Banking. Six workloads, one endpoint.
Retail, corporate & transaction banking — the workloads below need more than one data model at once, which is exactly why they stall on a stitched stack.
Universal, private-sector and public-sector banks, small finance banks and payments banks with 2M+ customers, a legacy core, and an AI mandate from the board.
The forcing function.
Banking is the largest single AI value pool in the economy — and yet the typical bank runs a core system, a warehouse, a CRM, a fraud engine, a search cluster and a bolted-on vector store, each holding its own copy of the customer. Every AI initiative begins by rebuilding that customer from six places.
The AML entity graph — mule rings and layering are shapes across accounts, devices and beneficiaries that no flat table can represent.
annual GenAI value available to global banking — McKinsey Global Institute
of AI projects unsupported by AI-ready data will be abandoned through 2026 — Gartner
engines consolidated: relational, vector, graph, search and NL on one substrate
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.
Balances, product holdings, service interactions, channel behaviour and household relationships in one queryable view. Semantic similarity finds look-alike customers; graph traversal surfaces the family, employer and business links flat CRM tables lose.
Cross-sell conversion lift; more products per customer without a new CDP licence.
Model accounts, counterparties, devices and fund flows as a live graph rather than a nightly extract. Multi-hop traversal exposes layering, round-tripping and mule rings; vector similarity catches behavioural analogues of known typologies.
Materially fewer false positives per alert investigated — the dominant AML cost line.
Hybrid retrieval over product manuals, circulars, pricing grids and the live customer record, joined in one query. The RM asks in plain language; the answer comes back sourced.
RM productivity gain; faster time-to-proficiency for new relationship staff.
Semantic retrieval over policy documents, FAQs and the customer's own transaction history lets self-service answer account-specific questions — with the entitlement check running in the same transaction.
Lower cost per interaction; deflection that survives account-specific questions.
Every decision, disbursement and override logged to one source of truth, answerable in plain language on demand. No reconciliation across five systems when the regulator asks how a decision was reached.
Audit response in hours rather than weeks; lower regulatory-remediation exposure.
Score payments against the live graph of accounts, beneficiaries and devices, joined with vector similarity over historical fraud patterns — inside the payment window, not after settlement.
Fraud caught pre-settlement rather than recovered post-hoc; direct write-off reduction.
Impact statements are directional targets referenced to published industry research — calibrate against your own baseline.
The stack this replaces in Banking.
Five licences, five sync jobs, five security perimeters — collapsed into one atomic store.
One substrate under retail, risk and compliance — so the bank ships AI products in weeks, and can prove to the regulator exactly how each one decided.