A computer-implemented
system (100) for forecasting expected credit losses (ECL) in accordance with IFRS 9 using
machine learning and explainable analytics with monitoring audit trails, comprising at least one processor and at least one memory that stores instructions which, when executed, cause the
system (100) to operate: a
data acquisition interface (1) configured to receive portfolio, customer, risk, repayment, collateral and macroeconomic data from one or more internal and external sources (9); a
data management and quality engine (2) configured to perform validation, matching, missing value handling,
anomaly detection and
data origin marking for the received data; a staging and segmentation engine (3) configured to determine the staging and segmentation in accordance with IFRS 9 for risks based on rules and / or learned risk signals; a
feature engineering and feature store module (4) configured to generate, version, and store features that are aligned with observation window and report data; an ECL predictive engine for
machine learning (5) configured to
train and / or execute predictive models to estimate PD, LGD and EAD and to calculate the 12-month ECL and lifetime ECL under forward-looking scenarios; an explainable analysis engine (6) configured to generate local and global explanation outputs that identify factor contributions to at least staging and ECL outputs, and that produces human-readable explanation codes; an
overlay and management customization
workflow module (7) configured to apply post-model overlays with documented justifications and approval controls; and a monitoring audit trail and reporting engine (8) configured to generate a timestamped audit
record that captures input versions, transformation origin, feature versions, model versions,
scenario assumptions, overlays, user actions and report outputs.