AI for fintech that decides in seconds, not days.

A credit risk scoring model takes structured inputs about a loan applicant and outputs a probability of default. The inputs can include traditional bureau data, credit score, payment history, utilization, derogatory marks, combined with alternative data you have access to: bank transaction history, income verification documents, employment records, and behavioral signals from your application flow. The model is trained on your historical loan data: applications that were approved, repaid, and defaulted. It learns which combinations of applicant features correlate with repayment behavior in your specific lending segment and product. Output is a numeric score and the contributing factors, so your underwriting team can understand why a score is high or low. For markets where bureau data is thin, we build models that weight alternative data signals more heavily. We assess what data you have in discovery and tell you what accuracy improvement is realistic before we start building.

Real-time fraud detection for payment processing is a classification model that evaluates each transaction against a set of features and outputs a fraud probability score in milliseconds. Features include transaction amount, merchant category, location, device fingerprint, time of day, velocity signals (how many transactions in the last 10 minutes), and behavioral patterns derived from the cardholder's historical activity. The model scores each transaction as it arrives. Transactions above a threshold trigger a hold or decline. Transactions in a middle band may trigger a step-up authentication request. The model is trained on your historical transaction data labeled with fraud outcomes. It learns the specific fraud patterns on your platform rather than applying generic rules. Because fraud patterns evolve, the model is retrained on a schedule as new labeled fraud data accumulates.

Loan origination document extraction takes unstructured documents, bank statements, pay stubs, tax returns, ID documents, and utility bills, and extracts structured data fields from them automatically. The AI reads the document, identifies the relevant fields (account holder name, monthly income, account balance, employer name, employment dates), and outputs structured data into your loan origination system. For bank statements, the model also classifies individual transactions by category, salary credits, rent payments, loan repayments, gambling transactions, which gives underwriters additional signal beyond the headline numbers. The model handles a range of document formats and layouts, including scanned paper documents and photos taken on a mobile phone. It flags documents where confidence is low for manual review rather than silently producing incorrect extractions.

Standard AML transaction monitoring uses rules: alert when a cash deposit exceeds a threshold, alert when transactions occur in high-risk jurisdictions, alert when structuring patterns appear. Rules catch known patterns but generate large volumes of false positives because they cannot account for customer context. AML anomaly detection uses unsupervised and supervised ML models that learn each customer's normal transaction behavior and flag deviations from that baseline. A transaction that is unusual for this specific customer surfaces, even if it does not trigger a rule. Combined with network analysis that maps transaction flows between accounts, the model can surface layering and structuring patterns that are invisible to rule-based systems. Output is a prioritized alert queue with the contributing signals.

The cost depends on what is being built. A single-purpose credit scoring model or fraud detection pipeline typically runs between $40,000 and $120,000 depending on data complexity, integration requirements, and the number of score thresholds and product types we need to support. A multi-model system covering credit scoring, document extraction, and AML costs more. We scope the work in discovery, calculate the cost, and lock the price before development starts. No work begins without your sign-off on the scope and cost.

A single-purpose system, such as a fraud detection model or document extraction pipeline, typically reaches production in 8 to 12 weeks. Multi-model systems covering credit scoring, AML, and customer support may take 16 to 24 weeks depending on integration depth. The timeline starts after discovery. We scope the project in week 1, design and architect in weeks 2 to 3, build and QA from weeks 4 to 12, and deploy to production with 8 weeks of post-launch support included.

Yes. A banking voice AI agent authenticates the caller with a spoken PIN or voice biometric, then handles the interaction end to end: confirming a flagged transaction on a fraud callback, walking through loan pre-qualification questions, or reading a required disclosure and capturing verbal acknowledgment with a timestamped audit record. Compliance requirements, MiFID II, Dodd-Frank, PCI DSS disclosure language, are configured into the dialogue layer before deployment, not left to the agent's discretion.

Yes. We sign NDAs before any data access. Financial data handled during development is treated under the same controls as production data: encryption at rest and in transit, role-based access controls, and audit logging. We have experience building GDPR-compliant systems for European clients and data handling standards consistent with SOC 2 controls. Compliance requirements are scoped in week 1, not retrofitted before launch.