In the previous post, we explained the risk intelligence concept, and some of the general challenges that arise when implementing a risk management system. This post lists some of the important conclusions made by the 2006-2009 Chartis Research surveys (read the Chartis Research white paper), including why existing risk management systems don’t succeed in integrating the entire spectrum of risk applications (such as counterparty credit risk management or liquidity risk management) into one system.
Surveys conducted by Chartis Research between 2006 and 2009 show the average total implementation time for a risk management system to be 14 months with five months to deploy the first usable analytic application. These surveys also revealed that on average 70% of the project effort (time and resources) were spent on data management tasks, for example extract, load and transfer (ETL), data quality and data mapping tasks. These data management activities are often underestimated in terms of complexity and cost which add to overall implementation time. These surveys revealed a number of key lessons:
- Building a risk-data warehouse and positioning generic BI tools on top does not lead to better risk-based decision making. It just provides more technology. FIs should involve the end-users (i.e. decision makers) in the development process to ensure that the technology will deliver timely risk insight (such as counterparty credit risk management data or liquidity risk information) to the right place.
- Data reporting and visualization has been an afterthought in many risk technology projects. Too much focus has been given to the implementation of specific silo applications. The consequence is risk data aggregation challenges throughout the financial services industry.
- Risk technology vendors have consistently failed to deliver their promise of an enterprise-wide risk management system integrating the spectrum of risk applications (i.e. counterparty credit risk management, operational risk management, market risk management, ALM, liquidity risk) onto a single platform that delivers a “single version of the truth” across all business lines, asset classes and entities. Increasingly FIs are seeking component tools to help them link, aggregate and visualize data from disparate risk and trading systems by providingan umbrella layer over all their internal and external risk applications. This component approach provides significantly more flexibility and greater control than the packaged solution approach.
- The traditional static and reactive online analytical processing (OLAP) functionality used by standard BI tools is not sufficient for monitoring risk within FIs’ fast-paced and dynamic environments. Many FIs now view real-time risk intelligence as a mission-critical capability, particularly within the trading and capital markets arena. The need for real-time risk intelligence was highlighted during the financial crisis when it became clear conventional technology architectures could not deliver real-time data aggregation and reporting. A lack of vital and updated risk information such as counterparty credit risk management data, liquidity numbers and risk-based performance metrics for intraday trading made informed decision-making very difficult. This lack resulted in wrong decisions, and in many cases no decisions, being taken.
Real-time risk intelligence may be the goal for many FIs, but to date only a handful have the capability, and it is limited. The situation is changing rapidly as they seek to roll out real-time analytics across their operations such as foreign exchange, interest-rate, counterparty credit risk management, equities and fixed-income trading. These FIs are the vanguard, however, and most are still struggling with the fundamentals of risk management. FIs’ abilities in this area vary greatly and are influenced by their size, location, strategy, risk management experience and approach to IT.
Some FIs have experienced chief technology officers with clear ideas of what’s available on the market and how they can build in-house solutions. Others are more dependent on vendors for ideas and solutions for accurate counterparty credit risk management data, foreign exchange information, operational data, and so on.
Chartis has identified stages of maturity, which serve to characterize any FI seeking to implement risk management solutions. Chartis uses a five-level scale: silos, risk data aggregation, advanced analytics, stress and scenario testing, real-time risk insight.
In the next post (coming soon), we will describe the concept of Silos used by FIs, and some of the main criticisms of this approach.