Why competitive intelligence is different in fintech

From the outside, fintech looks like B2B SaaS wearing a different logo — subscription pricing, self-serve trials, familiar sales motions. Underneath, three layers decide who actually wins a deal, and none of the standard competitive intelligence playbook was built to reach them.

Most competitive intelligence methodology is written for a generic B2B SaaS company, and most of it transfers reasonably well to fintech. Feature comparisons work. Pricing intelligence works, in the sense examined elsewhere in this series. Demo intelligence works largely unchanged. The buying journey, on the surface, follows a familiar shape: discovery call, demo, pilot, procurement, close.

Underneath that surface, fintech is governed by structural forces almost entirely absent from a typical SaaS competitive landscape — financial services regulation, dependency on a licensed banking partner, and a security posture that a buyer can never directly verify. Each of these layers shares a specific, unusual property: it is technically disclosable, and practically inaccessible. Understanding why that distinction matters requires examining each layer against the research that explains it.

Quasi-public information: the category standard CI methodology misses

Elsewhere in this series, the central distinction has been between explicit knowledge — codified, published, publicly accessible — and tacit knowledge, which exists only in human memory and conversation, following Michael Polanyi's foundational framing of information that "cannot be fully articulated."1 Fintech introduces a third category that sits uncomfortably between the two: information that is technically public, held in a regulatory register or a partnership footnote, but practically inaccessible because it is fragmented, poorly indexed, or requires specialist interpretation to extract any strategic meaning from it.

Call this quasi-public information. It is not protected. No confidentiality agreement bars its disclosure. It is, in principle, available to anyone who looks. In practice, almost no one does — because looking requires knowing which of dozens of regulatory bodies to check, how to interpret a filing correctly, and critically, what a given disclosure actually signals about a competitor's strategic intent. This is the layer where fintech competitive intelligence diverges most sharply from the standard SaaS playbook.

Regulation as a barrier to entry — and a source of blind spots

Michael Porter's foundational framework on competitive strategy identifies barriers to entry as one of the central forces shaping industry structure, and regulatory licensing is among the clearest examples of a structural barrier that simultaneously protects incumbents and obscures competitor intent from outside observers.2 A banking licence, an e-money institution authorisation, or a state-by-state money transmitter licence portfolio takes years and significant capital to assemble. This is precisely why it functions as a moat — and precisely why monitoring it correctly, rather than merely noting that a competitor "is regulated," carries disproportionate strategic value.

The practical difficulty is fragmentation. There is no single, unified global register of fintech regulatory status. A competitor's UK authorisation sits with the FCA. Their US money transmission licences are scattered across dozens of individual state regulators, each with different disclosure formats. Their EU passporting status is a separate notification entirely. Reconstructing a coherent picture of a competitor's actual regulatory footprint — and, more importantly, what gaps in that footprint reveal about where they cannot yet compete — requires the kind of dedicated cross-referencing that a monitoring dashboard, built for website change detection, was never designed to perform.

Banking-as-a-service: the dependency nobody advertises

Most fintech products that touch card issuance, payment processing, or deposit accounts are not, technically, banks. They operate on top of a licensed banking partner's infrastructure — a banking-as-a-service, or BaaS, arrangement — using that partner's charter to legally offer regulated financial products. The identity and health of that relationship is one of the single most consequential pieces of competitive information in the entire fintech landscape, and it is almost never disclosed with any clarity.

The reasons a competitor's BaaS relationship matters are direct and structural. The sponsor bank's risk appetite constrains which customer segments the fintech can serve. The commercial terms of that partnership shape the fintech's real cost base — and therefore their genuine capacity to discount, independent of what their pricing page displays. Regulatory scrutiny of the banking partner, which has increased materially across UK and US markets in recent periods, can constrain or destabilise the fintech's ability to onboard new customers with little public warning. A competitor whose sponsor bank relationship is under strain may be structurally unable to win the exact deal you are competing for, regardless of how their sales team presents in the room — and no monitoring tool will tell you that.

"The identity and health of a BaaS relationship is one of the most consequential pieces of competitive information in fintech — and it is almost never disclosed with any clarity."

Why fintech buyers trust a badge they cannot verify

The heavy reliance on compliance certification in fintech sales cycles — the SOC 2 report, the ISO 27001 badge, the PCI-DSS attestation — is not a quirk of the industry. It is a textbook instance of a well-studied economic problem: transacting under conditions where one party cannot directly observe the quality of what the other party is offering.

George Akerlof's 1970 analysis of markets with unobservable quality — developed using the used car market but applicable far more broadly, and later recognised with a Nobel Memorial Prize in Economic Sciences — established that when buyers cannot verify true quality before purchase, markets are vulnerable to adverse selection: without a reliable signal, buyers cannot distinguish a genuinely secure, well-run fintech platform from one merely claiming to be.3 Michael Spence's subsequent 1973 signaling theory, also Nobel-recognised, provided the resolution: a party with unobservable quality can credibly signal it by incurring a cost that a lower-quality competitor would not find worthwhile to bear.4

A SOC 2 Type II audit is expensive, time-consuming, and cannot be meaningfully faked. That is precisely why it functions as a credible signal, and precisely why fintech buyers — particularly compliance and risk functions evaluating a vendor on behalf of a regulated institution — lean on it so heavily. But a signal, by its economic nature, confirms only that the cost was paid. It does not disclose the scope of what was actually audited, the exceptions noted in the underlying report, or whether the certified state of the system a year ago still reflects its current operational reality.

RESEARCH CONTEXT

The signaling framework explains a specific, exploitable gap: two competitors can display an identical ISO 27001 badge while one has audited their entire production environment and the other has scoped the audit narrowly around a single, low-risk subsystem. The badge alone cannot distinguish them. The audit scope statement can — and it is rarely published anywhere the badge itself appears.

A different buying committee, a different loss pattern

Fintech sales into banks, regulated enterprises, or other risk-sensitive institutional buyers routinely involve a buying committee structurally different from a typical SaaS evaluation — a dedicated compliance or risk function sits alongside the economic buyer, frequently with effective veto power regardless of how well the commercial conversation has gone.

This has a direct connection to the win-loss distortion examined elsewhere in this series. A deal lost because a risk function raised an unresolved concern about data residency, sub-processor exposure, or certification scope frequently gets logged in the CRM as lost to "price" or "timing" — because the rep involved in the commercial conversation may never have been given full visibility into why the compliance function actually said no. The self-serving and socially desirable attribution defaults to the familiar, external cause, even more readily than in a standard SaaS loss, because the true reason was often decided in a conversation the rep was not even part of.

What a fintech-specific intelligence engagement should cover

The standard battlecard framework examined elsewhere in this series — positioning, pricing, demo script, objection handles, competitive weakness — remains the correct foundation. A fintech-specific engagement adds a distinct layer on top of it.

ADDITIONAL LAYER FOR FINTECH COMPETITIVE INTELLIGENCE

On top of the standard battlecard framework

1. Regulatory status and pipeline

Confirmed licences across relevant jurisdictions, applications known to be in progress, and — critically — what each one strategically unlocks for the competitor's roadmap.

2. Banking or BaaS partner relationship health

The identity of the sponsor or issuing bank behind the competitor's product, and any signal of strain, exclusivity, or renegotiation in that relationship.

3. True certification scope

What a SOC 2 or ISO 27001 report actually covers, versus what the badge alone implies to a buyer who has not read the underlying document.

4. Compliance-buyer objection patterns

The specific risk and compliance objections a competitor has learned to pre-empt in front of a regulated buyer — distinct from, and often more decisive than, the economic-buyer objections a standard battlecard covers.

5. Real unit economics

The interchange, FX spread, and funding cost structure underneath a competitor's published pricing — the genuine constraint on how far they can move in a competitive negotiation, distinct from a simple discount floor.

A PATTERN ACROSS FINTECH ENGAGEMENTS

A pattern recurs consistently across fintech competitive intelligence work: organisations that have thoroughly mapped a competitor's product feature set frequently have no clear picture of their regulatory footprint or banking dependency at all — the two layers most likely to actually constrain what that competitor can do next.

A feature comparison tells you what a competitor has built. Regulatory and banking-partner intelligence tells you what they are structurally capable of doing — which, in a regulated market, is very often the more decisive question.

The through-line to the rest of this series

The core argument of this series has been that the intelligence deciding competitive outcomes is disproportionately tacit — held in human memory and conversation rather than published documentation. Fintech does not contradict that argument. It adds a variant of it: a layer of information that is technically public but practically requires the same disciplined, specialist investigation as tacit knowledge to actually extract and interpret. Standard monitoring software, built to watch for website and pricing changes, was never designed to reach either layer. Reaching both is what a genuinely fintech-literate competitive intelligence capability is for.

REFERENCES

1. Polanyi, M. (1966). The Tacit Dimension. Doubleday. University of Chicago Press edition, 2009.

2. Porter, M.E. (1980). Competitive Strategy: Techniques for Analyzing Industries and Competitors. Free Press.

3. Akerlof, G.A. (1970). The market for "lemons": Quality uncertainty and the market mechanism. Quarterly Journal of Economics, 84(3), 488–500.

4. Spence, M. (1973). Job market signaling. Quarterly Journal of Economics, 87(3), 355–374.