01
Loan-origination document analysis
KYC, bank statements, ITRs, GST returns, property papers — all parsed, cross-checked, and scored against the bank's underwriting policy. RMs review exceptions, not stacks.
Industry · BFSI
Inference at scale,
without the data leaving your jurisdiction.
Financial institutions face a hard constraint: regulators expect data residency and auditability, while public-cloud AI offers neither cleanly. Marxen deploys on-premise AI inside the bank — auditable, residency-compliant, and operationally fast.
§ 01The problem
Residency is not optional. Auditability is not optional.
RBI's cybersecurity framework, IRDAI's data localisation guidance, and the DPDP Act each carry their own version of the same demand: critical customer data stays inside the institution's boundaries, and every action on it is logged. Public-cloud AI fails that test the moment a token leaves the perimeter.
Marxen deploys AI inside the institution's data centre — model serving, retrieval, audit log, and human-in-the-loop UI. The architecture is what makes it compliant; the operations team can read the audit log and prove it.
§ 02Use cases
Where AI earns its place. In bfsi.
Ten concrete workflows where Marxen has deployed — or can deploy — sovereign AI in bfsi institutions.
- 02
Regulatory circular search
RBI master directions, SEBI circulars, IRDAI notifications, FATF advisories — searchable in natural language across years, with the source paragraph cited.
- 03
Anomaly narration on transaction logs
AML and fraud teams get human-readable explanations of clusters and outliers — not raw alerts. Every narration cites the underlying transactions.
- 04
Internal audit assistants
Walk the policy, walk the evidence, surface gaps. Audit teams handle exceptions; AI handles the rest of the documentation.
- 05
Trade-finance document automation
LCs, BoLs, invoices, packing lists, certificates of origin — extracted, cross-referenced against UCP rules, and routed for exception handling.
- 06
Insurance claim document processing
Health, motor, and property claims — claim forms, FIRs, medical records, surveyor reports — assembled, cross-checked, and ranked for adjuster review.
- 07
Branch-staff vernacular knowledge base
Tamil, Hindi, Marathi, Bengali query handling at branches and call centres — grounded in the bank's product manuals and circulars.
- 08
KYC / AML continuous monitoring
Adverse-media checks, sanctions screening, beneficial-ownership tracing — all on the institution's own data, with audit-grade evidence trails.
- 09
Customer-call compliance review
Sales and recovery calls transcribed, scored against the script and regulatory limits, with full retrieval for the QA team.
- 10
Investor research over private models
Equity and credit research on the institution's own analyst notes — never going out to a public API, never leaking a thesis.
§ 03The Marxen approach
Built for the framework. Not retrofitted to it.
DC-resident inference
Models served from the bank's own infrastructure. No external API calls. Network segmentation enforced at the L4 layer.
Full audit trail
Every query, every model, every retrieval logged. Inspector-ready exports from day one.
Sector-tuned models
Fine-tuned on financial English, Indian regulatory language, and code-switched customer dialogue. Not just a Western foundation model with prompt engineering.
Air-gap option
For critical-infrastructure-grade deployments, the entire AI stack can run offline with regulated update channels.
§ 04Compliance
Residency and auditability — by design.
Marxen's BFSI deployments are designed to pass RBI, SEBI, and IRDAI scrutiny. Customer data does not leave the institution. Models do not transmit gradients to external servers. Audit logs are exportable in standard formats.
We work with the bank's CISO and DPO before line one of code is written.
- DPDP Act
- RBI Cybersecurity Framework
- SEBI
- IRDAI localisation
- ISO 27001-ready
Start a bfsi conversation
Tell us your use case. We'll tell you honestly whether sovereign AI is the right move.
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