Builds points scorecards for binary targets (credit risk, fraud, propensity) on the optimal binning and weight of evidence engine of 'OptimalBinningWoE', and takes them to the risk parameters of the internal ratings-based (IRB) approach. Variables are selected through optimal binning, eight admission rules, hold-out revalidation with frozen bins and a consensus of 'glmnet', 'xgboost', 'lightgbm' and 'ranger' models weighted by out-of-sample performance; the audit funnel never drops a candidate from the report. The scorecard is fitted with an explicit, auditable scale alignment (a log-odds regression on the raw score composed with the points-to-double-the-odds map); cut-offs are swept with frozen cuts; reject inference is reported as a sensitivity band; the population and characteristic stability indices (PSI and CSI) are monitored with both the fixed and the sample-size-adjusted threshold; and production SQL is generated in fourteen dialects, with the agreement between R and SQL verified by test. The IRB layer builds the default flag; calibrates the scorecard to a long-run default rate with rating grades, margins of conservatism and floors to give the probability of default (PD); models workout loss given default (LGD) in two stages with downturn and in-default estimates; models credit conversion factors from facility snapshots to give the exposure at default (EAD); and computes expected loss, risk weights, regulatory capital and expected credit loss from parameter tables selected by framework preset. The heavy numeric kernels (rank correlation of wide weight of evidence tables, exact concordance counts for Somers' D, streamed expected credit loss paths) are compiled with 'RcppArmadillo'. The scorecard methodology follows Siddiqi (2017) <doi:10.1002/9781119282396> and Thomas et al. (2017) <doi:10.1137/1.9781611974560>.