The Bond Buyer's 2026 Predictions Report
The Bond Buyer Predictions 2026 survey was fielded online during November and December, with responses from 74 municipal finance professionals. Respondents represent a range of organization types, with the largest shares from broker-dealers (27%), issuers (18%), municipal advisors (16%), and law firms (8%).
Top findings from the report- Disclosure and compliance functions within municipal finance are poised for major change through AI.
- Data quality and accuracy was the top concern among AI skeptics in the industry.
Results from the report are highlighted below using interactive charts. Mouse over each section for more detail, click on the chart labels to show or hide sections and use the arrows to cycle between chart views.
This item is the start of a series diving into new research from The Bond Buyer. Click the links below to read the other parts of the overall research.
- Part one: Muni growth predicted despite Fed shakeup
- Part two: Tariffs, political turmoil pose major threats to public finance
- Part four: Coming soon
- Part five: Coming soon
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
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
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.
"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
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.
"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.
