Risk Analytics The credit risk decision discipline.
EL = PD x LGD x EAD

Three components, one line of math.

Expected Loss is the bridge between rating and decision.

PD comes from Phase 1. LGD captures collateral and recovery. EAD is what the borrower will likely owe at default. Outstanding plus drawdown.

Multiply the three. The number is what to price for, provision against, and steer the portfolio away from.

In this phase

From three inputs to a defensible loss number.

Five activities. Three inputs in. One loss expectation out, per facility, per portfolio, per IFRS 9 stage.

01

Pull PD from rating

One-year PD per grade. Calibrated annually.

02

Estimate LGD

Net of collateral, recovery experience, workout cost.

03

Compute EAD

Outstanding plus credit conversion factor on undrawn.

04

Multiply to EL

EL per facility. Aggregate to portfolio.

05

Stage and provision

IFRS 9 staging. 12-month vs lifetime EL.

Try it

Run the line of math.

Set the PD from a rating grade, the LGD from a collateral class, the EAD from the facility size. The calculator runs the EL line and shows the IFRS 9 stage. Move the rating up or down a notch and watch the loss expectation respond.

Featured demo · live

Q-Lana Expected Loss Calculator

The arithmetic of credit loss made transparent. Enter the rating-driven PD, the collateral-driven LGD, and the facility-driven EAD. The calculator returns expected loss in absolute and percentage terms, identifies the IFRS 9 stage, and frames the figure as either a 12-month or lifetime expectation. Built for credit teams that want the math defensible at audit.

Launch the calculator
The toolkit

Five signature tools.

Phase 2 instruments make the loss arithmetic visible and traceable. Every input has a source, every output has a downstream use.

Tool 01

PD Term Structure

One-year and lifetime PD curves per rating grade. Calibrated annually against realised defaults. Source for the PD input in the EL calculation and for IFRS 9 staging logic.

View the tool
Tool 02

LGD Reference Library

LGD estimates by collateral class, sector, and recovery experience. Adjusted for workout cost and time-to-recovery. Updated as new recovery cases close.

View the tool
Tool 03

EAD & CCF Engine

Exposure at default including undrawn commitments at the appropriate Credit Conversion Factor. Handles revolving, term, and contingent facilities consistently.

View the tool
Tool 04

Expected Loss Calculator

The arithmetic engine. EL per facility, EL per portfolio cut, sensitivity to PD shifts. Output feeds Phase 3 pricing, provisioning, and capital allocation.

View the tool
Tool 05

IFRS 9 Staging Logic

Stage 1 (12-month EL), Stage 2 (lifetime EL on significant increase in credit risk), Stage 3 (lifetime EL on impaired). Documented triggers, audit-ready trail.

View the tool
The principle

Q-Lana on Expected Loss.

Expected loss is what makes pricing honest. When PD, LGD, and EAD reach the deal table instead of a model room, the relationship manager, credit, and pricing all reason from the same number. Margin stops being an opinion.

Christian Ruehmer, Co-Founder, Q-Lana

Across Q-Lana

Related

Two places Expected Loss shows up next.

Why this matters

Loss before it happens.

Expected Loss is what makes risk pricing honest. Without it, every facility is priced to a margin without reference to its underlying risk. With it, the institution prices to risk, provisions to risk, and allocates capital to risk. The discipline that one line of math imposes is the difference between an ad-hoc credit book and one that scales without surprises.

For CROs, Heads of Credit Risk, and provisioning teams

Walk us through how Expected Loss reaches the deal table.

Thirty minutes. We compare your PD, LGD, and EAD wiring against what we have built and recalibrated across more than a hundred portfolios.

Q-Lana Weekly

One newsletter. Five lenses. In your inbox every week.

Curated news with practitioner commentary. One Deep Dive in rotation. One applied tool. Read in fifteen minutes. Used the same week.

Q-Lana. The Operating System for SME and Corporate Finance.
Information becomes intelligence. Intelligence becomes advantage.