger than in businesses where deal flow is concentrated among a handful of giant clients. Munis are fragmented by design. State and local governments borrow for projects ranging from schools and water systems to transportation and healthcare facilities, and those financings are often episodic. A banker who is waiting for issuers to call is already behind. A banker who can see patterns in issuance cycles, funding plans and proposal behavior has a better chance of being in the room first.
That is why the technology matters even if the underlying service remains unchanged. AI can improve prospecting, proposal drafting, calendar management and client segmentation. It can help a banker turn a large market into a searchable one. In a business where timeliness and familiarity matter, that can compress the gap between having a name on a list and being invited to underwrite a deal.
Bank of America’s municipal operation appears built to absorb that kind of gain. Its public-finance team is large, geographically distributed and already embedded in a broad set of borrower relationships. The bank says it serves issuers across the municipal marketplace, which is exactly the sort of operating model that can benefit from tools that automate screening and outreach.
That does not mean the entire competitive hierarchy will change quickly. Municipal underwriting is still driven by pricing discipline, execution quality and trust. Issuers often return to banks that have delivered clean execution in prior deals, and public-sector borrowers can be conservative about changing advisers or underwriters. AI can enhance the process, but it does not erase the importance of human judgment. The winning bank still has to price a transaction correctly and navigate political and budgetary constraints that no model can fully capture.
Still, the direction of travel is clear. The more fragmented the market, the more valuable it becomes to have systems that can score opportunities at scale. In that sense, AI is not a futuristic add-on to muni underwriting; it is a practical response to the market’s structure.
The Real Competitive Question Is Not Whether AI Works, But How Fast Rivals Catch Up
Bank of America’s move also underscores a broader truth about financial technology adoption: the benefit of AI often accrues first to the firms that already have the most data, the most people and the most active client networks. A small bank can buy tools. A large bank can train those tools on a bigger body of past transactions and client behavior. That advantage compounds when the market itself is broad and repetitive.
In munis, that compounding effect may be especially powerful. Each issuer relationship can feed the next one. Each proposal can help refine the next pitch. Each completed transaction can improve pricing models and mandate selection. If AI shortens the cycle from identification to proposal to execution, then the bank that already leads the market can widen the lead without needing a dramatic change in strategy.
“We’re proud to support issuers and investors across all segments of the municipal marketplace,” Bank of America says on its municipal banking page, describing a platform that includes financing and lending, treasury management, sales and trading and industry leadership.
That kind of breadth is exactly what makes the AI story credible. The technology only becomes meaningful when it is attached to an operating platform that spans the market. Without that, AI is just software. With it, AI can become a force multiplier for coverage, targeting and response time.
The competitive risk for rivals is obvious. If they wait until AI is already standard across the market, the largest platform will have had years to refine its models, learn from its wins and losses and embed the tools in day-to-day deal origination. In a business where relationships matter but attention is scarce, speed can be as important as price.
What This Means For The Municipal Market
The immediate implication is not that AI will radically change how cities and states borrow. It is that the process of getting in front of those borrowers may become more efficient, more targeted and more data-driven. That could intensify competition for mandates, especially among the large banks that already dominate public finance.
For issuers, that may mean more tailored proposals and faster responses. For banks, it means higher expectations: the easy excuse that a banker simply missed an opportunity becomes less defensible when software can scan the market continuously. The municipal business has always rewarded institutions that can combine local relationships with national scale. AI gives the biggest players a new way to do both at once.
Bank of America is not alone in pursuing automation, but its position in the rankings and its broad municipal footprint make it one of the firms most likely to turn that investment into measurable franchise gains. The more AI improves prospecting and execution, the more the bank’s existing dominance can become self-reinforcing.
The key takeaway is simple: in munis, AI is less about replacing the banker than about making the bank harder to ignore. If Bank of America uses it well, the technology could widen the gap between the largest public-finance platforms and everyone else.
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