AI for Financial Services

It depends on scope, integration depth, and governance. A focused, lower-liability build (e.g. document automation or an internal knowledge assistant) typically runs $30,000–$80,000; a decisioning, fraud/AML, or core-integrated system with full model-risk documentation and monitoring runs $80,000–$250,000+. We almost always start with a fixed-price PoC (typically $15,000–$25,000) that proves value on your real data and defines the validation path before you commit. Every engagement opens with a free discovery call for a realistic estimate — and an honest view of the likely ROI and the review effort involved.

A focused PoC delivers a working system on your real data in 4–6 weeks. A production capability for a single high-value use case typically takes 12–20 weeks — longer than an unregulated build because model validation, explainability evidence, and security review are part of the path, not an afterthought. The biggest variables are data-access approvals and the depth of model-risk review your use case triggers. We sequence delivery so you see working software early and the validation never becomes a surprise at the end.

For any model touching credit, pricing, or a claim, explainability is a legal precondition, so we design for it rather than bolt it on. Every consequential decision comes with the specific factors that drove it, so you can issue an accurate adverse-action reason instead of an opaque 'computer says no'. Where regulation demands it, we favour inherently interpretable models even over a marginally more accurate black box, and we produce the evidence your regulator and conduct team need to see exactly how a given customer got a given outcome — on demand, as a report you run, not a forensic exercise.

Yes — doing exactly that is the point of doing it well. Rules engines fail by flagging everything and burying investigators in false alerts while the real case hides in the noise. We build machine-learning detection that lifts genuine catch rates while cutting the false-positive queue, tuned to your risk appetite and the very different costs of a missed high-value fraud versus a false block on a good customer. Every alert carries the signals behind it so an investigator starts with a reason and can support a SAR, and we monitor and retrain as fraud typologies shift.

No — and any vendor comfortable with AI autonomously moving money or making binding decisions should worry you. Every agent has an explicit authority scope: what it may do alone, what it must escalate, and what it must refuse — enforced in the system, not left to the model's discretion. Payments, limit changes, and binding customer decisions route through a human-approval gate sized to the consequence, while low-risk, reversible steps run straight-through. Every action is logged and attributable, so the human oversight is real, and provable to an auditor.

We treat bias as a legal risk, not just a technical one. Before a decisioning model launches, we test it for disparate impact across protected groups and deliberately hunt for proxy variables — because excluding a protected attribute doesn't help if the model learned a correlated stand-in like a postcode or device. Fairness metrics are then monitored in production alongside accuracy, so a developing disparity is caught before it becomes a pattern of harm. The methodology, results, thresholds, and any remediation go into the model file for your conduct and compliance teams to defend.

Yes to both. For sensitive workloads we deploy inside your cloud tenant or fully self-hosted on open-weight models, so customer data never leaves your perimeter and no third-party API sees it — often the only architecture that clears a bank's data-protection and outsourcing review. Privacy, residency, retention, and masking are engineered in to align with GDPR and your local rules. And ownership is unconditional: the model, training data, code, and serving stack are yours; we sign NDAs before discovery, retain nothing after a project concludes, and never reuse your data to benefit anyone else.

We're a build partner, not a certification body, but we design to the controls behind the frameworks financial services rely on — SR 11-7 model risk, GDPR and local data protection, SOC 2, ISO 27001, PCI-DSS where cards are involved, DORA for operational resilience, and emerging AI-specific regulation such as the EU AI Act. We map your specific obligations to concrete system controls during discovery and produce the documentation your compliance, audit, and risk functions need — so compliance is engineered in rather than bolted on before launch.

Yes — integration is the hard part of financial-services AI and a core strength for us. We connect to core banking, card and payment rails, policy administration, CRM, and your data warehouse through whatever they expose — modern APIs, message queues, secure file transfer — and build structured wrappers for legacy systems without clean APIs so you avoid a risky migration. The AI inherits your existing entitlements and permission model rather than bypassing it, and any write-back into a system of record gets validation, idempotency, and rollback paths.

The best first use case is high-value, lower-liability, and measurable — so you prove the model and the governance approach together without a wrong answer reaching a customer's account or a credit decision. For most institutions that's document processing, an internal knowledge assistant over policy and regulation, or human-in-the-loop financial-crime triage. Starting there sidesteps the heaviest validation while proving value, and earns the internal trust to take AI toward decisioning later. We help you pick deliberately, because in finance the wrong first choice buries the programme in review before it proves anything.

Yes — and in finance the ongoing part is non-negotiable. Every engagement includes a period of post-launch support (monitoring, incident response, performance review), and retainers cover the model governance regulators expect: drift and fairness monitoring, periodic re-validation against fresh data, retraining as fraud patterns or the economy shift, and documentation updates. Because models degrade quietly rather than failing loudly, this monitoring is what keeps them inside their approved envelope — which is why we define operational and model ownership before go-live, not after.