A group-wide framework

MEAG executes an initiative to enhance economic risk measurement

Published on 16-Nov-2012

"With IBM Algo Risk as a core component within our risk framework, we will be able to meet [up-to-date ALM] requirements." - Peter Schenk, Head of Investment Controlling, MEAG


Financial Markets

Deployment country:

Algorithmics Solutions, BA - Business Analytics, BA - Risk Analytics


The asset management business has an enviable history. For many years, the industry as a whole has returned fairly consistent profit margins, while its annual rankings of dominant groups have featured a slate of familiar names from year to year.

Business need:
MEAG MUNICH ERGO AssetManagement GmbH (MEAG) is the asset manager for Munich Re and ERGO, Germany’s second largest insurance group, and works for other external partners. The company elected to replace existing, in-house systems with a scalable, group-wide framework to help measure and manage risk.

MEAG chose IBM® Algo Risk® as an enterprise-level solution to support relative risk calculations, risk capital budgeting, and VaR within the Munich Re Group. IBM Algo Risk supports multiple investment strategies, asset classes, valuation methodologies, risk/portfolio analytics, and scenario generation techniques with real-time access to market and risk information.

Helps to enhance economic risk management across the organization.

Case Study

The asset management business has an enviable history. For many years, the industry as a whole has returned fairly consistent profit margins, while its annual rankings of dominant groups have featured a slate of familiar names from year to year.

However, this landscape may be changing. Today’s asset management market is more dynamic and more competitive than it has been in the past, with market leaders striving to achieve superior performance and to control operating costs within a global framework of complex products and services. New technologies are playing an increasingly important role in the ability of firms to sustain themselves and maintain profitable customer relationships.

As a major asset management player in the European financial sector, Munich-based MEAG is aware of these challenges. With investments in securities, real estate, and funds, MEAG leverages its international competence as the asset manager for the Munich Re Group and the ERGO Insurance Group to encompass all asset classes, regions, and product types.

“We work within a liability-driven framework and manage assets against complicated benchmark portfolios with asymmetric and dynamic risk profiles. Furthermore, we run our group’s market risk model, which keeps on being developed further and further,” says Peter Schenk, Head of Investment Controlling for MEAG.

Having determined that the company’s risk infrastructure was not suited to follow the rapid evolution of methods and instruments in this context, MEAG chose to invest in its framework to enhance economic risk measurement.

Surveying the market
MEAG is the asset manager for the Munich Re Group and the ERGO Insurance Group, and is one of Europe’s leading asset management companies. With offices across Germany and in New York and Hong Kong, MEAG’s global presence provides access to all the world’s capital markets, while its size and independence from banks enables it to acquire the best information from international investment companies for low transaction fees.

As part of its goal to generate better than average risk-return profiles for its investors, MEAG pursues a stringent and liability oriented investment approach. In 2006, the company determined that its existing risk solutions were not sufficient to meet future requirements coming from benchmark portfolios with complex instrument types and dynamic reallocation rules.

In order to enhance economic risk measurement, a market risk controlling initiative was launched. The upgraded solution was intended to support day-to-day risk management tasks, such as risk capital budgeting, and the early warning system, with an emphasis on the liability perspective.

“When we select a vendor for a large-scale project, we put a lot of effort into our selection process and project set-up,” states Peter Schenk. Project leaders follow a well-established route to design specific requirements with clearly stated achievements. For the market risk controlling initiative, the solution had to be accessible across Munich Re´s international businesses, and include support for replicating portfolios.

The system also had to be scalable, so that it could be extended into full valuation. For MEAG, both the depth and flexibility of reporting and analytics functions, and the reputation of the vendor were decisive factors that led the company to partner with IBM for this project.

Building the team
Given the intended range of users across the Munich Re Group, putting together the company’s support team for market risk controlling initiative was an involved task. Representatives from MEAG, Munich Re, and ERGO formed both the project’s steering committee and the project team.

Four to five people were assigned full-time work on the technical part, while six to seven full-time employees were slated to work on the project’s functional side. To ensure that all of the company’s stakeholders were properly represented, a secondary audience of 20 to 30 representatives was regularly consulted.

Peter Schenk observes that, when you have a group-wide project of any kind, it is inevitable that you end up fighting for resources across the group. “While consolidating the group’s implementation needs into one common set of requirements was a challenge, we also understood that following a structured route would pay dividends by eliminating surprises later on in the project.”

Claudio-Peter Prutz, who heads IT and Organizational Development for MEAG, is also pleased with the new solution’s approach: “The implementation of IBM Algo Risk fits perfectly into our strategy of using proven standard software, rather than betting on individual data processing components that are spread across risk controlling and management areas in many organizations.”

The project and next steps
The initial implementation called for a central installation of IBM Algo Risk to support only sensitivity analysis, with full valuation capabilities to be added during a second phase.

A major technical challenge was to port IBM Algo Risk to Red Hat Enterprise Linux on Intel Xeon 64 bit processors, in order to take advantage of the cost and performance benefits associated with the Linux platform. By providing enhanced data management across all asset classes, Linux-based installations enable clients to reduce infrastructure complexity and to leverage their risk investment into savings from improved operational procedures.

In line with its long-term approach, MEAG plans to extend the functionality of IBM Algo Risk from its current sensitivity approach to full valuation over the next year.

Peter Schenk is confident that the partnership with IBM will prove to be the right decision: “Up-to-date ALM requires the simultaneous representation of real asset portfolios and of synthetic portfolios that replicate insurance liabilities and strategic preferences of the investors. We have to continuously know absolute risk figures of these portfolios and mismatch figures between them, and we have to be Solvency II compliant when doing these calculations. With IBM Algo Risk as a core component within our risk framework, we will be able to meet these requirements.”

About IBM Business Analytics
IBM Business Analytics software delivers data-driven insights that help organizations work smarter and outperform their peers. This comprehensive portfolio includes solutions for business intelligence, predictive analytics and decision management, performance management, and risk management.

Business Analytics solutions enable companies to identify and visualize trends and patterns in areas, such as customer analytics, that can have a profound effect on business performance. They can compare scenarios, anticipate potential threats and opportunities, better plan, budget and forecast resources, balance risks against expected returns and work to meet regulatory requirements. By making analytics widely available, organizations can align tactical and strategic decision-making to achieve business goals.

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Products and services used

IBM products and services that were used in this case study.

Algo Risk

Legal Information

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