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Prediction Markets and Payments: Could Derivatives on Transaction Flows Become a New Asset Class?

ttransactions

2026-01-26

11 min read

Could derivatives on authorization rates, interchange spreads, and fraud metrics become tradable assets? Explore market design, hedging, and regulation.

Hook: Payments teams can't hedge what they can't trade — but that may be changing in 2026

Rising interchange costs, unpredictable authorization dips, and episodic fraud spikes are eroding margins and creating operational volatility for merchants, issuers, and acquirers. What if those payment-flow risks could be packaged, priced, and traded — like interest-rate swaps or commodity futures — enabling direct hedges and new liquidity pools? With Goldman Sachs publicly exploring prediction markets in early 2026, this possibility has moved from niche experiment to plausible market innovation.

The thesis in one line

Prediction markets and payments derivatives could create a new asset class priced off transaction-flow metrics — authorization rates, interchange spreads, and fraud incident indices — unlocking liquidity and hedging for the payments stack while forcing new regulatory and data-quality disciplines.

Why 2026 is different: context and catalysts

Several 2025–2026 developments converged to make payments-linked derivatives realistic:

Major financial institutions, notably Goldman Sachs, signaled interest in prediction markets on Jan 15, 2026, publicly validating institutional appetite for event- and metric-based trading.
The maturation of high-fidelity payments observability platforms and real-time rails (RTPs) means metrics like authorization rates are measurable at scale and with low latency.
Advances in oracle design, tokenization, and custody reduce frictions for on-chain settlement while regulated venues and OTC derivatives infrastructures adapted to tokenized representation of economic exposures.
Market participants — merchants, fintechs, and hedge funds — faced sharper volatility in interchange and fraud throughout 2024–2025, raising the commercial case for hedging tools tied to payments KPIs.

How payments derivatives and prediction markets would work (practical framework)

We can think of three building blocks: the metric, the contract, and the settlement/oracle layer.

1) Define the metric (what to trade)

Clear, auditable definitions are essential. Candidates:

Authorization rate — authorized transactions divided by authorization attempts, defined by time window, BIN, card network, or merchant cohort. Instrumenting and forecasting these metrics benefits from forecasting and analytics tooling (see forecasting platforms).
Interchange spread — average basis points paid over a benchmark (e.g., Visa/Mastercard weighted average vs. a published reference) for a merchant cohort.
Fraud incidence index — fraud chargebacks or confirmed fraud volume per 10k transactions, possibly normalized by merchant category code (MCC) or region.

2) Standardize contracts (what pays)

Contracts map metric outcomes to payoffs. Examples:

Futures — monthly settlement based on realized authorization rate for a merchant cohort. Payoff = notional × (realized rate − strike).
Binary/event — pays 1 if fraud incidents exceed a threshold during a quarter (a prediction-market style contract).
Spread swaps — a swap where one leg pays the observed interchange spread vs. a fixed coupon.
Option-style hedges — put options protecting merchants from authorization-rate declines below a strike.

3) Reliable settlement & oracles (how it pays)

Settlement must be tamper-resistant and auditable. Options include:

Network-sourced oracles — formal API feeds from card networks or acquirers with signed attestations.
Multi-source aggregation — combine acquirer, gateway, and processor feeds to reduce single-source manipulation risk; secure collaboration platforms for provenance and logs help here (data governance & provenance).
On-chain attestations — cryptographic proofs of aggregated metrics with time-stamped anchors for decentralized settlement; these need low-latency, edge-aware hosting and anchoring (edge hosting patterns apply).
Regulated clearing — centralized clearinghouses that net positions and guarantee settlement for large institutional players; expect early pilots to interact with marketplace and platform policy changes (marketplaces policy changes).

Role for Goldman Sachs and other institutions

When Goldman’s CEO called prediction markets "super interesting" in January 2026, it underscored three institutional roles:

Market formation — banks can seed liquidity, quote two-way markets, and sponsor standardized contracts.
Prime services — offering clearing, margining, and capital-efficient leverage to hedge funds and corporates; distributed teams and ops platforms will be needed to scale pilots (remote-first ops).
Product design — leveraging data science to structure contracts that match merchant and issuer risk profiles; product architects should study AI-driven deal matching and data-driven bundling techniques for pricing and hedging.

Who benefits — by stakeholder

Merchants

Merchants could hedge authorization-rate risk (e.g., during peak promotions or entry into new markets) and swap variable interchange exposure into fixed-cost structures. That can stabilize gross margins and improve pricing certainty for CFOs.

Issuers and acquirers

Issuers could monetize proprietary data by creating indexed products. Acquirers can hedge cohort-level fraud spikes that cause chargeback volatility. Analytics and tooling vendors (think tools that help teams find signals and price risk) will play a central role — see our roundup of practical tooling for marketplace and payments teams (tools roundup).

Traders and macro funds

New instruments provide diversification. Correlation trades — e.g., trading interchange spreads against consumer credit spreads — become possible.

Fintechs and data providers

Payment analytics companies can supply the raw metrics oracles need, creating a data-monetization runway.

Liquidity and market structure: how deep could these markets be?

Liquidity will depend on three things: economic exposure, standardization, and regulatory clarity.

Economic exposure — the more firms have direct balance-sheet sensitivity to a metric (e.g., merchants to authorization rates), the larger the natural hedgers and the deeper the market.
Standardization — standard contract definitions and settlement mechanics enable intermediation and competitive quoting.
Regulatory clarity — once regulators classify these contracts (securities, commodities, or something new), institutional participation and clearing will increase.

In an early phase, liquidity will likely be concentrated in OTC bespoke swaps. If standardized (monthly authorizations for top 10 merchant cohorts by volume), exchange-traded contracts could follow — especially if banks underwrite two-way books.

Hedging strategies: practical examples

Here are three realistic hedging playbooks payments teams should consider implementing now.

1) Merchant hedging authorization risk

Situation: A retail chain expects a 10% uplift in card volume from a new loyalty promotion but fears a BIN-blocking issue will reduce authorization rates by 2–3 percentage points.

Buy a monthly authorization-rate put for the merchant cohort (notional sized to expected incremental gross margin).
If realized authorization rate falls below strike, the contract pays out, offsetting lost incremental sales.
Combine with operational controls: pre-promotion stress testing with acquirer and tokenization providers.

2) Acquirer hedging fraud spikes

Situation: An acquirer underwrites a lot of BNPL and wants protection against a regional fraud outbreak.

Enter a binary contract paying out if fraud incidents per 10k transactions exceed a threshold within a region/month.
Use proceeds to fund chargeback reserves or buy reinsurance-style protection.

3) Interchange spread swaps for treasury optimization

Situation: A merchant with thin margins wants to cap drift in interchange expense relative to budget.

Swap variable interchange spread against a fixed coupon for the fiscal quarter.
Net periodic settlement against actual interchange ledgered by the acquirer.

Data integrity and market abuse: key risks and mitigations

New financial incentives create new attack vectors. If a payment-flow metric becomes the basis of tradable securities, actors might attempt to manipulate transaction streams.

Manipulation vectors

Artificially inflating or suppressing authorization attempts (e.g., scripted low-value attempts) to alter authorization rate measurement — a variation on classic fraud vectors discussed in fraud prevention & border security.
Colluding with merchants/gateways to delay reporting or reclassify transactions.
Submitting false attestations or compromising oracles.

Mitigations

Multi-source oracles — require matching signatures from at least two independent processors.
Time-delayed settlement windows — short delays reduce incentive for transient manipulation while preserving usefulness for hedging.
On-chain proofs & cryptographic anchors — anchor aggregated metrics with signed Merkle roots from processors; these approaches benefit from low-latency edge and anchoring infrastructure (edge hosting patterns).
Surveillance and position limits — exchanges or clearinghouses monitor for suspicious trade patterns and apply limits; expect policy updates and platform rule changes to follow early pilots (marketplaces policy changes).

Regulatory oversight: the unavoidable complexity

Payments derivatives and prediction markets straddle multiple regulatory domains. Expect scrutiny on at least four fronts:

Securities vs. commodities — regulators will assess whether these contracts are derivatives subject to CFTC/SEC oversight; classification affects market access and disclosures.
Market integrity — anti-manipulation rules, audit trails, and surveillance obligations will mirror those for other derivatives markets.
Data privacy and confidentiality — transaction-level data are often protected; anonymization and aggregation standards are necessary to avoid violating card network rules or privacy laws. See practical consent and continuous-authorization approaches in consent capture playbooks.
AML/KYC — prediction markets have historically drawn AML scrutiny; participants and platforms must implement robust KYC, sanctions screening, and suspicious-activity reporting.

Practical compliance steps for pilot sponsors:

Engage regulators early and seek no-action relief or pilot exemptions where possible.
Work with card networks and acquirers to create permissible aggregated data schemas.
Design contracts to avoid disclosing transaction-level data while offering sufficient resolution for hedging.
Implement layered KYC and AML checks for market participants; expect institutional counterparties to demand regulated clearing.

Case study (hypothetical): Goldman-backed authorization-rate futures

Scenario: In mid-2026, Goldman launches a pilot for monthly authorization-rate futures for large e-commerce cohorts. Key features:

Settlement based on a multi-source index compiled from three processors and the merchant’s acquirer; index calculation rules published in advance.
Goldman provides initial two-way quotes and acts as a central liquidity provider; prime brokerage and margining offered to hedge funds and corporate clients.
Contracts cleared through a regulated CCP with position limits and standardized dispute resolution.

Outcomes after the pilot:

Merchants hedge peak campaign risk, reducing promotional downside.
Acquirers and processors refine telemetry—creating better real-time observability that also improves fraud detection (fraud prevention learnings).
Regulators iterate on guidance: clarity on data reporting and capital treatment accelerates institutional adoption.

Market design considerations — what product architects must get right

Product success depends on aligning incentives and minimizing arbitrage that undermines the metric’s economic meaning. Key design choices:

Granularity vs. fungibility — finer cohorts (per-MCC, per-BIN) are valuable for precise hedging but fracture liquidity. Start broader, then add granularity as the market deepens.
Lag and lookback windows — shorter windows enhance timeliness but increase noise; choose horizons matching hedging needs (e.g., daily for operations, monthly for P&L hedges).
Price discovery — incentivize market makers and publish order-book data to foster fair pricing; marketplace-grade forecasting and market data platforms can help (forecasting platforms).
Dispute resolution — clear arbitration for data disagreements is critical to market confidence.

Advanced strategies and cross-asset plays

Once liquid, payments derivatives unlock sophisticated trades:

Basis trades — exploit mispricings between a merchant's actual authorization experience and the nearest liquid authorization-rate futures contract.
Correlation overlays — trade fraud-index contracts against cybersecurity equities or insurance spreads.
Macro hedges — incorporate payments metrics into broader macro models (e.g., authorization declines as early indicators of consumer distress in emerging markets).

Practical checklist: how to prepare (for payments teams, treasurers, and compliance)

Actionable steps you can start today:

Instrument and baseline your metrics — ensure you can report authorization rate, interchange expense, and fraud incidence with consistent definitions and API access. Consider tooling reviews and market analytics platforms (forecasting & analytics).
Engage data partners — contractually secure signed, auditable feeds from processors and acquirers or use certified analytics providers.
Run scenario analysis — quantify P&L sensitivity to metric moves and size hypothetical hedges.
Talk to prime brokers and banks — assess appetite for two-way quotes and understand margin/collateral requirements; coordinate across distributed teams and ops platforms (remote-first productivity).
Review legal classification — consult counsel on whether prototypes will be treated as securities, commodities, or bespoke OTC derivatives under local law.
Design data governance — anonymize where necessary, and build provenance logs and dispute-resolution processes into data agreements. Platforms that operationalize secure collaboration can accelerate readiness (data governance playbook).

Objections and counterarguments — realistic limits

Not all payments metrics will be good underlyings. Challenges include:

Measurement noise — small merchants have volatile authorization rates that make hedges prohibitively expensive; robust forecasting tooling helps here (see platform reviews).
Privacy constraints — some networks restrict sharing of transaction-level data, limiting indices’ granularity.
Regulatory friction — if regulators treat certain contracts like securities, the compliance overhead may deter market makers.
Potential for perverse incentives — players could prioritize market outcomes over customer experience unless governance is tight.

Future prediction (2026–2028)

By 2028 we expect a tiered market evolution:

2026–2027: Pilot OTC markets and pilot exchange products supported by banks and processors. Institutional players provide liquidity but with conservative position limits.
2027: Regulatory frameworks begin to solidify as pilot data reveal manipulation risks and data governance templates. Standardized monthly authorization and fraud-index contracts emerge.
2028: Sufficient liquidity and regulatory clarity allow for exchange-traded instruments and integration with corporate treasury hedging stacks. New hedge funds specializing in payments risk appear.

Closing: the strategic implications

If payment-flow metrics become tradable at scale, the results will be profound: merchants gain ways to lock in margin certainty; acquirers and issuers can manage operational volatility with financial tools; and investors gain a novel asset class tied directly to real-economy transaction flows. But success depends on rigorous metric design, robust oracles, and clear regulatory guardrails. Goldman's public interest in prediction markets in 2026 signals institutional capital is ready to test these boundaries — and payments teams should be preparing now.

"Prediction markets are super interesting," — David Solomon, Goldman Sachs CEO, January 15, 2026. (Source: PYMNTS)

Actionable takeaways

Instrument your KPIs with signed, auditable feeds today — treat them as primary financial data.
Design pilots around broadly defined cohorts to balance hedging usefulness and liquidity.
Prioritize oracle security and multi-source validation to make your products credible and defensible.
Engage regulators early and prepare for AML/KYC and data-privacy obligations.
Plan for phased adoption — OTC bespoke contracts first, standardization and exchange-trading later.

Call to action

Ready to explore how payments derivatives could fit into your risk stack or portfolio? Contact our transaction analytics desk to run a customized scenario analysis, get a contract template, or join a 2026 pilot. Early movers will shape the standards — don't wait until the market sets them for you.

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