Use casesBanking and financial services
Governed AI for banking and financial services.
Federis helps banks, insurers and financial institutions deploy AI with policy enforcement, model governance, evidence grounding and audit trails for regulated workflows.
Sensitive AI decisions can be traced across identity, policy, model, evidence and response.
Approved models, data boundaries and runtime paths are enforced before execution.
Designed for private-cloud, on-premises and restricted financial infrastructure.
Generative AI gating for regulated banking
Financial institutions face severe penalties for exposing customer PII, such as PAN or Aadhaar numbers, and must meet strict data sovereignty, digital safety and regulatory audit requirements. Federis places a secure gateway between internal banking applications and generative AI models, so compliance becomes an enforceable runtime behaviour rather than an advisory policy.
MRMF: model risk management across the model lifecycle
Federis includes a model risk management framework (MRMF) control plane built around the RBI's June 2026 draft Guidance on Regulatory Principles for Model Risk Management. A model moves from framework to production only through checks that are enforced in the platform, not tracked in a spreadsheet.
Board-approved framework
The framework settings are approved by the model risk committee with proof of approval attached, verified by an independent admin and then locked. An approved version cannot be edited, and the committee is reminded before its next review.
Risk tiering that does not dilute
One critical factor is never averaged away by low ones, and two high factors escalate a model a level. A tier confirmed below the calculated one is rejected unless an approved exception covers it.
Independent validation
A model's developer or owner cannot be its validator. Acceptance needs a report and executed tests, and validation of a different version does not count toward promotion.
Release admission from real prerequisites
Production admission checks that the validation, approval and attestations actually exist. A high-tier model is approved by the model risk committee, and a failed assurance result blocks it at runtime.
Fairness and hallucination assessment
Text models are assessed with your own datasets, uploaded as CSV or JSON and kept inside your organisation. Rates are computed on judged items, with coverage reported alongside them.
Red-team and drift monitoring
Scheduled attacks run against the model itself, and drift is measured with the population stability index. Too little data is reported as not assessed, never as a pass.
Third-party model oversight
Third-party models enter the oversight queue at every tier. Due diligence passes only when every dimension has accepted evidence from an independent reviewer, with an expiry, and supervisor audit rights are recorded.
Human oversight and kill switch
Answers under human-in-the-loop use cases are held for review. A suspended model is refused on every route, and its in-flight streams are terminated.
Customer outcomes
Chat escalations become durable handoffs to a human handler. AI disclosure is shown on first use, and a grievance can reconstruct the original decision before it is investigated and resolved.
Risk appetite and breaches
Exposure is measured against the published risk appetite. A breach is opened once, routed to the role that must act, and closed when it ends.
Retention and legal hold
Model records carry a ten-year retention floor, and a legal hold blocks purge until it is cleared. The database refuses to delete a retained record.
Verifiable evidence packs
Evidence packs are stored and hash-verified, and tampering is detected on verification. A lifecycle proof can be exported as a signed PDF whose signature verifies offline.
Credit risk assessment and automated underwriting
Credit analysts use AI to synthesise audit reports, transaction histories and corporate filings into underwriting memos. Federis is designed to enforce strict data partitioning, so risk models retrieve only documents within the analyst's authorised account scope.
PII redaction
Pre-inference guardrails detect and mask customer names, account numbers and credit details.
Residency enforcement
The sovereignty engine routes the query to approved regional server lanes to prevent cross-border data leakage.
Data scoping
Dynamic context injection restricts retrieval to documents matching the specific account ID and the analyst's permissions.
Regulatory compliance review (RBI, SEBI and global directives)
Compliance teams match internal guidelines against active regulatory updates. Federis is designed to ground compliance summaries in official documentation, with citation verification and confidence-based rejection when grounding is insufficient.
Evidence grounding
The retrieval fabric maps summaries to verified, original regulatory PDFs and generates inline citations.
Explainable policy
Every policy decision, such as allowing access to a market-trend model, is backed by an OPA audit trace.
Anti-hallucination gating
Answers that suggest actions contradicting active compliance constraints are rejected.
Fraud investigation and suspicious activity summarisation
Security analysts triage fraud alerts by querying device logs, transaction histories and past suspicious activity reports. Federis protects these datasets with a zero-trust mesh, so only authorised analysts can query past case files.
Cryptographic audit seals
Every query, OPA decision and model parameter is logged, hashed and sealed in WORM (write once, read many) storage.
Istio strict mTLS
Service-to-service communication is encrypted and verified continuously across the processing pipeline.
Case-file access control
Policy checks every request for past case files before inference, so access follows the analyst's role.
Deployment fit
- The model risk controls support a regulated entity's own framework. The bank remains responsible for its approved framework, roles, evidence quality, independent validation and regulatory obligations.
- Designed for regulated workflows where evidence, access and audit requirements are non-negotiable.
- Keeps infrastructure integrations replaceable while preserving a consistent governance layer.
Bring governed AI to banking.
Talk to us about your environment. We will walk through the control plane against your workloads, data boundaries and audit requirements.
- Zero data egress
- Any model, any cluster
- WORM audit vault