Lending. Six workloads, one endpoint.
Digital origination, BNPL & embedded credit — the workloads below need 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
Recall@5 improvement from graph-guided over naïve vector retrieval — multi-hop QA benchmarks
every automated decision reconstructable from one source of truth
Six workloads, five query shapes.
Almost no workload here needs only one data model — which is why single-model databases deliver it 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.