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You are here: Home » Case Studies » More accurate assessment of operational risk in financial organisations

More accurate assessment of operational risk in financial organisations

Problem

  • New regulations (Basel 2) mean that banks and other financial institutions have to quantify their exposure to operational risk in much the same way as they quantify credit and market risk exposure
  • While there is a wealth of data and well-established statistical methods for calculating credit and market risk, no such data or methods exist for operational risk
  • In particular there are no established methods for dealing with the problem of predicting rare, high consequence operational loss events (such as the Barings collapse)
  • To satisfy the Basel 2 accord the major banks need to develop methods that incorporate the small amount of relevant historical loss data, with more subjective data about processes and controls
  • One of Agena's clients, a major South African Bank, needed to develop an operational risk solution that satisfied Basle 2, with additional contraint that any solution had to integrate with the organisation's existing data and IT structure

Solution

  • Agena developed a class of risk maps in AgenaRisk that are dynamically created from the Bank's existing database of risk and control information
  • The solution quantifies and rates qualitative and numeric risk
  • Integrates self-assessment questionnaires and operational risk models
  • Takes account of dependencies when modelling total losses
  • Models external and internal risks
  • Models dependencies and interrelationships - incidents occur when numerous controls fail
  • Deals with the credibility of information and uncertainty
  • Copes with missing audit / assessment data and accommodates differences in expert opinions

Benefits

  • Provides quantitative predictions even when data is unavailable because expert judgement is built into the models
  • Reduces, manages and mitigates risks, hence leads to reduced costs, better reputation and increased profits
  • Assesses the vulnerability of business lines before they have experienced losses
  • Demonstrates good practice to regulators, shareholders and ratings agencies
  • Aggregates total loss forecasts over business lines, by taking account of risk dependencies, to forecast the capital charge in the form of a value-at-risk (VaR) distribution
  • Provides a rigorous, structured assessment of all risks
  • Focuses management action on the key risks and controls
  • Enables regular risk assessments to be performed and see confidence accrue
  • Forecasts the risk profile into the future
  • Supports "what-if?" and scenario analysis
  • Helps identify areas for business process improvement