Industry Insights

A comprehensive vision

Deloitte Consulting principals see opportunities for financial institutions that integrate their risk and financial data.

Deloitte Consulting principals see opportunities for financial institutions that integrate their risk and financial data.

Financial institutions (FIs) are facing a complex set of challenges—worldwide economic reces­sion, increased regulatory scrutiny and extensive competition. As a result, many executives are turning to IT solutions.

The goal is straightforward: timely, accurate information for perfor­mance improvement. To achieve this goal, industry leaders are more completely integrating risk and financial data to provide a deeper, holistic view across products and lines of business (LOBs).

Most FIs do a good job of reporting within product lines and LOBs. However, when crises hit, data isolation can impede access to the infor­mation needed to answer complex questions quickly and accurately.

Many organizations have undertaken business intelligence (BI) ini­tiatives to consolidate data, but they have aggregated it only at a high level. Thus, only intense labor by spreadsheet jockeys can drill deeply enough to answer the in-depth questions needed for analysis.

Spreadsheet-based analysis simply won’t work in the long run. It’s too time-consuming, and the potential for error is too great.

Impressive results

Institutions that automate and improve the integration of financial and risk data are frequently impressed with the benefits.


1. Identify unseen issues

Improved integration data can reveal unrecognized issues and help discern patterns in the financial network. With this knowl­edge, organizations can better understand how market activities or shocks are shifting leverage, and its associated risk, across products and relationships. They must have the flexibility to see shifts coming and stay ahead of the curve to manage risks.

For example, the risk management func­tion typically engages in “what if” scenario analysis. Having integrated data allows banks to project how revenue will change for each scenario analyzed. The ability to determine the “how” of analysis is much more powerful than being able to crunch numbers to determine the “what,” which is the limit of what most institutions can do. Improved data integration enables institutions to analyze information at a more granular level, better understand their risk, take steps to limit their exposure in LOBs or product lines with limited profit­ability, and transfer that exposure to areas with greater potential for increased profitability.


2. Assess risk proactively

The capability—via improved data integration—to more accurately assess risk can also enable FIs to improve their financial and market position and shift from a reactive to a proactive management style to enhance business performance. For instance, the more accurately a bank can assess its market, financial and operational risk, the more accurately it can report this risk to the Federal Reserve Board. With more exact risk reporting, reserve requirements can be more precisely calculated.

FIs need better information—for better decision making and to survive market turbulence and increased governmental scrutiny.

The equation is simple: More accurate reserve estimates mean more capital available for lending. More accurate information, via improved analysis capability, can mean better—and proactive—decision making, and more intelligent use of funds. More intelligent use of funds should equal lower costs. Lower costs plus greater revenues result in increased profitability and more shareholder wealth.


3. Improve stress-testing models

Improved integration of data can also help FIs optimize stress-testing models to better prepare for unforeseen events. It can especially enhance scenario analysis by enabling more accurate, in-depth examinations. In the recent mort­gage crisis, for example, many large institutions obtained holdings in derivatives such as credit default swaps (CDSs) as part of their investment portfolio. At many of these firms, stress-testing models historically looked at whether the CDSs would ultimately pay but didn’t give enough weight to negative cash flows that could occur because of a credit event. Cash flow during a credit event can be different from the payment of the underlying holdings. Thus, having access to the detailed information about the underlying holdings, in combination with the top-level view, provides the foundation for better risk analysis.

For many derivative products, if the market value of the underlying investments drops sub­stantially, firms are required to post collateral. Looking broadly at all mortgages, the default rate for most was 15% to 17% at the height of the collapse. Yet many mortgage-backed securi­ties (MBSs) were selling for as low as 30 cents on the dollar—which meant that even if 60% of the mortgages failed, the MBSs could be good investments long term.

However, because of the collapse, CDSs were suddenly seen as very risky; accordingly, they lost value. Financial firms were forced to mark down the value of their CDSs and post collateral on them. Cash flows were severely affected. Here, market perception was critical to risk analysis, and the models simply didn’t account for those perceptions. Integrated data might have enabled these banks to construct more complex, accurate stress-testing models, allowing them to differentiate between the truly high-risk and the perceived high-risk holdings.


4. Address compliance issues

Finally, a deeper, more holistic view of financial and risk data enables organizations to more effectively manage regulatory compliance across the enterprise. As market regulations change, banks need to better understand their data to shift their risk exposure and leverage to comply with new regulations. Risk models will have to change to keep up with how liquidity is being pulled from the bank.

Consider the relationship between risk and compliance at banks. As a condition to obtain federal funding under the Troubled Asset Relief Program (TARP), the Federal Reserve requires banks to reduce their leverage positions. However, banks need leverage to remain competi­tive in the market. Integrated information can help them construct leverage models that are complex yet nimble enough to keep up with changing regulations and the need for leverage.

Garbage in, garbage out

There’s an old saying in IT: “Garbage in equals garbage out.” It means bad information leads to bad decisions. The inverse is also true: Improved information leads to better decision making.

As FIs navigate an uncertain environment, they need better information—not only for better decision making, but also to survive market turbulence and increased governmental scrutiny. The integration of financial and risk data is one way to gain timely, accurate information to optimize performance and keep moving forward.

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