Banks and capital markets firms have moved past asking whether AI is useful — most now run generative AI somewhere in the business. The hard part in 2026 is different: getting models out of the pilot and into production under model risk management, fair-lending scrutiny, and the EU AI Act — without a black box your examiners will reject. We build finance AI that ships, with the governance baked in from the first design review.
Banking and capital markets sit on decades of legacy core systems, the most sensitive customer data in any industry, and a regulator watching every automated decision. That is exactly why so much finance AI stalls in pilot. The constraints below are not edge cases — they decide whether a model ever reaches production.
In April 2026 the Federal Reserve, OCC and FDIC issued revised model risk guidance — SR 26-2 supersedes SR 11-7 — and supervisors now expect AI and generative models to carry the heaviest governance: independent validation, ongoing monitoring, and documented controls. A model with no validation evidence is a model your examiner stops.
Under US fair-lending rules the CFPB requires specific, accurate reasons for any adverse credit action — a black-box score that cannot explain a decline is not usable. The same standard applies to AML alerts an analyst must defend. Every decision model needs reasons a human and a regulator can read.
Under Annex III, AI used for creditworthiness and certain insurance pricing is classified high-risk. From August 2026 those systems require conformity assessment, data governance, logging, human oversight, and audit-ready technical documentation — with penalties tied to global turnover for getting it wrong.
Customer financial data carries GDPR, sector confidentiality, and jurisdictional residency obligations. Where data is stored, where inference runs, and what a model is allowed to see all become design constraints — not afterthoughts. The architecture has to respect them before a single prediction ships.
Fraud, scams, account takeover, and synthetic identity now move faster than overnight batch review. Detection has to score transactions in real time and adapt as attack patterns shift — while keeping false positives low enough that genuine customers are not blocked.
Most institutions run on dozens of disconnected systems built long before continuous, governed AI. Fragmented data is the single biggest reason pilots never scale. Production AI in finance lives or dies on how cleanly it connects to the core, the data warehouse, and the systems of record.
The majority of institutions now run generative AI somewhere in the business, but only a fraction have moved it from proof of concept to governed, day-to-day operation. The blockers are consistent: fragmented data foundations, legacy systems, and compliance demands that pilots were never built to satisfy. We work where that gap is, engineering finance AI that survives the validation, the audit, and the examiner.
Governed by design
Validation, monitoring, and audit evidence built in from the first design review — not bolted on for the regulator afterwards.
You own it
Code, models, prompts, and documentation are yours, designed so your risk and engineering teams can operate and validate them independently.
We work on the use cases where banks and capital markets firms see real return and where governance is non-negotiable. Each maps to a Miniml expertise team that has shipped it before — so the build, the validation, and the handover come from people who have done it in a regulated environment.
Onboarding packs, loan files, ISDAs, and credit agreements still arrive as PDFs and scans. We build extraction that reads them accurately, flags the exceptions, and cuts onboarding from days to hours — with provenance back to the source page.
Document intelligence →Decisioning that scores creditworthiness on far more signal than legacy models — with the reasons attached to every outcome, so an adverse action can be explained to the customer and defended to the regulator.
Decision intelligence →Grounded assistants over filings, research, market data, and internal documents — so analysts and relationship managers find, summarise, and cite faster. Every answer carries a source the human can check before it leaves the desk.
Knowledge & retrieval →Agentic workflows that resolve trade breaks, reconciliations, and operational exceptions with a human in the loop on the cases that matter — the operations work where AI is already running in production across the industry.
AI agents →Real-time models that score transactions and behaviour as they happen, adapt to new fraud and scam patterns, and forecast credit and portfolio risk — tuned to catch loss without burying genuine customers in false positives.
Predictive analytics →The layer that gets the rest into production: independent validation, monitoring, explainability, and audit-ready documentation aligned to SR 26-2 and the EU AI Act — built so your model risk team can own and defend it.
AI governance →In finance, a model that cannot be validated, explained, or audited is a model that does not ship. We treat the controls below as part of the build — not paperwork added at the end to satisfy an examiner.
Documentation a model risk team can take into independent validation without rebuilding it.
Decision reasons attached to every prediction, not reconstructed after a complaint.
Performance, bias, and drift watched continuously — with alerts before a model degrades in production.
Technical documentation, logging, and human-oversight controls mapped to EU AI Act high-risk obligations.
Usually for three reasons: fragmented data spread across legacy core systems, governance that was never designed in, and a model no one can validate or explain. Most finance AI fails the audit, not the demo. We work backwards from production — the validation, the explainability, and the data integration — so the pilot is built to ship from day one.
We build the evidence as we build the model: documented assumptions, independent validation support, ongoing performance and bias monitoring, and clear controls. In April 2026 the Fed, OCC and FDIC issued revised guidance — SR 26-2 supersedes SR 11-7 — and supervisors expect AI models to carry the heaviest governance. We design to that bar, then hand it to your model risk team to own.
If they evaluate creditworthiness or set certain insurance pricing, yes — Annex III classifies them high-risk, with obligations phasing in from August 2026. That means conformity assessment, data governance, logging, human oversight, and audit-ready documentation. We map your systems to those requirements and build the controls in, rather than retrofitting them under deadline pressure.
It has to be. US fair-lending rules require specific, accurate reasons for any adverse action, and a black box that cannot explain a decline is not usable. We use models and techniques that produce reason codes a customer-facing officer, an analyst, and a regulator can all read — explainability is a build requirement, not an add-on.
It stays where your obligations require. We design for residency, confidentiality, and GDPR from the architecture up — running on your cloud, your tenancy, or your private infrastructure, with access scoped so a model only ever sees what it is permitted to. Where data sits and where inference runs are design decisions, made before any prediction ships.
Yes. The code, the models, the prompts, and the documentation are yours, designed so your engineering and model risk teams can operate, validate, and extend them independently. Enablement is part of the engagement — we do not build something only we can maintain.
A 30-minute conversation with a senior consultant. Bring a model stuck in proof of concept, a use case your risk team keeps blocking, or a regulatory deadline you need to be ready for. We’ll tell you what it takes to get it into production — governed, explainable, and built for you to own.
Book a consultation →