Reproducible Risk-Weighted Asset Calculations

Provides transparent, deterministic and auditable calculations of risk-weighted assets, own-funds requirements, interest-rate risk in the banking book and related capital metrics. It supports canonical in-memory tables and versioned spreadsheet datasets, strict validation, synthetic reference profiles, bitemporal snapshots, calculation controls and traceable regulatory source metadata. Methods are parameterised against the European Parliament and Council (2013) Capital Requirements Regulation < https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32013R0575> and its amending Regulation (EU) 2024/1623 < https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32024R1623>. A granular analyst API exposes individual formulae, domain views, controls, schemas and auditable parameter overrides. The implementation is intended for analytical, educational and model-validation use and does not constitute legal or supervisory advice.

riskweightedassets

riskweightedassets is the R implementation of the RiskDataScience GmbH reference engine for CRR III risk-weighted assets, regulatory capital, IRRBB and ICAAP. It provides a stable R API, canonical Excel contracts, realistic synthetic portfolios, bitemporal snapshots, deterministic lineage and reconciliation controls. No Python runtime is required.

The package is intended for research, education, prototyping and independent model validation. It is not legal, supervisory, accounting or investment advice and is not a certified regulatory reporting system.

Functional scope

credit risk under the Standardised and IRB Approaches, CRM and output floor;
counterparty credit risk, SFT, CCP, securitisation, CVA and crypto exposures;
settlement, large exposures, legacy market risk, parallel FRTB and operational risk;
own funds, buffers, leverage, MREL/TLAC and capital headroom;
IRRBB/CSRBB and ICAAP economic and normative perspectives;
68 canonical input tables in 16 workbooks and 34 structured result tables;
applied and fully-loaded views from the same official snapshot;
two complete synthetic bank profiles and Python 1.0.0 golden-master parity.

Credit supporting factors

The IRB path now applies explicitly eligible SME and infrastructure supporting factors without changing K or expected loss. SA and IRB attestations are independent; factors feed each output-floor path exactly once. Four new public formula functions expose the arithmetic. Historical SA reductions without eligibility evidence remain reproducible but produce an explicit warning.

After installation, see vignette("supporting-factors", package = "riskweightedassets") and the supporting-factor guide. The complete-function-reference vignette covers all 77 exports.

Installation

To test the 1.2.4 update candidate before CRAN publication, install its source package:

install.packages("riskweightedassets_1.2.4.tar.gz", repos = NULL, type = "source")

Install the currently published CRAN version with the following command. It installs 1.2.4 only after CRAN has accepted and published this update:

install.packages("riskweightedassets")

First in-memory calculation

library(riskweightedassets)

tables <- generate_synthetic_tables(bank_profile = "MID_SIZE_UNIVERSAL")
result <- calculate_tables(tables)
print(result)
result$metrics[c("RWEA_KSA", "RWEA_IRB", "TREA", "CET1_RATIO")]

Granular analyst API

The package exposes 77 documented public functions. Analysts can calculate an individual formula, inspect or override a regulatory weight, extract one result table, compare applied and fully-loaded metrics, or obtain a focused risk-domain analysis without using private package internals.

sa_exposure_value(100, 2, committed_undrawn = 40, annex_i_class = "CLASS_2")
irb_capital_requirement(.01, .45, .20, 2.5)

tables <- generate_synthetic_tables(bank_profile = "KSA_BANK")
override <- data.frame(
  parameter_key = "RWA_MULTIPLIER", dimension_1 = "PILLAR1",
  dimension_2 = "", parameter_value = 13
)
scenario_tables <- override_regulatory_parameters(
  tables, override, reason = "Sensitivity analysis", approved_by = "Model Risk"
)
parameter_overrides(scenario_tables)

See the bank-analyst API map for the complete functional inventory and governance rules.

Excel workflow

All writes go to a destination selected by the caller. Package resources are immutable.

root <- file.path(tempdir(), "rwa-runs")
dataset <- generate_synthetic_dataset(
  root,
  as_of = as.Date("2026-08-31"),
  version = "v1.0.0",
  bank_profile = "KSA_BANK"
)
validation <- validate_dataset(dataset)
result <- calculate_dataset(dataset)
result$output_files

calculate_dataset() writes six output workbooks and a machine-readable run manifest below outputs/<deterministic-run-id>/. For integration without spreadsheets, calculate_tables() accepts and returns ordinary R objects.

Synthetic data and external-source boundary

The package contains synthetic data only. It contains neither customer data nor downloaded regulations, standards, PDFs or other third-party publications. regulatory_sources() returns official URLs and archival SHA-256 values; the documents remain outside the distribution.

Documentation

documentation map
complete standalone documentation
generated R function reference manual
public R API
bank-analyst API map
complete installed API vignette: vignette("complete-function-reference", package = "riskweightedassets")
architecture
data model
Pillar 1 and capital methodology
governance, controls and acceptance
reference profiles and testing
regulatory source catalogue
installation and CRAN release

Online documentation describes the GitHub release. For this installed package's exact API, use the bundled help pages and complete-function-reference vignette.

Within R, start with help(package = "riskweightedassets") and the vignettes: