Operational risk quantification and capital

OpCapital Analytics is a set of tools, designed for the modelling of Operational Risk, and economic and regulatory capital calculations for Basel II and Internal Models for Solvency II (see demos and downloads). OpCapital Analytics has obtained a wide industry recognition being nominated, during Operational Risk Award 2016, by RiskNet, as Best Risk Analytics Tool, Best Stress Testing Product and Best Overall Product of the Year.

It can be used for the following purposes:

  • ICAAP operational risk economic capital models and AMA
  • Stress testing approaches such as CCAR, EU–wide stress testing and others
  • Operational loss budgeting and forecasting
  • Forecasting operational losses under different macroeconomic scenarios, including stressed scenarios
  • Internal risk based models
  • Identification of operational loss drivers
  • Operational loss predictive models: determining which organizational entities are more likely to suffer major operational losses

It is currently implemented in banks and insurers in three continents, and has been used to obtain the regulatory approval for operational risk capital in Solvency II and Basel II. Operational risk capital can be calculated using internal and external events, scenario analysis, RCSA, and KRIs.

A list of the main features is the following:

Scenario Analysis
  • Definition of the support information requirements
  • Pre-rating analysis and scenario rating
  • Scenario validation: cross consistency, Bayesian statistics, etc.
  • Scenario modeling into severity and frequency distributions
  • Scenario split into legal entities, business lines, etc.
  • Scenario stress testing
  • Audit trail of origin and all transformations in the scenario down to the simulation report
Incident analysis
  • Multiple incident management modules activated simultaneously
  • Multiple visual representation and analysis
  • Audit trail of all transformations, filters, etc., introduced in data
  • Audit trail of the data origin, filters and transformations which goes down to simulation report Frequency projections
Distribution fitting
  • 24 basic severity and frequency distributions
  • Mixtures of any combination of basic distributions
  • Simulations, fitting and comparison of all fitted distributions
  • User defined distributions via XML
  • Fitting via MLE, Robust Least Squares, Probability Weighted Least Squares, Probability Weighted and Moments approach
  • Distribution split in 3 segments (low losses, medium losses and tail losses)
  • GoF: AD, KS, Kramer von Mises, etc.
  • GoF visual: PP and QQ plots, histogram and CDF
  • Non parametric distributions
Extreme Value Theory
  • DEdH analysis
  • Tail parameter stability analysis by threshold
  • Tail plot
  • Mean Excess Plot
  • Hill estimator
  • HKKP-Hill
  • GoF analysis by threshold
Stability Analysis
  • Capital and Goodness of Fit stability analysis by threshold
  • Distribution parameter stability analysis
  • Re-sampling by bootstrapping and jack knife
Reasonability of losses generated by the fitted distributions
  • Estimation of capital by single loss approximation for all selected distributions
Monte Carlo simulation
  • Copula parameters fitting: Gaussian and t-student
  • OpRisk correlations calculation and stress testing
  • Nested copulas for the aggregation of multiple scenarios, data cells, etc.
  • Automatic stop when stability of results is reached
  • Audit trail report
  • Batch process for multiples consecutive runs with different parameterizations
  • Simulation in multiple currencies for multinational institutions
Insurance modelling
  • Modeling deductibles
  • Modeling maximum coverage
  • Applying deductibles and coverage for total losses or/and individual incidents
Operational loss forecasting and budgeting
  • Univariante analysis for risk factor identification
  • Multivariant analysis: regressive, autoregressive and ARIMAX
  • Loss forecasting module
  • Modelling archive
Backtesting of operational risk
  • P-Values to compare parametric to non-parametric distributions and non-parametric to non-parametric distributions
  • Graphical analysis for all distribution types
Stress testing of operational risk
  • Operational macroeconomic model
  • By the use of scenario analysis
  • Shifting distribution parameters
  • Shifting the fitting weigh to the tail observations
  • Shifting of severity and frequencies of scenarios
  • Stress testing of OpRisk correlations
Reporting in MS Office, PDF and RT
  • All graphics, tables and analytics have the corresponding report: Incident data analysis, Distribution fitting, EVT, etc.
Formal functionalities
  • Audit trail
  • User control
  • Interface with GRC software to import events, scenarios, user rights, etc.
  • Interface via ODBC drive
  • Handling of multiple currencies
  • Parallel computing for simulation
  • One click model replication
  • Modelling journal
  • Modelling archive

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