One scored view of a bank’s vigilance posture
A public sector bank’s Office of the Chief Vigilance Officer reviews compliance, anomalies, staff discipline, and branch audit against the CVC’s framework, but the evidence sat in five or six systems that were never built to talk. FireAI joined them into one continuously-scored vigilance intelligence layer, with six risk modules that no off-the-shelf banking software reports on.
- 6
- intelligence modules built from scratch
- 8
- analytical command rooms
- 4
- CVC themes in one view
- 5–6 systems
- joined into one risk graph
Trusted by 200+ orgs to boost business insights.
A public sector bank’s Chief Vigilance Officer used FireAI to replace manual cross-referencing of five or six disconnected systems with one continuously-scored vigilance intelligence layer: six purpose-built risk modules, a composite risk score across branches, officers, accounts, and zones, and eight command rooms aligned to the Central Vigilance Commission’s reporting framework.
Vigilance lives in systems that were never built to talk
Vigilance inside a public sector bank sits at the intersection of compliance, law enforcement, HR, and credit risk, and reports directly against the Central Vigilance Commission’s framework. To act on a pattern, the CVO’s office has to see across systems that do not connect: a complaint sits in one system, the officer it implicates sits in HR records, the loan tied to that officer sits in core banking, the sanction that approved it sits in a separate delegation-of-powers register, and the branch where it happened has its own audit history.
Spotting the real signal, an officer repeatedly sanctioning loans that go bad, at a branch with a poor audit rating, linked to a fraud already flagged elsewhere, meant cross-referencing five or six systems by hand. This is why views like “fraud-linked credit accounts” or “employee-linked stressed lending” do not exist in standard banking software: they need a relationship graph across employees, branches, loans, and complaints, scored continuously. That is a data-engineering problem, not a dashboarding one.
- Complaints, HR, core banking, sanctions, and audit history lived in five or six disconnected systems
- Linking an officer to a bad loan, a breached sanction, and a poor branch audit was manual cross-referencing
- Modules like fraud-linked credit accounts and employee-linked stressed lending exist in no off-the-shelf tool
- No continuous risk score across branches, officers, accounts, and zones
- The four standing CVC themes were prepared as separate reports by separate teams
- No single, audit-ready view of compliance against CVC directives
Why standard reporting fell short
The data existed across the bank, but turning it into vigilance intelligence meant work that off-the-shelf software could not do.
Manual cross-referencing by hand
Connecting an officer to a loan, a breached sanction, and a branch’s audit rating meant joining five or six systems manually, every time a pattern needed checking.
No off-the-shelf vigilance modules
Standard banking software has no “fraud-linked credit accounts” or “employee-linked stressed lending” report, because those need a scored relationship graph that does not ship in any product.
Four themes, four separate reports
Compliance outlook, anomaly detection, staff disciplinary action, and branch audit were prepared by separate teams as separate reports, with no shared, scored dataset underneath.
A continuously-scored vigilance layer, built from the schema up
FireAI treated this as a data-engineering and scoring-design exercise, not a templated dashboard build, extending the data model with vigilance-specific fact tables and a composite risk score.
Every tab is a room the CVO walks into. A Master Command Centre gives the bank’s full vigilance posture at a glance; seven case rooms each cover one theme, with their own KPIs and drill-down paths. Each view is wired to an exact source table, join logic, and formula, carries a one-line note on why it matters operationally, and has alert thresholds built in.
New schema for what banking software can’t report
The six requested modules had no tables in the bank’s schema, so FireAI built dedicated fact tables: fraud-linked accounts, wilful defaults, sanction irregularities, employee-linked lending, branch exceptions, stressed accounts, CVC compliance, and preventive vigilance.
A scored relationship graph
Employees, branches, loans, and complaints are joined into one graph and scored continuously, with a composite risk score spanning branches, officers, accounts, and zones, so a pattern surfaces as a number, not a manual hunt.
Eight command rooms, one boardroom view
The Master CVO Command Centre synthesises all four CVC themes into one page, backed by dedicated rooms for complaints and investigations, branch risk and audit, credit and sanction risk, employee integrity, CVC compliance, and preventive vigilance.
Evidence-backed, with alerts wired in
Every KPI ties to a source table, join, and aggregation formula rather than a black box, and thresholds escalate automatically, for example an outstanding exposure above ₹10 Cr on a fraud-linked account routes straight to the CVO.
Why they chose FireAI
- It built the joins and continuous scoring that off-the-shelf vigilance tools do not have, from the schema up
- Every view is evidence-backed, wired to an exact source table, join, and formula, not an opaque score
- The schema, scoring, and module design are bank-agnostic, a reusable blueprint deployable across other banks
From four siloed reports to one scored dataset
A working prototype; reported as the capability delivered, not as financial outcomes.
For the first time, the CVO’s office has a single environment where compliance outlook, anomaly detection, staff disciplinary action, and branch audit are not four separate reports, but four views into the same continuously-scored dataset. A composite Branch Risk Score blends audit ratings, deposit anomalies, NPA exposure, and inspection history into one number per branch, surfaced as a geographic heatmap and a leaderboard of the highest-risk branches, the office’s daily action list. And because the schema and scoring are bank-agnostic, the same blueprint can be redeployed for other banks on their own data.
We are in the process of strengthening our vigilance monitoring framework and would like to request your support in providing a sample dashboard related to CVC guidelines and vigilance reporting.
How the build went
Working prototype, delivered for evaluationFireAI treated the brief as a schema and scoring design exercise rather than a dashboard build, specifying the data model and analytical logic before wiring the views to real loan-level data.
- 1
Vigilance-specific schema first
Because the six modules had no tables in the bank’s schema, FireAI designed dedicated fact tables for fraud-linked accounts, wilful defaults, sanction irregularities, employee-linked lending, branch exceptions, stressed accounts, CVC compliance, and preventive vigilance.
- 2
A composite risk-scoring layer
A unified scoring layer spanning branches, officers, accounts, and zones was built on top of the new schema, turning cross-system patterns into a single comparable risk number.
- 3
Every view wired to its source
Each KPI and chart was tied to an exact source table, join logic, and aggregation formula, with a one-line CVO relevance note and built-in alert thresholds such as the ₹10 Cr fraud-exposure auto-escalation.
- 4
Eight rooms in one prototype
The Master Command Centre and seven theme rooms shipped together as a working prototype built against real loan-level data, for the CVO’s office to evaluate.
Key takeaways
- Vigilance reporting is a join-and-score problem across five or six disconnected systems, not a dashboarding one
- FireAI built new fact tables and a composite risk score for modules off-the-shelf banking software does not have
- The four standing CVC themes became four views into one continuously-scored dataset
- The schema, scoring, and module design are a reusable blueprint deployable across multiple banks
Who should consider FireAI?
Banks and financial institutions whose vigilance, compliance, and risk data is spread across core banking, HR, complaints, sanctions, and audit systems that do not talk to each other.
Frequently asked questions
See your bank’s full vigilance posture in one view
If your vigilance, compliance, and risk data is scattered across core banking, HR, complaints, and audit systems, FireAI can join and continuously score it into one CVC-aligned command centre. Book a demo on your own data.
Want results like Banking & vigilance?
Book a demo