30 October 2009

Analytic tools are key to unlocking the benefit of financial institutions’ investment in risk management systems, says Methodware's Tom Bolger.

The elements of a strong enterprise risk management (ERM) program are already in place at most financial institutions. The credit risk function reviews loan portfolios and conducts stress testing. Market, liquidity and financial risk areas monitor interest rate exposures and critical ratios. Operational risk, which continues to evolve, targets fraud, anti-money laundering and information security.

All these groups generate and review large volumes of data, both current and historic. To make this data useful to the operation of the institution, you need analytics. The established risk functions have data models, software engines and quantitative skills to assist this process, but operational risk is still developing clarity around analytics.

The three major roles within an ERM program – all of which play significant parts in operational risk management – may all use the same data elements, but have different yet complementary views on that data. Risk managers take information and look forward, trying to determine the probabilities and impacts associated with future scenarios. Compliance officers look at today to review whether the organisation is doing what it must in order to keep regulators happy. Audit teams look back to find the gaps and control failures that occurred and understand why.

To meet these needs, as well as provide an overall sense of the ERM program’s direction, a strong analytical function requires a robust software application or data model, as well as several key items to ensure you maximise the return on your technology and human resource investment.

Too often, risk management is viewed only as the avoidance of major losses or the mitigation of control failures.

Tom Bolger , Methodware A clear definition of what management needs. Institutions must identify the desired end result of their investments in analytics and reporting, then work backward, rather than selecting a technology platform and deciding that whatever comes from it will be sufficient. Bear in mind a solution that fits a Tier 1 institution may not suit a building society or community bank, so successful organisations implement those solutions that are most appropriate for their specific needs.

Understanding of, and the ability to explain outcomes. Executives must be able to intelligently explain what was analysed, what it means and why. Having resources with advanced quantitative skills is a challenge for many smaller institutions, so a simplified, logical approach may be the appropriate solution for your organisation. Don’t assume that successful analytics are limited to concepts like Value at Risk or methodologies like Monte Carlo simulation – common sense approaches such as analysing the causes of your five largest losses each month can have substantial value.

Data quality and relevance. Based on your needs, look to identify the right data elements. Every institution has a number of core systems generating vast amounts of data – once you have identified the correct elements, ensure that your risk management application is integrated with the transactional system. This creates efficiency, and reduces the chance of errors. Consider reviewing your data feeds periodically in order to verify that they are still relevant for your analytical processes.

Operational risk analytics present institutions with a potential competitive advantage. Too often, risk management is viewed only as the avoidance of major losses or the mitigation of control failures. Organisations are looking for ways to reduce exposures and comply with directives like Basel II and Solvency. These are critical to your success, but also consider the other side of risk management – the opportunities.

When establishing your analytical objectives, include efficiency targets and business process improvement goals. Quantifying the benefit of operational risk management has been a challenge for institutions, as so often it has been linked to potential losses that have been averted. Using risk analytics to realign your control resources or evaluate your back office processes can help you recognise tangible, positive results more quickly, and gain competitive advantages in a market where you need every benefit available.

Regardless of the analytical approach or technology solution your institution has implemented or is considering, remember that they are not substitutes for good governance, smart management or market knowledge – they are tools to help your organisation succeed.

Tom Bolger is the product manager for Methodware, which has developed and distributed risk management and audit software solutions globally since 1993. Methodware solutions use Microsoft SQL technology.

This article first appeared in the Autumn 2009 edition of Finance on Windows.

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