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Credit risk analytics

Robust probability-of-default and loss-given-default analytics for informed credit decisions.  

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What we delivers

Segment-specific calibration tailored to financial institutions, corporates, SMEs, retail and project finance.

Transparent analytical workflow: sampling, parameter selection, calibration, coefficient estimation and risk-scale calibration. ​

Recovery analytics that combine collateral, guarantees and structural credit protections with prudent deductions. 

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Core Coverage

Probability of Default (PD)

  • Banks & financial institutions 

  • Corporates, SMEs & MFIs 

  • Brokers, insurers & retail 

  • Greenfield infrastructure 

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Loss Given Default (LGD) 

  • Securities & commodities 

  • Real estate & receivables 

  • Plant, machinery & equipment 

  • Vehicles & guarantees

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ECL Computation

  • Expected Credit Loss (ECL) 

  • Performing exposure 

  • Significant increase in credit risk 

  • Credit-impaired exposure 

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PD Models

From data to calibrated credit-risk scores

Our Probability Default (PD) framework develops differentiated default-risk estimates by borrower type, business profile and financing context. The approach is designed to support underwriting, portfolio monitoring and internal risk governance. 

Analyzing Business Data

Model construction methodology

Commercial Bank model

Financial Institutions (FI) model

Insurance model

Range of models

Broker model

Retail model 

Greenfield Infrastructure model (various segments) 

Large corporate model 

Small Medium Enterprise (SME) model 

Micro Finance Institution (MFI) model 

LGD Models

Estimate net recovery with a disciplined collateral and credit-enhancement lens 

Loss Given Default (LGD) models quantify the likely economic loss following default by estimating recoverable value after collateral realisation, guarantee support, structural protections and applicable deductions. 

Image by Hiroyuki Sen

Asset and support coverage

Asset classes

  • Stocks 

  • Commodities 

  • Real estate 

  • Plant & machinery 

  • Equipment 

  • Vehicles 

  • Receivables

Credit enhancement

  • Guarantees 

  • Structural features 

  • Recovery timing assumptions 

  • Realisation-cost adjustments 

  • Marketability / liquidity haircuts 

  • Documentation considerations 

Image by Masjid MABA

Build a more consistent view

of credit quality, recovery and portfolio risk.

HOW PD & LGD work together

PD estimates the likelihood that a counterparty defaults; LGD estimates the severity of loss if default occurs. Together with exposure at default (EAD), they support expected-loss assessment: 

Expected Loss = PD × LGD × EAD 

Expected Credit Loss (ECL) Computation

The ECL framework tracks changes in credit risk and assigns exposures to the appropriate stage, determining the measurement horizon and carrying-amount treatment. 

Loss Given Default (LGD) models quantify the likely economic loss following default by estimating recoverable value after collateral realisation, guarantee support, structural protections and applicable deductions. 

Image by Jakub Żerdzicki

IFRS 9 Staging Framework

12-month ECL in Stage 1; lifetime ECL in Stages 2 and 3.

Measurement horizon

Expected cash shortfalls are discounted using the effective interest rate.

Discounting

Forward-looking information 

Incorporate reasonable and supportable macroeconomic scenarios and probability weights.

Expected Credit Loss = Probability of Default × Loss Given Default × Exposure at Default 

Key computation mechanics

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