Solving the Right Problem: A Case Study in Risk Management

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Click to learn more about author Steve Zagoudis.

“Successful problem solving requires finding the right solution to the right problem. We fail more often because we solve the wrong problem than because we get the wrong solution to the right problem.” – Russell L. Ackoff [1]

Managing risk starts with identifying and solving the right problem. A recent client experience bears this out. A bank’s external regulator did not have confidence in the results of 30-year income projections. Even after multiple model runs, something was definitely off.

Companies use sophisticated modeling tools to forecast the value of their assets or liabilities some 20 to 30 years into the future. They are working to determine their future cash flows, including projected interest income or expense. Typically, multiple scenarios are employed using different interest rates along with assigned probabilities. This allows executives to plan accordingly. This modeling consumes vast amounts of raw data, as is the case in a financial institution.

There are three major components of interest rate projection models that can influence the accuracy of the results:

1. Quality and sophistication of the modeling tool
2. Model Risk scenarios and basic assumptions used for the model run
3. Quality and accuracy of the data fed into the model

Our client institution had received a Matter Requiring Attention (MRA) from their regulator, giving them several months to remediate the issue and report back. The client’s Model Risk team was convinced the variations were caused by the modeling tool. They proceeded to utilize a different tool and reran the tests — at the cost of millions of dollars and months of effort. The problem was not with the modeling tool.

Next, the team modified the basic scenarios and assumption sets used to run the model. The results showed vastly different answers, which was expected since they changed the underlying business rules. But alas, both modeling tools showed consistent unacceptable variations. The problem was not with their model assumptions.

We suspected the problem lay in the third component of the model — the quality and accuracy of the underlying data that was fed into the model. My mentor and first employer at Standard Oil was a huge fan of Gane and Sarson Data Flow diagrams (DFDs). Whenever we started a project at Standard Oil, our first task was to draw a Level 0 DFD, putting the system in question in the middle of the page and showing all data flows in and out.

We created this DFD diagram at the start of the project to help us understand the scope of the data feeds into the model. It turned out this institution had multiple copies of their commercial loans scattered across different systems and data warehouses. Market Risk defined each of the model runs using data from different systems, assuming all copies of the data were the same. They were not. Thus, the right problem was identified and rectified. The new results were validated and reported to the regulators.

We subsequently gave the client recommendations for new information governance procedures and technical solutions to mitigate data risk going forward. The goal was to provide new confidence in interest rate forecasts. Not only was the immediate problem solved, but our approach helped to future-proof this critical part of the client’s business. Unfortunately for this company, the same problem of data inconsistency with their financial models surfaced again in their financial reporting and public disclosures, triggering new regulatory and audit issues.

Even in this advanced information age, institutions continue to operate without the full knowledge of their true sources and quality of data, which makes it difficult to know if they are solving the right problem. We developed the Reconciliation Control Framework® for this type of situation. In very simple terms, it answers the key basic question, does A = B = C? Our framework is an example of data triangulation. According to BetterEvaluation:

“Triangulation facilitates validation of data through cross verification from more than two sources. It tests the consistency of findings obtained through different instruments and increases the chance to control, or at least assess, some of the threats or multiple causes influencing our results.”

In the case of our client, the InfoCheckTM control reconciled the loan data across the multiple systems, proving day in and day out that A=B=C was offering a permanent reduction in enterprise risk.

With the works of those like Russell Ackoff, Gane and Sarson, and so many others as guidance, it is possible to identify and solve the right problems.

In the next blog in this series, we will complete the remaining sections of the Data Governance policy.


[1] Ackoff, R. L.: 1974, Redesigning the Future: A Systems Approach to Societal Problems (John Wiley & Sons, New York)

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