NBFCs. Six workloads, one endpoint.
Non-banking financial companies & co-lending — the workloads below need more than one data model at once, which is exactly why they stall on a stitched stack.
NBFCs, housing finance and micro-finance institutions with ₹1,000 Cr+ AUM, active co-lending or DLG partnerships, and a field-heavy collections operation.
The forcing function.
NBFCs compete on speed of decision and quality of collection — both data problems before they are model problems. RBI's Digital Lending Guidelines demand end-to-end audit trails on every credit decision, disbursement and collection; DPDP adds consent logging and residency. Co-lending has multiplied the systems that must agree on the same loan. Fragmentation is now a regulatory exposure, not just an engineering inconvenience.
The NPA early-warning graph — stress travels through promoters, guarantors and group companies weeks before the account shows it.
end-to-end auditability required across decision, disbursement and collection
consent logging, localisation and breach notification on the same record
relationship maps across promoters, guarantors and related entities for NPA early warning
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.
Bureau feeds, bank statements, GST filings, device and telco signals in one store. Vector similarity on borrower profiles finds the closest repaid cohort when there is no bureau score to lean on.
Approval-rate expansion into new-to-credit segments without a rise in early delinquency.
Model the loan, the partner, the sourcing LSP and the guarantee arrangement as a graph. Every party's view of the same loan reconciles in one atomic write instead of three overnight files.
Partner settlement disputes fall; co-lent book reporting becomes a query, not a project.
Rank accounts by predicted recoverability using repayment history, contactability and behavioural similarity — then answer “which 40 accounts should this field officer visit today” on the field app.
Higher recovery per field visit; measurable cost-to-collect reduction.
Promoter, guarantor, group-company and common-address links traversed live to surface stressed borrowers weeks before the account itself shows stress.
An earlier intervention window — the highest-leverage variable in credit-cost management.
Slice, stratify and stress the book conversationally — pool selection, cut-off criteria and investor reporting from live loan-level data rather than a stale extract.
Faster pool assembly and investor response; better-priced securitisation.
Key Fact Statements, consent artefacts, disclosure text and grievance records indexed alongside the structured loan record — answerable, with sources, when the regulator asks.
Inspection readiness maintained continuously rather than assembled reactively.
Impact statements are directional targets referenced to published industry research — calibrate against your own baseline.
The stack this replaces in NBFCs.
Five licences, five sync jobs, five security perimeters — collapsed into one atomic store.
Credit, collections and compliance on one loan-level truth — so growth into thin-file segments does not buy an audit problem.