Operational Risk - OREX

The Challenge

The banking and investment management industry needs to understand their operational risk profile to meet regulatory expectations, but this can be challenging given the demanding requirements of maintaining a strong operational risk management framework. This operational risk solution allows organisations to understand and control these risks whilst focusing their energy on what matters most.

This solution implements a scenario-based loss distribution approach (LDA) model to combine loss data and business knowledge using the "as-simple-as-possible" modelling principle, via a secure, easy to use interface. This operational risk solution is ready to be implemented directly into your existing capital quantification and ICAAP/ICARA architecture.

Features

Powerful loss distribution visualisations

Transparent calculation engine

Intuitive model inputs and user interface

Granular period-on-period analysis

Automated sensitivity analysis

Full back-up and audit trail of model run

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How it works

Input risk profile Users are able to quickly and easily upload scenario and correlation data, based on their assessments of firm-specific operational risks. Users also have the ability at this stage to choose tailored model run parameters, from the number of simulations to run to the model’s confidence level.
Run the model This is the time-saver. Based on approaches commonly applied within the industry and aligned with regulatory expectations, our model quantifies operational risk capital while allowing your risk analysts to focus on understanding and managing the underlying risks; where value is really added.
Analyse results The model outputs results for the user to analyse either in the tool via the powerful charting capabilities, or by downloading the information to excel to create bespoke analytics.4Perform supporting analytics
Perform support analytics Quickly understand why operational risk capital has changed from the last period, test the sensitivity of the operational risk capital figure to changes in its inputs, and confirm the correct number of simulations to use, using our powerful supporting analytics functions.