FinTech, Insurance & Surety
AI that reads financial documents like an underwriter.
InWork Global builds AI-powered financial intelligence systems — document processing, risk scoring, underwriting automation, and compliance-ready data pipelines — for US insurance, surety, and fintech companies.
Our expertise
Five requirements generic AI vendors consistently miss.
The surety and insurance industries have specific technology requirements. InWork's InSurety platform addresses all five — and so does every financial AI system we build from scratch.
Structured financial analysis
AI must extract AND interpret financial ratios, not just copy numbers from documents. We compute liquidity, leverage, and bonding capacity — not just OCR the page.
Audit trail requirements
Every underwriting decision must be traceable. AI outputs are documented with input data and model version, on an immutable, timestamped, user-attributed log.
Document variety
Financial packages contain PDFs, scanned images, Excel exports, bank statements, and handwritten notes. Our pipeline handles all of them, with confidence scoring per field.
Human-in-the-loop design
Regulators require human underwriter review on material decisions. AI assists and flags; it does not replace. Final credit decisions remain human-controlled throughout.
Data sensitivity
Financial data requires encryption at rest and in transit, access controls, and retention policies — AES-256 at rest, TLS 1.3 in transit, role-based access throughout.
Platform architecture
From document intake to risk output.
Complete AI underwriting workflow platforms for insurance companies, surety agencies, and specialty finance firms — built in five phases.
Document ingestion — multi-format support (PDF, DOCX, XLSX, JPEG, PNG, TIFF) with an OCR layer (Azure Form Recognizer, AWS Textract, PaddleOCR fallback) and document-type classification.
AI extraction & normalization — field extraction with per-field confidence scoring, currency and date normalization, entity resolution, and missing-field detection and flagging.
Financial analysis — liquidity, leverage, and profitability ratios; working-capital trend analysis; revenue growth; and surety-specific bonding capacity estimation (10x working capital rule).
Risk scoring — a normalized 0–100 score with A/B/C/D tier classification, confidence interval, exception flags on critical ratios, and peer benchmark comparison where data is available.
Output — structured JSON for system integration, a formatted PDF underwriting report, CRM/AMS push (Applied Epic, AMS360, HubSpot), and an exception queue for human review.
Document intelligence
The financial services industry runs on documents.
Applications, statements, tax returns, balance sheets, certificates — all unstructured, all critical. Our production deployments achieve >94% field-level extraction accuracy on clean PDFs and >87% on scanned documents.
Compliance-ready by design
Explainable, non-discriminatory, auditable.
Deploying AI in financial decision-making attracts regulatory scrutiny. We design for it at the architecture level.
Proof in production