Financial Services
We design and ship AI that fits your risk appetite, regulatory stack, and core banking environment — from FIS and Fiserv to Temenos and Jack Henry — so fraud, compliance, and operations teams get tools they trust, not another pilot that stalls at model validation.
Why delivery is harder than the slide deck
Fraud detection must run at transaction scale with low false positives, yet attack vectors shift constantly — from card-present skimming to CNP fraud, synthetic identities, and authorized push payment scams. In Deloitte research on banking and insurance, 58 percent of banks cited fraud detection among their top generative AI use cases. Yet wiring models to real-time decisioning on ACH, wire, and card rails without tripping Reg E dispute timelines is where most implementations stall.
Regulatory and compliance workload keeps growing. McKinsey survey data on finance organizations found that 65 percent planned to increase generative AI investment in 2025, up sharply compared to prior years. Every new model or workflow needs controls, audit trails, and model risk management aligned with SR 11-7 and OCC guidance — plus BSA/AML programs that satisfy FinCEN SAR filing requirements and CFPB fair lending scrutiny.
Loan files, insurance policies, and legal agreements still mean heavy manual review. Underwriters spend hours pulling data from 1003 applications, W-2s, bank statements, and title docs. Teams need document intelligence that extracts facts reliably, maps to investor overlays and underwriting conditions, and fits existing LOS approval workflows instead of creating shadow processes.
Risk and capital decisions depend on many correlated inputs. Credit scoring, CECL reserve estimates, portfolio concentration views, and DFAST/CCAR stress scenarios get more complex as markets move faster. Analytics must be explainable to risk committees and integrated with the data warehouse and core systems the firm already trusts.
AI applications in financial services
The Hackett Group found that 33 percent of finance organizations were already scaling AI for accounts payable, one of the most mature process areas. McKinsey reported that 44 percent of CFOs were using generative AI for more than five finance use cases in 2025, compared to 7 percent in 2024. We turn that momentum into systems you can run and govern.
Fraud Detection
Real-time transaction scoring across card, ACH, wire, and Zelle/RTP rails with behavioral biometrics and device fingerprinting. Adaptive models retrain on investigator dispositions so false-positive rates drop without manual rule tuning. Outputs map to your case management and SAR filing workflows.
Compliance Automation
KYC onboarding with automated document verification and beneficial ownership resolution. AML transaction monitoring that generates SAR-ready narratives with cited evidence. Ongoing screening against OFAC, PEP, and adverse media sources — integrated with your BSA platform so analysts work exceptions, not data entry.
Document Intelligence
Automated extraction from 1003 applications, tax returns, bank statements, title docs, and insurance declarations pages. Output maps to your LOS conditions checklist and investor overlays. Policy Q&A grounded in your underwriting guidelines with human-in-the-loop escalation for exceptions.
Risk Analytics
Credit decisioning support with explainable feature importance for adverse action notices. Portfolio concentration monitoring, CECL reserve modeling inputs, and DFAST/CCAR stress scenario generation — all producing narratives risk committees and examiners can follow, not black-box scores.
Plan your financial services AI roadmap
We scope use cases against your risk appetite, charter, and existing core and data infrastructure — then deliver production systems with the model risk documentation, validation artifacts, and audit trails your examiners and model risk teams expect under SR 11-7. If you are moving AI from pilot to production, we should talk.
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