Bank of America (BoA) warns that the explosive growth of prediction markets and online sports betting may lead to increased financial strain on consumers and new credit risks for lenders.
In a note published on Friday, as reported by Bloomberg, BofA strategists led by Mihir Bhatia stated that the rapid expansion is “creating a new credit risk for lenders.” That’s because the gambling-like losses build consumer debt.
The strategists wrote that “easy access and gamified interfaces encourage frequent and impulsive wagers.” They added that this convergence of speculation and entertainment could “pressure credit quality, increase delinquencies, and impact earnings for issuers and subprime lenders.”
Prediction Markets Drive a New Wave of Speculation
The strategists pointed to the surge in online betting since the 2018 federal ban lift. BoA notes that prediction-market platforms such as Kalshi and Polymarket are “creating a new form of speculative engagement.”
Their financial contracts, BofA wrote, are tied to “sports games and other events.” Their adverse effects “may be most pronounced for low-income consumers and especially for young men.”
The warning arrives as prediction markets rapidly expand sports event contracts, which many argue mimic sports betting. Earlier in November, BoA downgraded DraftKings and Flutter, citing exposure to margin pressures and competition in prediction markets.
Research Shows Financial Harm Over Time
BofA further increased its concerns by pointing to research from UCLA Anderson and USC. It found that in states allowing online betting, “the average credit score drops by almost 1% after about four years, while the likelihood of bankruptcy increases by 28%.” The same study concluded that “the amount of debt sent to collection agencies increases by 8%.”
BoA also cited consumer self-reporting. That includes a U.S. News survey in which “one in four bettors report missing bill payments“. Also, according to the survey, 45% say they don’t have money to cover living costs for three to six months.”
BofA added that the marketing tactics of betting products “amplify participation and translate into rising credit balances and increased loss severity for lenders.”
Subprime Borrowers and Specialty Lenders Most Exposed
BoA warned that lenders in the sector— including Bread Financial, Upstart, and OneMain — are most exposed to “lower income or credit-stressed consumers.”
The strategists wrote: “Online betting markets introduce a new risk for lenders, one that they have not had to deal with historically, and underwriting models may need to be adapted.”
Bloomberg noted data by Dune Analytics showing that monthly notional trading volume across Kalshi and Polymarket “rose above $8.5 billion for the first time in October.” The explosion of sports-event contracts on Kalshi’s exchange was a primary driver of the results.
Kalshi continues to operate nationwide under a federal CFTC license, even in states that classify sports prediction markets as gambling. That has led to numerous lawsuits.
Kalshi disputed the idea that its platform harms users. Spokesperson Jack Such told Bloomberg: “As a federally regulated financial exchange, Kalshi’s model provides fairer, more transparent pricing and doesn’t extort consumers like casinos do. Since we aren’t a ‘house,’ our revenue doesn’t come from customer losses.”
Polymarket “cleared one of the final regulatory hurdles to reopen in the US,” Bloomberg noted.
A Debate Over Financial Harm Intensifies
The long-term financial impact of legalized wagering remains a subject of debate. A recent study by the Progressive Policy Institute found no evidence to suggest that the rise of legal sports betting in the US has affected credit scores.
Still, Bank of America stressed that risk is not evenly distributed. According to strategists, the most severe effects cluster among young men and subprime borrowers. These are groups that now trade billions on prediction-market platforms each month.
With event-contract trading accelerating, they warned that lenders may soon need to incorporate this behavior directly into credit-risk models.










