Recent research commissioned by HTEC and carried out by Censuswide shows financial services lead all industries in AI investment growth. In the research, which includes 235 financial services and insurance (FSI) executives within a global sample of 1,500, 88% of those pursuing adoption reported higher investment than a year earlier, while only 16% measure AI risk consistently across their portfolios. Regulators, from national supervisory authorities in the EU to the FCA and the Bank of England in the UK, continue to hold each firm accountable for every model it runs, which makes the sourcing decision as much a regulatory question as an engineering one.
For FinTech firms, the sensible way through the build-or-buy debate is to settle it one layer at a time: buy commodity AI capability, own the layers that carry regulatory accountability (data, evaluation evidence, decision logic, and the audit trail), and keep every vendor replaceable behind interfaces the firm controls. How quickly a firm can swap a core model with its evidence trail intact shows whether that design works.
What does composing an AI platform mean for a FinTech firm?
A composable AI platform draws models, data services, and workflow components from several sources behind interfaces the firm owns, so any single component can be replaced without rebuilding the rest. HTEC’s guide to building or buying enterprise AI agents frames the choice use case by use case, and in financial services the question goes one level deeper, to the layers inside a single platform.
Which parts of a FinTech AI platform carry regulatory accountability?
The firm should own the layers a supervisor will question: training and grounding data, evaluation evidence built on its own chargebacks, fraud dispositions, complaint outcomes, credit and fraud decision logic, and the audit trail. The Bank of England and FCA found that 46% of UK financial firms had only a partial understanding of the AI they use, largely because of third-party models, and HTEC’s 2026 FSI research shows the oversight gap persists, with 42% of respondents weighing AI risk only informally alongside value and another 11% not measuring it at all. The rules that turn a model’s score into an approval, a decline, or a referral to a human reviewer should be written and maintained in-house, so that every decision can be traced to a specific rule that can be explained to a regulator or a customer.
Why is buying foundation models and commodity AI services usually the right call?
Specialist vendors have already handled much of the integration work needed to deploy foundation models, document extraction, transcription, and translation in practice. Internal pilots often overlook this work. HTEC’s December 2025 financial services research found 56.9 percent of leaders expecting to rely on third-party platforms and only 37 percent believing they could build the necessary capabilities internally. Our most recent survey shows that appetite is turning into spend, with Fintech and Insurance firms averaging a 35.9% increase in AI investment compared to last year.
Buying also demands visibility, since financial services executives rank security and privacy controls (35%) and cost controls with usage monitoring (30%) as the areas where they most want openness from technology partners, and an internal model gateway supplies an independent record of both. UK financial firms report that the top three providers account for 73% of cloud and 44% of model providers, so every purchase is also a resilience decision.
How do DORA and the EU AI Act reshape AI vendor strategy for financial institutions?
In November 2025, EU regulators designated 19 technology providers as critical to the financial sector under the Digital Operational Resilience Act (DORA), placing them under direct regulatory oversight. Responsibility for third-party risk still rests with each institution, and each must maintain a register of the vendors it relies on, along with a documented plan for exiting any that support an essential function. Regulation (EU) 2026/1744 postponed the EU AI Act’s high-risk credit scoring obligations to December 2, 2027, giving firms time to build logging, human oversight, and documentation, including what scoring-model vendors must supply. Financial services executives already expect this level of evidence: when asked what documentation they most want from technology partners, 29% named full traceability across prompts, logs, and data lineage, ahead of the 21% who named high-level policy statements. Replaceable components turn exit planning from a paper exercise into a tested capability.
How long would it take your firm to replace a core AI model?
A well-designed AI platform should make model replacement routine rather than disruptive. If changing providers takes months, flexibility is limited. If it can be done in a sprint while preserving performance and traceability, the platform is delivering the resilience it was built for. Three habits keep a switch affordable. First, put a gateway between your applications and the models so logging, access control, and cost tracking happen in one place. Second, negotiate contracts that protect your data and give advance warning before a version is retired. Third, run regular fallback drills so the backup option is proven before you need it. An AI-native SDLC keeps evidence flowing from delivery, and API-first modernization of legacy cores keeps surrounding systems undisturbed.
Could your team replace a core model within a single sprint?
Picture the week a foundation model provider retires the version behind the firm’s dispute resolution assistant, just as the supervisor requests evidence on a sample of its recommendations. Could your team produce that evidence, validate a replacement against its own historical cases, and cut over in a matter of weeks? HTEC works with banks, payment providers, and FinTech platforms on core modernization, data foundations, and AI architectures built for supervisory expectations of evidence and resilience.
FAQ
What is a composable AI platform in financial services?
A composable AI platform combines models, data services, and workflow components from several sources behind interfaces the institution owns. Any single component can then be replaced without rebuilding the rest or breaking the evidence trail supervisors expect.
Should a FinTech company build or buy its AI platform?
The decision is best made layer by layer: buy commodity capability such as foundation models and document extraction, and own data, evaluation evidence, decision logic, and the audit trail. Those owned layers carry regulatory accountability that a vendor cannot take on for the firm.
When do EU AI Act high-risk rules apply to AI credit scoring models?
Regulation (EU) 2026/1744, the Digital Omnibus on AI, moved the high-risk obligations for credit scoring to December 2, 2027. The extra time is best spent building logging, human oversight, and documentation, including what vendors must supply.
How does DORA affect how banks choose AI and cloud vendors?
DORA leaves each financial entity responsible for its own registers of information and exit strategies for critical or important functions, even when a provider is designated as critical. That favors vendors that can be replaced and architectures in which an exit can be tested.
Which parts of an AI platform should a bank keep in house?
Banks should keep training and grounding data, evaluation evidence from their own cases, credit and fraud decision logic, the audit trail, and the gateway that controls logging, access, and cost. These are the layers a supervisor will question.
