Can Group Finance and Risk Management Get Along?

For the past few years there has been a renewed interest in integrating finance and risk data at capital markets firms for cash optimization and to gain a grasp on transparency into how that cash is at risk.

This isn't surprising, as these firms lost several of their profit lines due to changing regulations and economic impacts. Other drivers of the trend include risk reduction and management of scare resources; margin leakage and optimization of capital and liquidity.

Finance and risk professionals would agree that integration of data is valuable to both areas, yet they may not agree on how or where to tackle the task. For example, when trying to reduce financial reconciliations there are a host of post-trade legacy systems with a number of trade feed source issues for finance and risk groups at investment banks. Risk is outputting varying degrees of data quality from their models and finance often has to normalize this information to use it in various reporting layers.

Many finance and risk groups also have scale and time problems related to trade data, valuations, limits and disclosure issues. And, of course, there is a need to do all of this globally and around the clock. Just consolidating these feeds can be the first point of contention, before they even move onto the more complex aspects of integration that need to be resolved, such as data standards and agreeing on definitions.

The multiple source systems can be managed a number of ways. For instance, should it be handled with logical data models, application consolidation or a data warehouse for example?

More importantly, the risk and finance groups need to find a middle ground on some of these aspects so they can gain traction in the process to unifying data.

Moving Mountains
As we know, there are formal, multi-year and very expensive approaches to data management that will result in a fully integrated company, but these are not where firms are getting the best return, output and use of time. Given the mountain to be moved in order to complete a successful finance and risk integration, companies can start small and build iteratively:

Data Governance: Where there isn't a data governance charter, get the funding to establish one and do it. This doesn't have to be perfect upfront -- it will be a dynamic charter that will change. It should set some basic data strategies and principles for risk and finance groups to chart against along with other units of the business that can leverage this work.

Low-Hanging Fruit: Identify a short list of business activities between risk and finance where costs and inefficiency are high. For instance, trade feeds for financial P&L reconciliations is one common area.

Small Can Be Big: Establish the dollar value associated with these high-cost business activities. Understand that sometimes the problem is so large that an incremental win, say just 2-3%, can wring millions just on in savings in personnel and automation.

Identify the Problem: Organize working groups to identify the business data priorities.

Know Where You Are Going: Roadmap and align business priorities to your data requirements and define some approaches, timetables and responsibilities.

With these initial steps, you will be able to establish successful "sprints" or small starter projects that can move finance, risk and IT to identify and agree on one or more areas to consider.

The next steps, which are infrastructure and application changes, can be made to resolve the risk and finance integration challenges. These steps can help firms avoid stumbling and moving in circles. Capital markets firms are starting to mobilize on the small "sprint" approach. The outcome is an established data strategy foundation to reduce group infighting and quickly evaluate and execute projects that will enable the source systems needed for finance and risk reconciliations.

Companies shouldn't expect group finance and risk folks to start spending weekends together, but they can get their data to mingle for the betterment of both groups.