Data availability is a key component in deciding which approach can be used for ECL measurement. A lack of sufficient data may result in judgment-based triggers for the Significant Increase in Credit Risk (SICR) criteria and adopting a simplified estimation of Probability of Default (PD), Loss Given Default (LGD), Exposure at Default (EAD) and modelling macroeconomic impact. These subjective models are likely to face challenges and additional overlays by auditors and regulators to account for idiosyncratic and macroeconomic factors. With adequate historic data, statistical modelling techniques can be adopted to identify patterns, forecast default rates, and predict recoveries across economic cycles with reasonable accuracy.
Data granularity helps in the segmentation of the portfolio based on the homogeneous risks of each product/segment. In case granular segmentation is not possible, estimates tend to be either inflated to accommodate the varying risk levels within the portfolio or set aggressively, overlooking the full range of risk factors.
Data completeness improves the predictive capability of the models. When default data is adequate, models are less susceptible to volatility. This provides stability to provision estimates and a greater degree of certainty to capital planning. However, the selection of datasets must be consistent and justified. Choosing data to highlight favourable periods of credit trends or excluding certain periods/segments without an appropriate rationale may raise concerns about bias. Maintaining consistent data and modelling standards is therefore a minimum expectation to ensure the reliability of modelled outputs.
Data integrity and accuracy ensure that modelled outputs are relevant and credible for boards, auditors, rating agencies, regulators, and investors who rely on them for decision-making. Data traceability also supports the validation of modelled outputs and demonstrates strong governance standards. This makes the audit process more efficient, reduces the number of queries from regulators, and lowers the risk of overlays on provisions.
High-quality data enables the creation of Management Information System (MIS) dashboards for timely analysis of portfolio quality and prompting corrective action when required. It also helps auditors and regulators build confidence in the ECL approach, output, and governance. Furthermore, it enhances credibility and reduces the chances of rigorous scrutiny/challenges and potential financial penalties.
