KENANGA INVESTMENT BANK BERHAD INTEGRATED ANNUAL REPORT 2024 WE ARE KENANGA OUR SUSTAINABILITY APPROACH LEADERSHIP STATEMENT HOW WE ARE GOVERNED SHAREHOLDERS’ INFORMATION NOTES TO THE FINANCIAL STATEMENTS 31 DECEMBER 2024 NOTES TO THE FINANCIAL STATEMENTS 31 DECEMBER 2024 FINANCIAL STATEMENTS ADDITIONAL INFORMATION OUR VALUE CREATION APPROACH 293 292 51. FINANCIAL RISK MANAGEMENT (CONT’D.) (a) Credit risk (cont’d.) Impairment assessment (cont’d.) Grouping financial assets measured on a collective basis (cont’d.) The Group and the Bank classify these exposures into smaller homogeneous portfolios, based on a combination of internal and external characteristics of the financial assets, as described below: For debt instruments these are: • Internal grade • Exposure value For loan, advances and financing these are: • Product type (corporate loan and share margin) • Internal credit rating • Exposure value • Collateral type • Borrower's industry For balance due from clients and broker and other receivables these are: • Exposure value • Collateral type Forward-looking and probability-weighted To determine unbiased probability-weighted amount of ECL which considers range of possible outcomes and use of information about economic conditions, the Group and the Bank use external and internal information to generate a ‘base case’ scenario of future forecast of relevant economic variables along with a representative range of other possible forecast scenarios. The external information used includes economic data and forecasts published by governmental bodies and monetary authorities. The Group and the Bank apply probabilities to the forecast scenarios. The Group and the Bank have identified and documented key drivers of credit risk and credit losses for each portfolio of financial instruments and, using a statistical analysis of historical data to estimate the relationships between macro-economic variables and credit risk and credit losses. These are being reviewed and monitored for appropriateness on a quarterly basis. Linear Regression Analysis Linear Regression analysis is used to determine which macro-economic variables are predictive of the portfolio PD over time and how to weigh the macro-economic variables against each other in the model. A variety of variable selection methods i.e. univariate and multivariate factor selections are used in the Linear Regression process to eliminate non-predictive macroeconomic variables. Multiple-scenario Analysis The Group and the Bank generate a ‘base case’ scenario of the future direction of relevant economic variables as well as a representative range of other possible forecast scenarios. The Group and the Bank then use these forecasts, which are probability- weighted, to adjust their estimates of PDs. 51. FINANCIAL RISK MANAGEMENT (CONT’D.) (a) Credit risk (cont’d.) Impairment assessment (cont’d.) Forward-looking and probability-weighted (cont’d.) Multiple-scenario Analysis (cont’d.) The scenarios by state of economy namely, "Booming", "Normal" and "Downturn" were used as defined in below table: State of Economy GDP Growth Rate (annual) Imports (Billions in USD) Downturn (D) <3.5% <200 Normal (N) 3.5%-5.0% 200-250 Booming (B) >5.0% >250 The assumptions used for the ECL estimates as at 31 December 2024 are set out below: Economic Factor Scenario 2024 2025 2026 GDP Growth Rate 1 N B N 2 N N N 3 N N D Imports (Billions in USD) 1 N B B 2 B B B 3 B B B The assumptions used for the ECL estimates as at 31 December 2023 are set out below: Economic Factor Scenario 2023 2024 2025 GDP Growth Rate 1 B B B 2 N N N 3 D D D KLCI Index 1 B B B 2 B B B 3 N B B The weightings assigned to each state of economy as at 31 December 2024 were as follows: All portfolios State of Economy Weighting B 10% N 60% D 30% The weightings assigned to each state of economy as at 31 December 2023 were as follows: All portfolios State of Economy Weighting B 10% N 60% D 30%
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