1. Enterprise-wide risk management & Internal auditing: How can technology help? Rik van de Weerthof Program Manager Risk Management SAS Europe, Middle East and Africa Copyright © 2000, SAS Institute Inc. All rights reserved.

2. Agenda n Determination of topic, goals and challenges n How can technology help?: n success criteria n practical implications and advantages

3. Agenda n Determination of topic, goals and challenges n How can technology help?: n success criteria n practical implications and advantages

4. Topic: Enterprise wide risk management ERM is the process of identifying, measuring and controlling the effect of internal and external factors that (can) negatively affect the value of a company.

5. Goal: Managing the ERM Process risk factors risk factors risk objects risk objects risk mea- sures risk mea- sures risk appe- tite risk appe- tite lossprofit uncertainty beha- vior beha- vior  R 

6. Goal: Managing the ERM Process models risk factors risk factors risk objects risk objects risk mea- sures risk mea- sures risk appe- tite risk appe- tite lossprofit uncertainty beha- vior beha- vior  R 

7. Challenges: The 6 biggest issues n Combining different risk types n Timeliness of figures n Guaranteeing accuracy of figures n Interpreting the analytical results n Running out of (computer) steam n Qualified personnel

8. Combining different risk types Different risk types: credit risk market risk FX risk IR risk legal risk operational risk etc. Different risk factors: counter party default market risk FX rates Interest rates legal structure fraud, human errors ? Different risk measures: Credit VaR Market VaR Total risk < sum of all risk types

9. Timeliness of figures Main Entry: time·ly Pronunciation: 'tIm-lE Function: adjective Inflected Form(s): time·li·er; -est 1 : coming early or at the right time 2 : especially suitable for the time - time·li·ness noun

10. Guaranteeing accuracy of figures VaR = 150 Mln with 95% confidence VaR = 400 Mln with 98% confidence

11. Guaranteeing accuracy of figures 400,000 500,000 600,000 700,000 800,000 900,000 1,000,000 Confidence level VaR 95,0% 99,9%

12. Interpreting the analytical results “ Dear CEO: Our Value at Risk is USD 5,000,000!” “ Dear Auditor: What the !@&*# does that mean for my company?”

13. Running out of (computer) steam n 5,000 trades per day n 100 positions n 100 risk factors n Suppose you want to do a Monte Carlo simulation with 100,000 replications n 100 * 100 * 100,000 = 1,000,000,000 calculations

14. Challenges: the 6 biggest issues n Combining different risk types n Timeliness of figures n Guaranteeing accuracy of figures n Interpreting the analytical results n Running out of (computer) steam n Qualified personnel

15. Agenda n Determination of topic, goals and challenges n How can technology help?: n success criteria n practical implications and advantages

16. How can technology help?: Success criteria n All major analytical techniques, within a single environment n Full parametrisation of analysis attributes n Portfolio-driven data model n Full Repricing n Incorporate new instruments and pricing models n Create and adapt instrument and model attributes n Analyses integrate different risk types n Traceable calculation processes with user intervention n Market modeling n Clear and concise drill-down reporting n Web reports / GUI n Data Management

17. Design specifications All major analytical techniques, within a single environment n No need to jump between specialist packages.. n Any number of different analyses can be performed in a single run.

18. Design specifications Full parametrisation of analysis attributes n Users must be able to customize the specifications of the various parts of the risk analysis process. n Users must be able to mix and match different specifications as needed for different analysis projects.

19. Design specifications Portfolio-driven data model n No fixed data model. n Ability to configure data model from an existing database schema. n Ability to process data “as is”. n Ability to pull data together from numerous heterogeneous data stores.

20. Design specifications Full Repricing n All instruments in the portfolio repriced to determine portfolio value at every market state considered by the analyses performed.

21. Design specifications Incorporate new instruments and pricing models n New instrument types configured easily. n User selects and installs pricing models. n No restrictions on pricing models. n Any number of vendor supplied or user written pricing libraries can be installed.

22. Design specifications Analyses integrate different risk types n Market risk and credit exposure handled within the same framework. n A unified and logical architecture: Consistent use of data definitions, calculation methods, representation of figures, … n (Un)conditional risk measures

23. Design specifications Trackable calculation processes with user intervention n Not a black box. n User can define and control key calculations. n Intermediate calculations can be stored and examined for additional processing or validation checks.

24. Design specifications Market Modeling n Powerful nonlinear statistical modeling features can be used to specify and fit models of market dynamics.

25. Design specifications Clear and concise drill-down reporting n All analytical results can be broken down or aggregated. n You can slice and dice the risk measures for a portfolio across any set of dimensions that you choose to define (by region, counter-party, instrument type, …). n Marginal and Conditional risk measures.

26. Design specifications Web reports / GUI Web Server Java-enabled web browser Risk Server HTTP

27. Design specifications Data Management n A true risk management solution has extensive possibilities for data access and data processing. n Must include extensive back-end data warehousing software.

28. True differentiators... n Openness, extensibility, flexibility n Data management n Clear and concise reports n State of the art risk engine n Market modeling leading to true business advantages

29. Agenda n Determination of topic, goals and challenges n How can technology help?: n success criteria n practical implications and advantages

30. “To manage a business well is to manage its future; and to manage its future is to manage information.” Marion Harper “Managing risk is managing the future.” Rik van de Weerthof + = “Managing risk = Managing information.” Some quotes...

31. n time-oriented data n coming from multiple applications n according to subjects meaningful to the business n driven by the need to inform decision makers Technology helps organising

32. Operational Systems Get data IN Large volume of Simple transactions Static applications Automates Routine Tasks Business Intelligence Systems Get information OUT Small number of Complex queries Dynamic applications Enables Creativity Technology can fulfill an operational or a business intelligence role

33. Product Distribution Credit rating Geographical Time (to maturity) Counter party Type Loans portfolio Option book Equity book Ctpty Ctpty Type All Ctpty >A B-A C-B Junk <1 yr 3-5 yr Office Country Region Enterprise Daughter Enterprise 1-3 yr 5-10 yr >10 yr Technology organises the information dimensions

34. Drill down Technology gives insight in multidimensionality

36. Technology supplies insight required for taking appropriate RR decisions models risk factors risk factors risk objects risk objects risk mea- sures risk mea- sures risk appe- tite risk appe- tite lossprofit uncertainty beha- vior beha- vior  R  The Power to Know ™

37. The Power to Know RISK R I S K D I M E N S I O N S TM