Risk Analytics The credit risk decision discipline.

Heuristic, statistical, combined. Calibrated, validated, used.

Every number in Risk Analytics starts here. A grade for the borrower. A probability of default where the data permits.

Three model families do the work. Heuristic, where credit-expert judgment carries the weight. Statistical, where historical data does. Combined, where both meet. The choice depends on the counterpart and the data on hand.

Phase 1 is where the grade is built, calibrated, validated, and used. Not a model-room exercise. The grade the credit committee acts on tomorrow morning.

In this phase

From borrower data to a defensible grade.

Five activities. The output is a grade the credit team trusts, the auditor recognises, and the regulator accepts.

01

Gather the data

Quantitative, qualitative, external. Define the default event.

02

Run univariate analysis

Identify discriminatory indicators. Filter the noise.

03

Build the scoring function

Multivariate. Logistic regression. Weight the blocks.

04

Calibrate to PD

Map scores to default probabilities. Bucket into grades A to E.

05

Validate and document

Quantitative back-testing. Use-test. Annual review.

Try it

Two calculators, two halves of the model.

The PD Scoring Calculator runs the statistical block: indicator inputs through the logistic-regression scoring function to a default probability. The Rating Calculator combines the statistical block with the heuristic and external blocks to produce the final grade. Both are working previews from Course 1 of the Risk Management training program.

Calculator 01 · statistical model

PD Scoring Calculator

Enter financial indicators. The scoring function runs a logit model on calibrated coefficients and returns a default probability with the matched rating bucket. Demonstrates how a statistical model translates raw data into a PD figure that credit committee can act on.

Launch the calculator
Working preview · logistic-regression scoring with calibrated coefficients
Calculator 02 · heuristic model

Rating Calculator

The full heuristic model. Quantitative block (60% weight) feeds from financial ratios. Qualitative block (30%) captures management and sector judgment. External block (10%) carries the sector and macro index. Composite score maps to a final rating grade and an approval routing decision.

Launch the calculator
Working preview · heuristic five-factor model with weighted blocks
The toolkit

Five signature tools.

Phase 1 instruments combine the rigor of statistical model design with the discipline of heuristic rating. The output is a grade the credit team trusts and the regulator accepts.

Tool 01

Combined Rating Methodology

Heuristic block (credit-expert questionnaire) plus statistical block (logistic regression on historical data). Weights calibrated to portfolio segment. Five grades A to E with clear delineation criteria.

View the tool
Tool 02

Indicator Library

Three families: quantitative (financial ratios, cash-flow indicators), qualitative (management quality, sector position), external (macro and sector index). Each indicator carries its discriminatory power and its survey-effort score.

View the tool
Tool 03

Calibration & PD Mapping

Maps composite scores to default probabilities using a logit function on calibrated coefficients. Buckets PD ranges into rating grades. Reviewed annually against realised defaults.

View the tool
Tool 04

Decision Routing Matrix

Routes new applications by grade: Auto Approval, Accelerated Approval, Standard Approval, Alert, Decline. For existing clients, supports limit adjustments based on payment behaviour and rating migration.

View the tool
Tool 05

Validation Pack

AUC and Gini, calibration tests, override pattern analysis. Documentation that satisfies internal audit, external auditors, and Basel-aligned supervisory expectations.

View the tool
From the field

Practitioners on rating discipline.

When the rating became a combined model, our committee stopped arguing about the grade and started discussing the drivers behind it. For the first time the audit trail showed why a borrower was rated as it was, not just the final number.

Head of Credit Risk, regional commercial bank

Across Q-Lana

Related

Two places Rating & Scoring shows up next.

Why this matters

The grade is the gateway.

Every downstream calculation in Risk Analytics depends on the rating. Expected loss multiplies PD by LGD by EAD. RAROC allocates capital against the rating-driven PD. The Risk Appetite Statement sets thresholds in PD-weighted terms. A weak rating model contaminates the entire stack. A strong one disciplines it.

For CROs, Heads of Credit Risk, and rating-model owners

Walk us through your current rating model.

Thirty minutes. We compare your data inputs, scoring function, calibration, and validation against what we have built and recalibrated across more than a hundred projects.

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