Credit Analysis — Intermediate
Early warning signals: catching deterioration before the 90-day NPA mark
The Overview tab's guidance to monitor signals "well before the
90-day mark" needs concrete, trackable indicators — waiting
for an actual missed payment is reactive, not genuinely
proactive credit monitoring:
Financial signals:
- DSCR trending downward across consecutive quarters, even
while still technically above 1.0
- Increasing reliance on short-term/working capital debt to
fund what should be operational cash needs
- Deteriorating receivables aging (customers taking
increasingly longer to pay — a leading indicator of the
borrower's OWN customers' financial stress, which will
eventually affect the borrower's own cash position)
Behavioral/qualitative signals:
- Delayed or incomplete financial reporting (a borrower
suddenly slower to provide requested financial statements
is itself a signal worth noting, independent of what those
statements eventually show)
- Request for a covenant waiver or loan restructuring before
any actual default has occurred (a proactive request often
signals the borrower already sees trouble coming)
- Key management departures or ownership changes at the
borrower entity
The genuinely practical skill here: distinguishing a single data point (one quarter's lower DSCR, one delayed report) from a genuine TREND worth escalating — a single weak quarter can reflect temporary, explainable factors (a one-off large expense, a seasonal dip), while a consistent multi-quarter downward trend across several of these signals together is a meaningfully stronger signal warranting proactive engagement (a conversation with the borrower, tightened monitoring, or early restructuring discussion) well before any actual payment default occurs — waiting for the 90-day NPA threshold itself means the credit analyst has already missed the entire window where proactive intervention could have improved the outcome for both lender and borrower.
Cash flow quality: distinguishing genuine operating strength from financial engineering
Two businesses can show IDENTICAL operating cash flow figures
while having genuinely different underlying credit quality:
Business A: operating cash flow driven by genuine, sustainable
operations — collecting receivables on normal terms,
maintaining stable inventory levels, no unusual one-time items
Business B: operating cash flow of the same dollar amount, but
achieved partly through STRETCHING payables (delaying payment
to suppliers longer than normal, which boosts reported
operating cash flow in the current period but isn't
sustainable and creates its own future risk — supplier
relationships strained, potential loss of favorable terms)
or through one-time asset sales classified in a way that
inflates the operating section
A credit analyst who accepts a headline operating cash flow number without examining its actual COMPOSITION (genuine collections and normal working capital management, versus payables-stretching or other one-time/non-repeatable items) risks approving credit based on a cash flow quality that won't actually persist into future periods — this is exactly why experienced credit analysts examine the specific line-item drivers behind a cash flow figure, not just the final total, since two identical totals can represent genuinely different underlying sustainability.
Industry and sector risk: the "Conditions" C, examined more deeply
Industry-level risk factors worth systematically checking,
beyond just "how is this specific borrower doing":
- Industry-wide demand trends (structural growth, decline,
or genuine cyclicality — these require different lending
approaches)
- Regulatory changes affecting the specific sector (a policy
shift that could materially affect the industry's economics)
- Competitive dynamics (is the borrower's specific competitive
position within the industry strengthening or weakening,
not just how the industry as a whole is performing)
- Input cost/commodity exposure specific to the sector
(relevant for manufacturing, agriculture-linked businesses
especially)
The reason this matters as a distinct analytical layer, not folded entirely into borrower-specific analysis: even a genuinely well-managed, financially strong individual borrower can face real credit risk from industry-wide headwinds outside their direct control (a structural demand decline in their specific sector, a regulatory change) — assessing industry Conditions separately from the borrower's own individual Character/Capacity/Capital/Collateral is what catches this category of risk, which purely borrower-specific ratio analysis alone would miss entirely, since a borrower's own historical financials don't necessarily reflect a forward-looking industry risk that hasn't yet shown up in past results.
Collateral valuation: the gap between book value and realizable value
Collateral value on a borrower's balance sheet (book value) is
frequently NOT what a lender could actually realize if forced
to liquidate that collateral in a genuine default/recovery
scenario:
Inventory — book value assumes normal-course sale; forced
liquidation typically realizes significantly LESS (distressed
sale pricing, potential obsolescence by the time recovery
actually occurs)
Receivables — book value assumes normal collection; in a
genuine borrower distress scenario, the borrower's OWN
customers may also be affected by whatever caused the
borrower's distress (correlated risk), reducing actual
collectability
Real estate/fixed assets — generally more reliably valued
and more liquid as collateral than inventory/receivables,
though genuinely market-dependent (a real estate downturn
affects even fixed-asset collateral realizability)
This is the practical reason Loan-to-Value ratios for different collateral types are typically set at meaningfully different, conservative levels reflecting each asset type's actual realizable-value uncertainty — a credit analyst treating all collateral types as equally reliable security, valued at full book value, is systematically overestimating the lender's genuine downside protection, which becomes a real, consequential problem specifically in the scenario collateral exists to protect against: an actual borrower default requiring recovery action.
Credit rating migration: what a rating change actually signals
A credit rating downgrade (e.g., from AA to A) isn't just a
label change — it reflects a rating agency's reassessment
that the SAME borrower's default probability has genuinely
increased, based on some combination of the factors covered
throughout this content (deteriorating financials, industry
headwinds, governance concerns)
Practical implication for a credit analyst: monitoring a
borrower's EXTERNAL rating trajectory over time (not just a
point-in-time rating) provides an independent, third-party
cross-check against the analyst's own internal assessment —
a borrower whose internal analysis looks stable but whose
external rating is trending downward warrants specific
investigation into what the rating agency is seeing that
internal analysis might be missing
Rating agencies (CRISIL, ICRA, CARE in India, per the Overview tab) apply broadly similar underlying analytical frameworks to what's covered throughout this content — using their published rating rationale (the specific reasons cited for a rating action) as a genuine input into internal credit analysis, rather than treating external ratings as a disconnected, separate data point, is a practical way to cross-validate and potentially catch analytical blind spots in an internal assessment.
