Finance AI Solutions

Financial services AI deployments operate under two constraints that shape every architectural decision: regulatory compliance requirements (SR 11-7, EU AI Act, MiFID II, AML/KYC obligations) and the cost of errors. A fraud detection model that generates excessive false positives destroys customer relationships; one that misses fraud generates direct financial losses. AI in finance is built to be explainable, auditable, and conservative rather than maximally automated.

Solution Areas

Fraud Detection — Identify fraudulent transactions in real time by scoring each transaction against behavioral baselines, network patterns, and anomaly signals. Production fraud models run at sub-100ms latency and must handle severe class imbalance (fraud rates of 0.1–1% of transactions). Gradient-boosted trees remain competitive with neural networks at the feature engineering that domain experts provide. Human review queues handle borderline scores.

Credit Scoring — Assess creditworthiness using financial history, behavioral data, and alternative data sources. Regulatory requirements (ECOA, GDPR, EU AI Act) mandate explainability: models must support adverse action notices that explain why credit was declined. SHAP values and monotonic constraints on interpretable models are standard approaches. Fair lending compliance requires bias auditing across protected demographic groups.

Anti-Money Laundering — Detect structuring, layering, and integration patterns in transaction flows that suggest money laundering. Graph-based models analyze transaction networks to identify suspicious flows across multiple hops. AI augments rules-based transaction monitoring rather than replacing it; false positive reduction is the primary ROI driver (AML false positive rates of 90–99% are common in legacy systems).

Document Processing — Extract structured data from financial documents: invoices, contracts, statements, regulatory filings. Combines OCR, layout analysis, and LLM-based extraction. Replaces manual data entry in accounts payable, accounts receivable, and onboarding workflows. Accuracy validation gates determine which extractions go straight-through versus human review.

Customer Onboarding — Automate KYC (Know Your Customer) document verification, identity checks, and risk scoring for new account opening. AI processes identity documents, matches against sanctions and PEP lists, and scores risk tier. Reduces onboarding time from days to minutes while maintaining compliance documentation.

Compliance Automation — Monitor communications, transactions, and employee activity for compliance violations. NLP classifies flagged communications; AI systems generate draft regulatory reports from structured data. Reduces compliance team manual review burden while improving coverage.

Portfolio Optimization — Optimize asset allocation across risk/return dimensions subject to regulatory and mandate constraints. Reinforcement learning and mean-variance optimization models run under human oversight; portfolio managers retain final discretion over allocation decisions.

Regulatory Reporting — Automate the extraction, transformation, and validation of data for regulatory submissions (COREP, FINREP, MiFID transaction reporting). Reduces manual preparation time and improves data lineage traceability for audit.