How Flagium
Measures Structural Equity Risk
Flagium AI applies structured forensic analytics to public company filings to detect early signs of financial deterioration — before concerns are broadly recognized.
What the score means
Structural financial stress, measured 0–100
What data is used
Quarterly & annual public filings, XBRL
How often updated
Synced each earnings season
Lookback window
12 quarters (3 years) of filed data
01. Framework Overview
Six Forensic Pillars
The risk score is constructed by evaluating each company across six independent forensic pillars. Each pillar monitors a distinct dimension of financial health. Signals from all pillars are synthesised into a single composite score.
Earnings Quality
Examines whether reported profits are backed by actual cash generation. Flags divergence between operating cash flow and stated net income, which can indicate accounting-driven earnings unsupported by business fundamentals.
Liquidity & Coverage
Assesses the company's ability to service its obligations in the near term. Monitors free cash flow generation and interest coverage to identify companies that are running on borrowed time.
Solvency & Leverage
Evaluates the long-term structural balance between revenue generation and debt accumulation. Identifies companies where debt is growing disproportionately to operational output.
Trend Deterioration
Tracks the rate of change in financial health indicators across multiple reporting periods. A single bad quarter is different from a multi-quarter deterioration sequence. This pillar captures the trajectory, not just the snapshot.
Relative Sector Risk
Benchmarks each company's stress indicators against its sector peers. Stress that is systemic across a sector is treated differently from stress that is company-specific, enabling more precise risk attribution.
Governance Signals
Monitors non-financial indicators that historically precede financial deterioration. Auditor changes, regulatory penalties, and promoter-level actions are tracked as leading indicators of structural risk.
Business Model-Adaptive Frameworks
The forensic engine adapts its signal set to the underlying business model of each company — not just its industry label. A Pharma company with inventory is evaluated differently from a SaaS company with zero inventory, even if both are listed in the same broad sector.
Applied to All Companies
Core Financial Health
- →Cash conversion deficit
- →Free Cash Flow stress
- →Revenue-to-debt divergence
- →Interest coverage ratio
- →Profit collapse
Governance Overlay
- →Promoter integrity signals
- →Auditor changes & material weaknesses
- →Regulatory penalties
- →Board-level compliance
Specialised by Business Model
Banking & NBFC
Banks, NBFCs, HFCs
- →NIM compression
- →GNPA spike
- →Provision coverage decay
- →Cost of funds divergence
- →Capital adequacy (CAR)
Manufacturing & Physical
Auto, Steel, Pharma, FMCG, Capital Goods
- →Inventory stress & bloat
- →Working capital expansion
- →Capex efficiency decay
IT & Asset-Light Services
IT Services, Consulting, SaaS, Platforms
- →EBIT margin compression
- →Revenue concentration risk
- →Relative growth weakness vs. peers
Asset Management
Mutual Fund AMCs, Wealth Managers
- →AUM yield compression
- →Fee margin erosion
- →AUM growth vs. sector peers
02. Data Sources
What We Analyse
Every risk score is derived exclusively from publicly available, officially disclosed financial data. We do not use estimates, analyst consensus, or market-derived signals.
Quarterly Filings
Q1–Q4 financial results published by companies on BSE/NSE regulatory portals.
Annual Reports
Audited full-year consolidated and standalone financial statements.
XBRL Disclosures
Structured financial data submitted in XBRL format to Indian stock exchanges.
Standardized Financials
Normalized balance sheets, P&L, and cash flow statements reformatted for forensic comparison.
Audit & Governance Records
Auditor change filings, regulatory penalty notices, and board-level disclosures.
Sector Intelligence
Aggregated peer-group metrics used to benchmark individual company performance.
Coverage: 2,000+ NSE/BSE-listed companies across Corporate, BFSI, and AMC sectors.
03. Scoring Logic
How Signals Become a Score
Signals are not simply added together. Each signal is evaluated across five dimensions before contributing to the final score. For example, the combination of rising debt, weak OCF, and recurring margin stress can accelerate a score materially compared to isolated events.
Severity
Each signal carries a base impact weight reflecting the structural severity of that financial event. Survival-level risks (solvency failure, capital decay) are weighted more heavily than temporary margin pressures.
Persistence
A signal that recurs across multiple consecutive quarters carries more weight than a one-off event. The engine distinguishes between isolated anomalies and sustained deterioration patterns.
Trend Acceleration
The rate of change matters. A company whose stress is rapidly accelerating quarter-over-quarter is treated as higher risk than one with stable, static stress indicators.
Sector Context
Each signal is benchmarked against sector peers. Company-specific stress is weighted more heavily than sector-wide deterioration, which may reflect macro conditions outside the company's control.
Recency & Time Decay
Not all historical data carries equal weight. Signals from recent reporting periods are given significantly higher analytical weight than older ones. This ensures the risk score reflects the current operating environment, not a company's state from several years ago. As data ages, its contribution to the score decays systematically — preventing stale signals from masking genuine improvements or deteriorations in the present period.
Weighting Philosophy
Signal weights are calibrated against historical corporate distress cycles specific to Indian listed markets — not imported from global models. They are tuned to maximise the correlation between early warning scores and subsequent credit or operational deterioration events. The logic and variables the engine weighs are disclosed here in full; the exact calibration coefficients are not published.
04. Score Interpretation
Reading the Risk Score
Scores are designed for relative comparison across a portfolio — not as absolute thresholds. A score of 65 in an industry with median scores of 70 carries a different meaning than the same score in an industry with a median of 20.
No material stress signals detected. Financial structure appears stable across monitored pillars.
Early or isolated signals present. Warrants monitoring but not immediate concern.
Multiple signals active. Structural stress is building across one or more pillars.
Persistent, multi-pillar deterioration. Requires close scrutiny and position review.
Severe structural breakdown across multiple dimensions. High probability of continued deterioration.
05. Limitations & Disclaimer
What Flagium AI Does Not Do
Understanding the boundaries of any analytical tool is as important as understanding its capabilities.
Not a Buy/Sell Signal
Risk scores indicate structural financial stress — not price direction. A high-risk score does not mean the stock will fall, and a low score is not a buy recommendation.
Based on Public Filings
The engine only analyses what companies disclose in official filings. It cannot detect fraud or misstatements before they are publicly revealed.
Backward-Looking Data
Scores are built from historical reported financials. They reflect what has been reported, not real-time operating conditions.
Not Investment Advice
Flagium AI is a research and monitoring tool. All investment decisions should be made in conjunction with licensed financial advisors.
Flagium AI is a structural financial risk monitoring system. It is not a trading system, price prediction engine, or investment advisory service. All outputs should be interpreted in conjunction with independent research and qualified financial advice.
06. Valuation & Expectation Gap Methodology
How FlagiumAI Estimates Intrinsic Value
FlagiumAI's core insight is the Expectation Gap — the distance between what the market is implicitly demanding from a company and what the business has historically delivered. Every model below produces the same three canonical outputs: implied metric, observed metric, and expectation gap score, regardless of which path is taken.
Fade-Adjusted Reverse-DCF
For companies with positive Free Cash Flow. Solves for the FCF growth rate the market is pricing in, then fades that growth toward a sector-specific terminal rate — reflecting the reality that high growth always normalizes over time.
Implied ROE Model (Justified P/B)
Free Cash Flow is not meaningful for financial institutions — cash is their raw material. Instead, FlagiumAI inverts the Gordon-Growth Justified P/B formula to find the Return on Equity the market is implicitly pricing, comparing it to the company's actual trailing ROE.
Implied Revenue Growth Model
When trailing FCF is negative (high-growth, CapEx-heavy, or pre-profitable companies), the DCF has no anchor. FlagiumAI instead solves for the Revenue CAGR that justifies the current Enterprise Value, assuming margins linearly ramp toward historical levels.
Financial Sector Score Composition (Institution-Aligned)
Aligned with Damodaran / Goldman Sachs / CLSA institutional research: ROE gap is the cause of the P/B multiple, not the effect. Scoring the cause at 40% and the effect at 30% reflects that causal hierarchy correctly.
Advanced Methodology Inquiries
For analysts, advisors, and professional investors seeking deeper framework discussions, please reach out.
Contact Us →05. Anatomy of a Forensic Score
Sector-Specific Risk Calibration Architecture
Corporate risk does not conform to a single universal standard. An inventory build-up that signals distress in a consumer goods company may reflect normal seasonality in an infrastructure contractor. Flagium applies sector-specific capacity limits, dynamic weighting, and customized flag triggers tailored to each industry's balance sheet mechanics.
Kirloskar Oil Engines
Working Capital & Inventory Velocity
Evaluates physical stock accumulation against sales velocity (M1), cash conversion divergence (F1), and gross margin defense.
Ujjivan Small Finance Bank
Asset Quality & Credit Cost Discipline
Monitors Gross NPA accretion (F7), provision coverage starvation (F8), and capital adequacy buffer preservation (F10).
Tata Consultancy Services
Asset-Light Cash Conversion & Utilization
Tracks unbilled revenue accruals against operating cash flow, headcount utilization decay, and debt-free margin defense.
Tata Steel Ltd
Debt/EBITDA & Commodity Cycles
Adjusts leverage capacity ceilings to cyclical EBITDA swings and monitors fixed debt servicing through downcycles (F4, F16).
Divi's Laboratories
Capex WIP & Regulatory Compliance
Scrutinizes CWIP-to-Gross Block ratios, USFDA compliance contingencies, and working capital cycles in global supply chains.
Asian Paints Ltd
Raw Material Inflation Passthrough
Analyzes gross margin resilience against crude derivative price shocks and distributor credit receivables extension.
Explore Empirical Forensic Case Studies
Inspect sequential 20-quarter blind backtests and statutory evidence across India's most significant distress events: DHFL (Insolvency & Fraud), YES Bank (NPA Divergence), and Rajesh Exports (Working Capital Trap).