AI’s Next Act Is Financial Decision-Making

Last year, Big Tech companies purchased 68.4 million permanent carbon removal credits, a 181% year-over-year increase, according to CNBC.

Pricing is increasingly being shaped by artificial intelligence (AI) systems that determine what credits are worth and how capital gets deployed, compressing decisions that once took weeks into seconds.

That dynamic is no longer confined to environmental markets. Across retail, energy and legal sectors, AI has quietly taken over the point of financial execution, determining when, how and to whom money moves, in real time and largely without regulatory scrutiny. The question is no longer whether AI is making financial decisions; it is whether anyone is watching when it gets them wrong.

Retailers were among the first to hand AI the keys to financial judgment. Platforms now use AI to decide whether a return is accepted, partially refunded or rejected, and many issue refunds before a returned item reaches the warehouse. According to a March report by Modern Retail, fraudsters increasingly deploy AI to generate fake damage photos, fabricated receipts and false documentation to claim refunds.

The risk cuts both ways: models that approve refunds too easily generate losses at scale, while models that deny too aggressively fuel disputes that erode brand trust. Refund timing, eligibility and loss allocation between merchants and consumers now run through models that operate at the moment of transaction with no human in the loop.

The stakes are not abstract. As reported by the National Retail Federation, total retail returns are projected to reach $849.9 billion in 2025, with an estimated 19.3% of online sales expected to come back. At that volume, a few percentage points of model error move billions of dollars in the wrong direction.

Consumer behavior is accelerating the shift. According to PYMNTS Intelligence, 54% of U.S. adults now use AI for personal tasks, with the average user relying on two to three different tools. Among power users, more than six in 10 access AI primarily through a smartphone app, confirming that AI has moved from occasional browser experimentation to habitual daily behavior. Every additional touchpoint where consumers engage with AI expands the surface area where AI can trigger or influence a financial outcome.

The same pattern is reshaping how financial instruments are priced and how liabilities are resolved, this time in markets with even larger transaction values.

In environmental commodity markets, AI-powered platforms already govern real-time pricing and automated settlement of carbon credits and renewable energy certificates. As covered by Xpansiv, its CBL exchange, the world’s largest spot marketplace for environmental commodities, runs matched transactions on a same-day settlement cycle integrated with 17 global registries, deploying AI across price discovery, risk management and settlement forecasting.

When that infrastructure misprices a credit or miscalculates a settlement, the error flows directly into the books of the financial institutions, corporate buyers and fund managers that trade on it. According to data cited by Carbon Credits, the global carbon market exceeded $1 trillion in 2025, making the accuracy of AI-driven pricing a material question for balance sheets across multiple industries.

Legal services present a parallel case. Law firms, insurers and litigation finance platforms now use AI to assess case outcomes, model settlement ranges and determine whether to fund a claim at all. As covered by the National Law Review in January, by the end of 2026, litigation intelligence is projected to operate as a continuous, predictive infrastructure, determining whether to file, where to file, when to settle and for how much, in near real time.

According to Bloomberg Law, the global litigation funding market could grow to nearly $50 billion by the mid-2030s. When AI governs whether a claim receives funding and at what valuation, it controls the timing and size of financial transfers, performing the same function a credit committee performs at a bank.

The thread connecting retail, energy and legal is not sector-specific disruption. It is a payments infrastructure shift. Retailers, environmental trading platforms and litigation funders now automate decisions that once required human financial judgment, making them de facto financial actors controlling the timing, amount and direction of money movement, without holding a banking license or operating under banking oversight.

That creates a specific and scalable risk. When a model makes an error, money moves incorrectly at speed across thousands or millions of transactions before anyone detects the problem. The efficiency that makes AI-driven payments attractive is exactly the mechanism that amplifies the damage when something goes wrong.

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