Regulatory Reform

The emergence of more stringent regulatory requirements like Basel III, Solvency II, Dodd-Frank, and European Market Infrastructure Regulation have propelled financial institutions and market practitioners to rethink their current risk measurement solutions and infrastructure. These new regulations compel risk management initiatives to be addressed as the first priority in many financial institutions' strategic plans where a return to growth will be as challenging as ever. Sophisticated strategies and solutions in compliance with the latest requirements are critical for financial institutions or asset managers to gain a competitive edge. The core service of providing wealth management and managing both customer and in-house portfolios asks for redeployment. Meanwhile the traditional model on which decision makers have long hinged becomes obsolete and susceptible to fallibility.

Accompanied by the recent financial crisis, many investors have become more financially literate and sensitive to financial risk. They tend to direct additional attention to transparency on the mechanics, risk and reward trade-offs of their investments and services. In other words, more than ever before, clarity on investment risks and transparency on the related service is expected from both portfolio and asset managers. Current regulatory evolutions are also reinforcing the trend towards more transparency while the corresponding regulators demand the service providers to make investors well-informed prior to their investment decision. Many wealth managers and private bankers have meticulously taken the latest regulatory infrastructure into account and made an attempt to optimize their models in the development of new products and services so as to drive profitable growth.

Model Risk

Models are simplified representations of real-world relationships among observed characteristics, values and events. Financial institutions rely heavily on models for a wide range of applications like risk management, marketing, valuation, reporting, etc. The level on the sophistication of the models applied differs from relatively simple spreadsheet tools to complex statistical framework directing millions of transactions.  But the application of models invariably presents model risk which involves the possibility of a financial loss, incorrect business decisions, misstatement of external financial disclosures, or damage to the user’s reputation as a result of:

• Potential flaw in the model design and development process;
• Misuse of models, or model results, by model users;
• Use of models whose performance below standard; and
• Fault in the model production process on data inputs and assumptions, or execution.

Regulators have tried to dictate the formalization of model development and execution to the regulated institutions as to their prudent use, procedures to validate their performance and applicable documentation criteria. To mitigate model risk, it is crucially important to conduct thorough analysis by objective parties who are adept at modelling and able to identify the limitations and assumptions and suggest proper enhancements.