The model for the calculation of the expected credit loss is based on applying detailed segmentation to the credit portfolio, taking into account the following characteristics at product and customer level:
- type of credit product;
- currency of the product;
- year of granting;
- assessment of risk of the customer’s default;
- the customer’s business segment;
- method of assessing the customer risk.
The Group calculates expected credit losses on an individual and on a portfolio basis.
The individual basis is used in respect of individually significant exposures. The expected credit loss from the exposure is determined as the difference between its gross carrying amount (in the case of an off-balance sheet credit exposure – the value of its balance sheet equivalent) and the present value of the expected future cash flows, established by taking into account the possible scenarios regarding the performance of the contract and the management of credit exposure, weighted by the probability of their realization.
The portfolio method is applied to exposures that are not individually significant and in the event of a failure to identify premises of impairment.
In the portfolio method, the expected loss is calculated as the product of the credit risk parameters: the probability of default (PD), the loss given default (LGD) and the value of the exposure at default (EAD); each of these parameters assumes the form of a vector representing the number of months covering the horizon of estimation of the credit loss.
The Group sets this horizon for retail exposures without a repayment schedule on the basis of behavioral data from historical observations. The loss expected both in the entire duration of the exposure and in a period of 12 months is the sum of expected losses in the individual periods discounted using the effective interest rate. The Group adjusts the parameter specifying the level of exposure at the time of default by the future repayments arising from the schedule and potential overpayments and underpayments to specify the value of the asset at the time of default in a given period.
The calculation of expected credit losses encompasses estimates of future macroeconomic conditions. In terms of portfolio analysis, the impact of macroeconomic scenarios is taken into account in the amount of the individual risk parameters. The methodology for calculating the risk parameters includes the study of the dependencies of these parameters on the macroeconomic conditions based on historical data. Three macroeconomic scenarios based on the Bank’s own forecasts are used for calculating the expected loss – a baseline forecast with a probability of 75% and two alternative scenarios, with a probability of 5%(optimistic) and 20% (pessimistic). The scope of the forecast indicators includes the GDP growth index, the rate of unemployment, the WIBOR 3M rate, the LIBOR CHF 3M rate, the CHF/PLN exchange rate, the property price index and the NBP reference rate. The final expected loss is the weighted average probability of scenarios from expected losses corresponding to individual scenarios. The Group ensures the compliance of the macroeconomic scenarios used for the calculation of the risk parameters with macroeconomic scenarios used for the credit risk budgeting processes. The baseline scenario uses the base macroeconomic forecasts. The forecasts are prepared on the basis of the quantitative models, taking into account adjustments for the presence of one-off events.
The extreme scenarios apply to cases of so-called internal shock, as a result of which the so-called external variables (foreign interest rates) do not change with respect to the baseline scenario. The extreme scenarios are developed on the basis of a statistical and econometric analysis, i.e. they do not reflect the events described, but the forecast path. Two scenarios are identified, optimistic and pessimistic. The share of the scenarios for the GDP path that falls between the optimistic and the pessimistic scenario is referred to as the probability of the baseline scenario. Such an assumption is used to forecast GDP growth, using a potential rate of growth of the Polish economy that varies over time, calculated with the use of quarterly data provided by the Central Statistical Office. The values of other macroeconomic variables used in the scenarios (rate of unemployment, property price index) are estimated after the extreme paths of GDP growth are defined.
The rate of unemployment is calculated on the basis of the quantified dependence on the difference between GDP growth and the potential rate of economic growth. The result is adjusted for significant structural changes taking place in the Polish economy, which are not encompassed by the quantitative model, in particular:
- the ageing of the Polish population (and the appearance of unsatisfied demand for labour, which will limit the scale of increase in the rate of unemployment in a situation in an economic downturn);
- the Polish labour market is nearing full employment (restrictions of supply mean that there is increasingly less space for a further decline in the rate of unemployment);
- the inflow of immigrants (only partly included in the official statistics).
The level of the property price index is set on the basis of changes in GDP, taking into account the conditions of supply and demand on the market based on the data and trends presented by the NBP in the publication “Information on housing prices and the situation on the residential and commercial property market in Poland” and the Group’s own analyses. The forecasts of WIBOR and LIBOR deposit rates are mainly prepared on the basis of assumptions regarding central bank interest rates. The CHF/PLN exchange rate is a cross rate of the EUR/PLN and EUR/CHF exchange rates. Its forecasts are a combination of the forecasts for these two rates. The EUR/PLN and EUR/CHF forecasts are prepared on the basis of a macroeconomic analysis (current and historical) based on econometric methods, as well as on a technical analysis of the financial markets.
Both the process of assessing a material increase in credit risk and the process of calculating the expected loss are conducted monthly at the level of individual exposures. They use a dedicated computing environment that allows for the distribution of the results to the Group’s internal units.
The Group has separated the portfolio of financial assets with low credit risk by classifying financial instruments for which the average long-term default rate does not exceed the probability of default specified by the rating agency for the worst class investment rating. This portfolio includes, in particular, exposures to banks, governments, local government entities and housing cooperatives and communities.