How AI Is Transforming Contract Reviews for Fintech Partnerships

Every quarter there are deals that die between fintech partners not because of the economics, not because of the trust, but because the contract is sitting in some inbox awaiting the legal review that takes a few more days than it should.

That delay is a revenue problem and, in 2026, an increasingly competitive one.

This article is for business executives who want to move faster with lower risk in this area but have not yet prioritized it. Read on to learn how AI is changing the calculus and what actually separates firms that do this well from those that are effectively paying an invisible tax on every deal they close.

The Scale of the Problem Most Leaders Underestimate

Deloitte, in a survey of more than 1,000 business leaders across the globe, estimates that poor agreement management collectively costs almost $2 trillion in global economic value every year. A single contract can have more than 15 internal handoffs before it is sent to the counterparty for negotiation.

Companies end up spending far too much time on agreements, and the drag amasses with every partnership, vendor, and customer agreement that a business engages with over a year.

For many fintechs, this is a structural disadvantage, as their business models rely on partnerships with banks, payments networks, data providers, and other ecosystem players.

Why Legal Became the Bottleneck

Furthermore, scaling fintechs generate huge amounts of contracts (NDAs to potential clients, data processing agreements, master service agreements, SLAs, security questionnaires, side letters, reseller agreements, and banking network access agreements), and no two counterparties use the same templates.

Legal teams were never designed to handle this at scale with consistent speed and quality. They were designed to handle this correctly. That is a very different goal. 

The end result is what nearly every fintech executive already knows by heart: deals stall while contracts sit in review, two different lawyers will sometimes reach different conclusions on the same clause, and the audit trail for why a particular position was accepted is buried in email threads, if it exists at all.

The part of this that causes the most downstream risk is when review decisions differ because of the reviewer rather than because of policy.

Finally, in terms of regulation, with the EU’s DORA coming into effect in early 2025 and the AI Act likely raising the bar on explainability and traceability, this liability is not welcome.

What AI Actually Changes (And What It Doesn’t)

People have unrealistic expectations of what AI can do with contracts. AI does not replace lawyers. It does not make judgment calls on complex risk trade-offs.

What it does, if done correctly, is eliminate the part of the process that takes the most time and produces the least amount of value: that first reading, the identification of clauses, the search against your standard positions, and the sorting of what you need to act on.

For example, a study by the legal technology company LawGeex found that AI was able to identify mistakes in NDAs 94% of the time, while experienced lawyers did so 85% of the time. AI achieved these results in one-fifth the time.

Across the industry, the operational impact follows a clear pattern: firms adopting AI-driven NDA automation shorten cycle times and cut post‑signature disputes within months. For a fast way to get started, explore templates and guided workflows at createmynda.com.

In practice, it usually means the AI reads the contract, tags and compares each provision against your encoded standard positions, and flags the provisions that don’t comply with your standards. It gives you a risk rating of low, medium, or high and an explanation for each flagged provision. But even the most basic reviewer is working from a structured brief that shows them exactly where the nonstandard language is and what your firm’s approved fallback positions are.

The time saved on low-risk variants can be dramatic, and the result is that the higher-risk clauses are much more rewarding to humans than pages of boilerplate.

Equally important: every flag, every decision, every override is logged, and that log becomes your audit trail without anyone having to build it separately.

The Three Things That Determine Whether AI Works

Most implementations fail or are underwhelming not because of the AI technology itself but rather because of how the firm prepares for it. Here’s how to differentiate.

Policy clarity comes before automation

If two reviewers at your company disagree about whether they really like this indemnification clause, no AI is telling you which one is right. AI-assisted review requires very precise definitions about what is a green-light clause, what is a yellow-flag clause that requires further discussion and senior review, and what is a red-line clause that should be escalated or renegotiated.

That exercise is valuable independent of the technology because it forces the organization to have conversations it’s been avoiding and to make explicit what currently lives only in the heads of individual lawyers.

Evidence by default is non-negotiable

Regulators and auditors do not want to be told that your process is sound. They want to see it. As a consequence, if a decision was not recorded in the system at a regulated fintech, it would not happen.

Logging should be automatic. You shouldn’t have to tell them to add a logging statement at the end of a review. If you have to log by hand to create an audit trail, that’s a hole. AI-assisted review closes it, but only if you build the logging into the workflow from the start.

Integration is more important than capability

You don’t want a high-capability AI contract review tool sitting outside of your workflows being used inappropriately. The bar is whether you can see status in your CRM, whether redlines show up in your contract lifecycle management tool, and whether your security and privacy teams are automatically notified when the right kinds of clauses are highlighted. Friction present in handoffs negates the speed gains.

An integrated system that has moderate capabilities will be much better than a more advanced system that requires manual data transfer.

Where the ROI Concentrates

Not all contracts are the same, so knowing which contracts are suitable helps with prioritization.

NDAs are short, templated, high-volume, and the business case to triage low-risk NDAs is compelling. 

The risk of getting it wrong is low, and NDAs are an ideal place to build confidence with the technology and the operational pattern before expanding to higher-risk, more specialized instruments with varied clauses and terms.

The largest AI risk is in data processing agreements and security exhibits, with the largest exposures in product data residency requirements, subprocessor obligations, breach notification windows, and liability caps. Missing a nonstandard provision is extremely risky under GDPR, CCPA, and developing APAC privacy laws and regulations.

Further, while AI can find these provisions much more rapidly, the decision about how to trade off risks lies with humans.

Most revenue impact from cycle time compression will come from what is in MSAs and SLAs. The most relevant terms are indemnities, limitations of liability, uptime commitments, service credits, and termination for convenience.

It can’t replace the commercial judgment that comes with making those deals, but it does eliminate the time-consuming process of finding them, bringing up the firm’s approved fallback language at the ready.

The Leadership Question Worth Asking

The question is no longer whether a computer can read a contract. The question is whether your organization has the governance foundation it needs to make AI outputs reliable, explainable, and auditable at scale.

That means you have codified your policies into the system and left the human decision-makers to focus on the exceptions. It also means you have an evidence trail that your second-line risk function and your external auditors can examine without having to ask anyone to recreate it.

Contracts are where your partnership agreements, your regulatory commitments, and your commercial risk all converge in a single document and should not be something we simply queue up as a legal task. They are a throughput, governance, and evidence problem, and the firms that have realized this are moving faster, taking on more partnerships, and walking into regulatory examinations with an unblemished record.