Timeliness of Risk reporting
Once a report is built in Spotfire, opening it is all that is required to update it with the most recent information. Spotfire reconnects to all used data sources and applies precisely the same calculation steps, so users can see yesterday’s report with now data with the click of a button
Inbuild sensitivity analysis and predictive analytics
Be it what-if analysis, Monte Carlo simulations to be run on a grid computing environment or advanced statistical models, our technology solution not only lets you see today’s valuations but also what their value would be if their guiding parameters, e.g. risk free interest rate, were different. It allows calling calculations in special business-user enabled Expression Language, TERR, open source R, SAS, Matlab, KNIME, Python, C++ via GridServer, H2O or Spark. These calculations can be as simple or as complex as you require them to be. For example, it can be your credit manager inputting new market information about the specific credit rating of a company to see the impact that has on portfolio valuation. Or it can be your CEO sliding a bar to input his/her beliefs regarding the macro-environment: this value be passed into the valuation of all assets, recalculating their value differently per each asset category, and bringing the final result back to Spotfire for your CEO’s appreciation. Check out this simple example of using Spotfire to measure operational risk
What-if analysis: how does a change in the scoring of a holding affect the portfolio risk exposure
Monte Carlo simulation using our technology to simulate a scenario-based view of Economic Capital needs. resulting from changes in macro-economic environment. The user specifies parameters that determine the joint distribution of these two risk factors, and their contribution to Total Losses. It displays consequent estimated loss distribution of a dependent asset and respective economic capital needs (blue area of the bar chart).
Loss Distribution Approach to Operational Risk & Analysis
This analysis implements simple frequency-severity model for operational risk event-types. This forms the basis of the Loss Distribution Approach alternative in the Basel Regulations.
Financial institutions incur various operational risk events.
For example, the Basel II regulations identify the following event types: Internal Fraud; External Fraud; Employment Practices and Workplace Safety; Clients, Products, & Business Practice; Damage to Physical Assets; Business Disruption & Systems Failures; Execution, Delivery, & Process Management. The firm will use a combination of historical internal data, and relevant industry data to construct distributions of both frequency and severity of different types of operational risk events in its different lines of business.
Using these distributions, the firm can simulate the losses in each cell of the event type / line of business matrix in a given risk horizon, such as one year. Driven by either regulatory or strategic reasons, the firm will demonstrate reserves up to the specified percentile of loss in its annual loss distribution. This amount is called Value-at-Risk for the given risk horizon (“annual”) and percentile rank (“99th”).
Statistical approach to operational risk
There are three main approaches to handle this type of risk, according to Basel III:
- Basic Indicator Approach (BIA), which is legally defined to depend on the dimension of the bank
- Standardised Approach (TSA), which splits risk by business line and scales it by a predefined factor
- Statistical model (AMA).
Taking the statistical approach to operational risk management has the potential to significally reduce capital requirements. Yet it can be computationally expensive. Specifically, it involves tight control over the following three dimensions:
- Data Management: get different input from different data sources.
- Core Component: functions and packages. Maintaining the code as easy to understand within the organization.
- Results Management: disseminating results across the business.
Comply efficiently with ever changing industry regulations. From BCBS’s to Dodd-Frank, from MiFID to Basell II and IFRS, a common backdrop to the extensive regulations imposed on financial institutions is that they possess adequately detailed information that ensures: consumer protection, transparency, and adequate assessment of financial risks, probability of losses and investment portfolio valuation. Upon this, our technology solution can help in several ways:
Our technology puts in the hands of business users the ability to combine any number of disparate data sources and wrangle it to find desired outputs. Spotfire keeps track of all calculation steps, such they are always traceable and, more importantly, repeatable. Being a very visual environment, results can include not just required data but metrics of data quality.
Below example of the source view diagram of table. Every transformation step is dynamically kept track of and will be reapplied upon data refresh.