AI for fintech with an audit trail on every decision
Faster onboarding, sharper fraud and anomaly detection and operations copilots for banks, lenders, payment companies and fintech start-ups, designed so that risk, compliance and audit teams can see exactly what the model did.
Financial services run on documents, transactions and questions. AI helps most where volume is high and the decision rule is already known.
KYC and onboarding document extraction
Read identity documents, proof of address and bank statements, extract fields with confidence scores and check them against application data. Touches the onboarding platform and the case management tool. Clear cases proceed; mismatches go to an analyst.
Fraud and anomaly detection
Score transactions and account behaviour against a learned baseline and surface the unusual ones with a plain-language reason. Touches the payment switch or ledger and the fraud operations queue. See fraud and anomaly detection.
Customer support and in-app assistants
Answer balance, card, dispute and status questions from verified account data, and raise a case when the customer needs a person. Touches the core banking API, the CRM and the mobile app. Built on our customer support AI pattern.
Policy, product and regulation Q&A for staff
A grounded assistant over internal policies, product terms and regulatory guidance, with citations. Touches the document store and the intranet. Compliance teams get consistent answers; the source is always one click away.
Collections and churn prediction
Rank accounts by likelihood of missed payment or attrition so outreach is timely and proportionate. Touches the data warehouse and the dialler or messaging platform. Used to prioritise contact, with the treatment decided by your policy.
Invoice and reconciliation automation
Match invoices, remittances and statements, flag exceptions and prepare journal entries for approval. Touches the ERP or accounting platform. See invoice processing.
Compliance and data
KYC, AML, model risk and auditability come first
What we design for in financial services
Financial regulators expect you to know your customer, monitor for money laundering, explain automated decisions and manage the risk of the models you rely on. We treat those as design inputs: deterministic rules for decisions, scored models with documented evaluation, LLMs for reading and drafting, a reviewer for anything flagged and a complete record of every step. Card data stays out of prompts and model logs. This is engineering guidance, not legal or regulatory advice; your compliance function owns the policy.
Model risk management translates into practical artefacts: a model inventory, evaluation results on your historical cases, drift monitoring in production and a change process for prompts and model versions. We produce those as part of the build, not as an afterthought. The AI governance and EU AI Act readiness services cover the wider framework.
Reference architecture
Flagship workflow: onboarding with document checks and risk scoring
The applicant submits documents through your app or web onboarding. An extraction step reads each document and returns structured fields with confidence. Rules and a scored risk model combine the extracted data with application data and external checks.
Clear cases complete automatically with a recorded rationale. Flagged cases land in an analyst queue with the evidence laid out: which field mismatched, which rule fired, what the model saw. The analyst's decision is recorded alongside the automated steps.
A model registry holds every model and prompt version with its evaluation results, and production monitoring watches for drift in document types, extraction confidence and flag rates. The same pattern extends to periodic reviews and transaction monitoring.
LLMs read; rules and scored models decide; analysts handle the flagged minority; everything is recorded.
Track record
What we have built that applies
Consumer apps with payments
Native mobile apps with secure authentication, card and wallet payments and real-time account views, delivered for consumer and business clients since 2006.
Eventify handles registration payments, discount rules, refunds and reconciliation for events worldwide, so we know what a payments pipeline needs to get right.
We do not recommend it. LLMs are excellent at reading documents, extracting fields and drafting explanations; decisions belong to rules and scored models you can explain and monitor, with an analyst for anything flagged. That split keeps the decision auditable and keeps the model risk conversation manageable.
How do you show regulators and auditors what the AI did?
Every automated step writes a record: input version, model version, extracted fields with confidence, rule outcomes and who reviewed what. We keep a model registry with evaluation results and monitor drift after launch. See LLMOps monitoring and AI governance.
Which models and where do they run?
Hosted models under a data processing agreement where the data classification allows, open-weight models in your cloud or on-premise where it does not. Card data stays out of prompts entirely. Our on-prem versus cloud guide covers the trade-offs.
What does fraud detection cost to run?
Scoring on structured transaction data is cheap per event; the cost sits in building good features and a labelled history. LLM-based review of flagged cases costs more per case but only runs on the small flagged fraction. We model both before you commit.
Ready to make AI work in production?
Tell us about your systems, your data and your constraints. We reply within one business day with a straight answer and a suggested next step.