KPMG Global Credit Loss Accounting Solution (gCLAS) is a powerful software application designed to help financial institutions cut through the complexity of IFRS 9 compliance by automating the necessary functions for expected credit loss (ECL) modelling, accounting and reporting for the measurement and recognition of asset impairment.Contact us for a demo
KPMG gCLAS offers robust, flexible and transparent expected credit losses (ECL) modelling. It constructs prepayment and default curves using statistical projections and historical data over multiple periods, and considers both default and non-default ending states including active loan migrations.
Transition matrices are used to derive loss and prepayment expectations and the probability of a loan transitioning from one credit state to another. Unlike simpler models that generate multi-year matrices by multiplying a single year transition matrix by itself, KPMG gCLAS uses multiple discrete matrices that can represent different financial conditions to generate projected loss and prepayment forecast.
Transition matrices can be generated using either asset balances or asset counts, depending on the instrument type and its characteristics. They can also be calculated using delinquency status, risk grade, or other user-defined risk metrics (e.g., credit score). You can also load your own custom matrices directly into KPMG gCLAS in lieu of asset level transition history.
KPMG gCLAS generates and discounts contractual cash flows and expected cash flows at the asset level.
Each asset’s characteristics and terms can be automatically uploaded into the system. You can provide an effective interest rate (EIR) or let KPMG gCLAS compute one. Lifetime cash flows are generated and, depending on changes in variable loan interest rate index and prepayment assumptions, KPMG gCLAS will automatically isolate the change in such inputs prior to discounting for credit loss and impairment purposes.
Depending on the asset’s IFRS 9 stage, either 12-month default or lifetime default projections are applied to risk-adjust the asset balance and curtail interest cash flows. The difference of the present valued cash flows represents the expected credit loss. Other critical calculations and risk characteristics are stored in the KPMG gCLAS database, which may be reported as needed.
Managing asset stages involves monitoring credit quality and determining the expected credit loss horizon. KPMG gCLAS simultaneously tracks input risk grades and delinquency data, and based on user defined parameters, assigns financial assets to IFRS stage 1, 2, or 3.
KPMG gCLAS then automatically assigns specific assets to the appropriate stage with management override capability. These features limit human error and help ensure accurate financial reporting while still allowing management to define parameters based on their own judgment or business decisions.
KPMG gCLAS automates accounting functions, including the generation of journal entries and financial statement disclosure reports.
KPMG gCLAS computes the credit loss amount and the effective income at the asset level. Interest and amortisation bookings generated by the core banking system can be automatically reversed through contra accounts, and amounts are tracked from period to period to enable catch-up bookings.
KPMG gCLAS automates disclosure reports such as the allowance for credit loss by IFRS 9 asset category and stage, reconciliation of net carry amount and related credit loss allowances to gross carry amount, and the reconciliation of impaired assets. It can report on changes in expected credit loss by attribute in order to aid management in explaining such changes
Through its transparent and detailed calculations of balance sheet figures, lifetime expected loss calculations and other analytics, it adds value well beyond helping you achieve IFRS 9 compliance by creating opportunities to rethink pricing and business or investment strategies based on a more complete understanding of risk.
KPMG gCLAS bridges traditionally separate functions and acts as a central hub for data required by each discipline. This can dramatically simplify the task of determining the drivers behind reserve or portfolio changes, which previously required risk and accounting experts to synchronise their data and share their ideas.
KPMG gCLAS can help you recognise and analyse the impact of these impending accounting rules today, giving you time to prepare and react, effecting change well before anything hits the P&L.