Matthew McQueen, head of Bank of America’s public finance department, confirmed Wednesday that the complex task of structuring local government debt will now bypass human eyeballs entirely. The bank plans to cement its domination of the municipal underwriting market by having language models generate thousands of pages of financial jargon solely for other language models to process.
NEW YORK — Matthew McQueen, head of Bank of America’s public finance department, confirmed Wednesday that the complex task of structuring local government debt will now bypass human eyeballs entirely. The bank plans to cement its domination of the municipal underwriting market by having language models generate thousands of pages of financial jargon solely for other language models to process.
The initiative aims to capture a larger share of the $4 trillion municipal bond market by leveraging artificial intelligence to rapidly manufacture the sheer physical volume of paperwork required to justify millions in advisory fees.
Historically, we have forced junior analysts to sit in cubicles at 2 a.m., pretending to read municipal zoning annexes and wastewater flow reports before pasting boilerplate legal text into a massive document," McQueen said. "Today, our language model can generate a 900-page debt structuring proposal in four seconds. We then email that file to a Moody’s or S&P language model, which instantly approves the bond rating based on the structural density of the text. It is a flawless, highly lucrative closed loop of meaningless data.
The municipal underwriting sector, which structures tax-exempt debt for public works like toll roads, school districts, and water authorities, has long been hampered by the physical limits of human boredom. McQueen noted that BofA’s proprietary models offer a significant competitive advantage because they do not require sleep, nor do they pause to experience existential dread when evaluating the deteriorating tax base of a Rust Belt city.
By removing human underwriters from the workflow, Bank of America can collect hefty advisory fees from midwestern utility boards without any bank employee ever accidentally learning about a crumbling local infrastructure project.
Industry analysts expect the move to spark an arms race among Wall Street's public finance departments, with rival banks already training their own algorithms to generate even longer and more structurally opaque indemnity clauses.
At press time, BofA’s public finance AI had successfully secured a AAA rating for a $450 million sewer bond after overwhelming a Fitch Ratings algorithm with a 14,000-page PDF consisting entirely of the word 'indemnified' repeated in eight-point font.