Hugging Face model: hewyler/compound-poisson-lognormal-risk-model
Author: hewyler
Created: 2026-06-13 18:46:11
Last modified: 2026-06-13 19:04:00
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Tags: ai-governance, risk-management, quantitative-risk, monte-carlo, operational-risk, compound-poisson, lognormal-distribution, value-at-risk, cvar, tail-risk, iso-42001, nist-ai-rmf, eu-ai-act, grc, financial-risk, risk-quantification, actuarial, loss-distribution, stress-testing, capital-adequacy, probabilistic-modeling, frequency-severity, risk-engine, responsible-ai, compliance, audit, internal-controls, model-risk, cfo-tools, board-risk, en, license:cc-by-4.0, region:us
Card metadata:
language: en
license: cc-by-4.0
tags: ai-governance, risk-management, quantitative-risk, monte-carlo, operational-risk, compound-poisson, lognormal-distribution, value-at-risk, cvar, tail-risk, iso-42001, nist-ai-rmf, eu-ai-act, grc, financial-risk, risk-quantification, actuarial, loss-distribution, stress-testing, capital-adequacy, probabilistic-modeling, frequency-severity, risk-engine, responsible-ai, compliance, audit, internal-controls, model-risk, cfo-tools, board-risk
task_categories: tabular-regression, tabular-classification, text-classification
size_categories: 100K<n<1M
pretty_name: Compound Poisson–Lognormal Monte Carlo Risk Model: AI and Operational Risk Quantification with ISO 42001 and NIST AI RMF Mapping

dataset_info: description: A fully reproducible compound frequency–severity Monte Carlo simulation engine for quantifying operational and AI risk exposure. The model convolves Poisson-distributed event frequency with lognormal-distributed loss severity across 100,000+ simulations to produce empirical loss distributions, Value at Risk (VaR), Conditional VaR (CVaR), exceedance curves, and capital reserve estimates. Outputs are mapped to ISO/IEC 42001 control themes, NIST AI RMF functions, EU AI Act risk tiers, and Basel III / Solvency II regulatory capital frameworks. Created by Prof. Hernan Huwyler, CIAO MBA CPA, Academic Director at IE Law School Center for Risk and Compliance and Senior Manager at the Capgemini Applied AI Lab.

features: name: simulation_id
dtype: int64, name: frequency_poisson
dtype: int64, name: total_annual_loss
dtype: float64, name: avg_severity_per_event
dtype: float64, name: scenario_label
dtype: string, name: exceedance_probability
dtype: float64, name: above_var95
dtype: int8, name: above_reserve_p75
dtype: int8, name: lognormal_mu
dtype: float64, name: lognormal_sigma
dtype: float64, name: poisson_lambda
dtype: float64, name: confidence_level
dtype: float64, name: severity_lower_bound
dtype: float64, name: severity_upper_bound
dtype: float64, name: var_95
dtype: float64, name: cvar_95
dtype: float64, name: reserve_percentile
dtype: float64, name: loss_at_reserve
dtype: float64, name: random_seed
dtype: int64
splits: name: train
num_examples: 100000, name: test
num_examples: 288
citation: @misc{huwyler2025montecarlo,
  author    = {Huwyler, Hernan},
  title     = {Compound Poisson–Lognormal Monte Carlo Risk Model:
               AI and Operational Risk Quantification},
  year      = {2025},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/datasets/hwyler/compound-poisson-lognormal-risk-model}
}

configs: config_name: default
data_files: split: train
path: data/train.csv, split: test
path: data/test.csv, config_name: percentiles
data_files: split: train
path: data/percentile_table.csv