Cevrynt transforms applications, bank statements, identity data, debt obligations, and risk signals into structured underwriting intelligence—ready for faster, evidence-backed human review.
One platform. Every signal.
One review.
Analyse deposits, revenue patterns, balances, negative days, NSF activity, ACH transactions, and recurring obligations.
Identify potential existing advances, recurring lender debits, repayment obligations, debt exposure, and possible stacking indicators.
Classify applications, bank statements, identification records, contracts, debt documents, and supporting files automatically.
Surface document inconsistencies, unusual transaction activity, identity mismatches, missing information, and other review flags.
Core document, analysis, verification, and reporting workflows developed.
Available for product testing and underwriting-workflow validation.
Preparing for lender feedback, benchmarking, workflow configuration, and integration testing.
Complex deal files create slow decisions.
When documents, banking activity, obligations, and verification live in separate systems, underwriters spend more time reconnecting facts than evaluating the deal.
Applications, banking data, debt records, and verification results often remain separated across multiple systems.
Underwriters spend significant time identifying deposits, cash-flow patterns, negative days, NSF activity, and recurring obligations.
Existing MCA positions, lender debits, recurring repayments, and potential stacking indicators can be difficult to identify consistently.
Intelligence underwriters can inspect and explain.
Trace each recommendation back to its source documents, transaction activity, verification results, and the review decisions that shaped it.
Every document, signal, and recommendation stays connected to its source, so underwriters can inspect the complete deal before making a decision.
From raw documents to a reviewable decision.
Follow each deal from source documents and structured extraction through analysis, verification, recommendation, and human review.
Securely receive applications, bank statements, identity records, contracts, and supporting documents.
Convert financial and business information into normalized, structured data with source references.
Evaluate revenue, deposits, balances, ACH activity, NSF events, obligations, and transaction behaviour.
Review business, identity, ownership, address, KYB, KYC, and fraud-related signals.
Generate an evidence-linked underwriting recommendation for human review.
Alternative lending needs
specialised infrastructure.
- 01Document complexityGrowing→
- 02Transaction complexityIncreasing→
- 03Manual review pressurePersistent→
- 04Verification sourcesFragmented→
- 05Debt exposureCritical→
- 06Fraud signalsExpanding→
- 07Explainable outputsNeeded→
- 08API-first workflowsEmerging→
Built for review. Ready to validate.
Cevrynt is preparing for private pilot deployment and raising a $250K pre-seed round. Investment information is provided for preliminary discussion purposes and does not constitute an offer to sell securities.
Bring every deal into one
underwriting workspace.
See how Cevrynt can help your team transform fragmented deal files into structured, evidence-linked, and human-reviewable underwriting intelligence.