Lending. Six workloads, one endpoint.
Six digital-lending AI workloads — credit-memo generation, bank-statement intelligence, synthetic-identity fraud rings and instant decisioning with built-in explainability among them — run on one OriginChainDB deployment across digital origination, BNPL and embedded credit. Each needs more than one data model at once, which is exactly why they stall on a stitched stack.
Digital lending platforms, BNPL providers, lending service providers and embedded-credit teams inside marketplaces, running high-volume, low-ticket, fully automated origination.
The forcing function.
Digital lending is where document intelligence, fraud detection and regulatory explainability collide inside a sub-second decision. A lender that stores the document embedding in one system, the applicant graph in another and the decision log in a third cannot honestly explain a decline — because no single system saw the whole decision.
The synthetic-identity graph — shared devices, addresses and payout accounts expose the ring at application, not at first missed EMI.
rows, embeddings, postings and edges commit together on one substrate
retrieval follows the applicant's relationships; vector similarity alone cannot traverse them
every automated decision reconstructable from one source of truth
Six workloads, five query shapes.
All six workloads need SQL, five need vector search, and two each need graph traversal, full-text and /ask — which is why single-model databases deliver them only with a second system and a sync problem.
Payslips, ITRs, GST returns, KYC documents and bank statements embedded and indexed on ingest, then retrieved semantically to draft the credit memo — with every claim traceable to the source page.
Origination turnaround compressed from days to minutes at constant credit quality.
Parse, classify and aggregate transaction narratives with full-text and vector matching, then compute obligations and surplus in SQL — one engine, one consistency guarantee.
Fewer income-assessment errors; less manual re-verification per file.
Traverse shared devices, addresses, employers, beneficiary accounts and referral chains as a graph; vector similarity flags applications that read like previously confirmed fraud.
Ring-level fraud caught at application rather than at first missed EMI.
Score, decide and log in a single transaction, so the reason codes, the retrieved evidence and the policy version that produced them are never out of sync.
Defensible declines; dramatically reduced grievance-redressal effort.
Continuously re-rate live customers against behavioural cohorts found by embedding similarity, joined with real-time repayment data.
Yield improvement on good cohorts without loosening policy at the margin.
Consent artefacts, purpose declarations and data-sharing events linked as a graph to every downstream use of the borrower's data.
Purpose-limitation provable per record — the hardest DPDP obligation to retrofit.
Impact statements are directional targets referenced to published industry research — calibrate against your own baseline.
The stack this replaces in Lending.
Five licences, five sync jobs, five security perimeters — collapsed into one atomic store.
A decision, its evidence and its audit trail written together — so speed at origination never becomes fragility at inspection.