What is the link between business risks and economic and financial returns? What is the risk exposure of the company? Is our risk management model able to measure and mitigate negative events? Can we capitalize the value of data to create an advanced scenario analysis? Can we use a quantitative approach to optimize the company investment portfolio?
A new approach to complexity
Nowadays, companies are dealing with an environment that, year after year, becomes more and more complex. The ability to predict the scenario volatility plays a key role in resource allocation. Reactive approaches to events, however, are not enough. Today, with the current data capabilities, we can generate quantitative models to anticipate risks and events.
- Affected by personal judgemental bias
- Static
- Descriptive/reactive
- Not linked to performance management
- Does not really consider correlation between risks
- Can improve only if manually modified
- Validated and statistically tested
- Dynamic
- Predictive
- Provide projections on performance and KPIs
- Evaluate correlations
- Can improve with use (machine learning)
Beyond traditional risk management
- Traditional performance management frameworks are based on the use of ‘assumptions‘, estimating the main variables affecting outcomes (demand volumes, raw materials prices, labor costs, external risks, etc.).
- The calculation of assumptions is traditionally based on the use of “static“ approaches (historical analysis, management sensitivity, market forecasts) but almost never is supported by evolved statistical/predictive models.
- “Stress test techniques” are usually set using “stand-alone” variables, without applying quantitative “dynamic” measures able to consider the combined effect of the events (ex. correlations between events, cascading impacts, compensation between impacts, etc.).
Focusing on “risks that matter”
A quantitative approach allows a company to focus efforts and allocate resources to key priorities. The link between business, operational, financial, compliance risks and business objectives highlights the correlation between key performance indicators and major risks.
The identification of key risk factors is based on objective elements, statistically validated and stressed through advanced measurement techniques.
The AFS Dynamic Risk Management (DRM) is based on the use of advanced quantitative modeling techniques, leveraging on:
- The use of statistical techniques to estimate the probability of occurrence and fluctuation of the assumptions;
- The use of predictive statistical models developed by academic researchers; and
- The dynamic correlation between risk and performance management.