AI's promises of change hamstrung by data quality worries

The Bond Buyer's 2026 Predictions Report

What impact will AI have on municipal finance?

Key takeaway: Disclosure and compliance functions within municipal finance are poised for major change through AI.

Financial services leaders have continued to forge ahead in their AI adoption quests and the municipal finance market is approaching a significant period of upheaval.

Disclosure and compliance (54%) was the number one area identified by municipal finance professionals as on the brink of major change due to AI in 2026. Credit analysis and research (53%) was close behind, followed by pricing and trading (49%), risk management and surveillance (46%) and portfolio optimization (43%).

Earlier this year, Munibonds.ai launched its AI-powered platform for muni bond analysis. The credit research tool, according to the company's founder Robert Kane, works by reviewing thousands of pages of information, including official statements, material events, rating changes, financials and even news articles, for every new issue.

The platform provides the user with a streamlined report, deal and bond summaries, key financial metrics, risk factors and disclosure flagging and an AI-generated podcast, he said.

"There's a lot of opportunity because there's a uniqueness to information in the space: 1.3 million bonds, more or less, each of which has thousands of pages, documents, material and events," Kane said. "I don't know that a lot of attention has been paid to it before because it's just been a Herculean task. ? But now it's possible to do that."

Adoption of a technology like AI in an industry like municipal finance is not a straightforward process, especially when that technology promises massive levels of change in a relatively short period of time.

Dave Sanchez, director of the Securities and Exchange Commission's Office of Municipal Securities, during an event hosted jointly by the SEC, the Municipal Securities Rulemaking Board and the Financial Industry Regulatory Authority, said the muni market "is defined by stability and, frankly, processes that have remained largely static for over 50 years."

"With the integration of [AI], automated compliance and agentic AI, we have been confronted with promises of unprecedented efficiency," Sanchez said. But as FINRA stated in its 2026 FINRA Annual Regulatory Oversight Report, the risks associated with AI "can be viewed as the silent erosion of human oversight."

The downsides of marrying AI and municipal finance

Key takeaway: Data quality and accuracy was the top concern among AI skeptics in the industry.

The promises of automation are tempting for many municipal finance professionals, but those same leaders are wary of the numerous challenges that come with the technology.

The top drawback about the adoption of AI in municipal finance by far was the number of concerns surrounding data quality or accuracy (32%). Over-reliance and complacency (14%), cybersecurity threat (11%) and lack of nuance/context (9%) were among the risks named the most problematic for AI adoption.

Lower down the list were job displacement (5%), lack of transparency (4%) integration issues (3%) and regulatory and legal concerns (3%).

The productivity and overall output of these tools is closely tied to the data being fed into them. Failure to clean and verify the data prior to running them through AI models opens firms up to sizable accuracy and other issues.

The more commonplace AI adoption becomes in municipal finance, the more insights professionals will have into how these tools are developed and deployed.

Jeff Lipton, municipal market intelligence analyst for The Bond Buyer, predicts that alongside AI "touching virtually every business vertical and reshaping and differentiating the views currently held by many public finance stakeholders," more clarity around how issuers can influence AI infrastructure development will emerge.

"Integrating AI into best practices will be challenging given the associated costs, the need for innovative learning tools, the overall selection and management of the data, the importance of preserving market competitiveness, efficiency and risk mitigation and expectations for enhanced regulatory oversight," Lipton said.

"For muni market stakeholders, the question is, 'how will we take all of this data and assimilate it into something that is far more analytically powerful?'," he said.