Model risk managers invest considerable time in determining which spreadsheets qualify as models, which are end-user computing (EUC) applications, and which are neither. Seldom, however, do model risk managers consider the question of whether a spreadsheet is the appropriate tool for the task at hand.

Perhaps they should start.

Buried in the middle of page seven of the joint Federal Reserve/OCC supervisory guidance on model risk management is this frequently overlooked principle:

“Sound model risk management depends on substantial investment in supporting systems to ensure data and reporting integrity, together with controls and testing to ensure proper implementation of models, effective systems integration, and appropriate use.”

It brings to mind a fairly obvious question: What good is a “substantial investment” in data integrity surrounding the modeling process when the modeling itself is carried out in Excel? Spreadsheets are useful tools, to be sure, but they meet virtually none of the development standards to which traditional production systems are held. What percentage of “spreadsheet models” are subjected to the rigors of the software development life cycle (SDLC) before being put into use?


Model Validation vs. SDLC

More often than not, and usually without realizing it, banks use model validation as a substitute for SDLC when it comes to spreadsheet models. The main problem with this approach is that SDLC and model validation are complementary processes and are not designed to stand in for one another. SDLC is a primarily forward-looking process to ensure applications are implemented properly. Model validation is primarily backward looking and seeks to determine whether existing applications are working as they should.

SDLC includes robust planning, design, and implementation—developing business and technical requirements and then developing or selecting the right tool for the job. Model validation may perform a few cursory tests designed to determine whether some semblance of a selection process has taken place, but model validation is not designed to replicate (or actually perform) the selection process.

This presents a problem because spreadsheet models are seldom if ever built with SDLC principles in mind. Rather, they are more likely to evolve organically as analysts seek increasingly innovative ways of automating business tasks. A spreadsheet may begin as a simple calculator, but as analysts become more sophisticated, they gradually introduce increasingly complex functionality and coding into their spreadsheet. And then one day, the spreadsheet gets picked up by an operational risk discovery tool and the analyst suddenly becomes a model owner. Not every spreadsheet model evolves in such an unstructured way, of course, but more than a few do. And even spreadsheet-based applications that are designed to be models from the outset are seldom created according to a disciplined SDLC process.

I am confident that this is the primary reason spreadsheet models are often so poorly documented. They simply weren’t designed to be models. They weren’t really designed at all. A lot of intelligent, critical thought may have gone into their code and formulas, but little if any thought was likely given to the question of whether a spreadsheet is the best tool for what the spreadsheet has evolved to be able to do.

Challenging the Spreadsheets Themselves

Outside of banking, a growing number of firms are becoming wary of spreadsheets and attempting to move away from them. A Wall Street Journal article last week cited CFOs from companies as diverse as P.F. Chang’s China Bistro Inc., ABM Industries, and Wintrust Financial Corp. seeking to “reduce how much their finance teams use Excel for financial planning, analysis and reporting.”

Many of the reasons spreadsheets are falling out of favor have little to do with governance and risk management. But one core reason will resonate with anyone who has ever attempted to validate a spreadsheet model. Quoting from the article: “Errors can bloom because data in Excel is separated from other systems and isn’t automatically updated.”

It is precisely this “separation” of spreadsheet data from its sources that is so problematic for model validators. Even if a validator can determine that the input data in the spreadsheet is consistent with the source data at the time of validation, it is difficult to ascertain whether tomorrow’s input data will be. Even spreadsheets that pull input data in via dynamic linking or automated feeds can be problematic because the code governing the links and feeds can so easily become broken or corrupted.

An Expanded Way of Thinking About “Conceptual Soundness”

Typically, when model validators speak of evaluating conceptual soundness, they are referring to the model’s underlying theory, how its variables were selected, the reasonableness of its inputs and assumptions, and how well everything is documented. In diving into these details, it is easy to overlook the supervisory guidance’s opening sentence in the Evaluation of Conceptual Soundness section: “This element involves assessing the quality of the model design and construction.”

How often, in assessing a spreadsheet model’s design and construction, do validators ask, “Is Excel even the right application for this?” Not very often, I suspect. When an analyst is assigned to validate a model, the medium is simply a given. In a perfect world, model validators would be empowered to issue a finding along the lines of, “Excel is not an appropriate tool for a high-risk production model of this scope and importance.” Practically speaking, however, few departments will be willing to upend the way they work and analyze data in response to a model validation finding. (In the WSJ article, it took CFOs to affect that kind of change.)

Absent the ability to nudge model owners away from spreadsheets entirely, model validators would do well to incorporate certain additional “best practices” checks into their validation procedures when the model in question is a spreadsheet. These might include the following:

  • Incorporation of a cover sheet on the first tab of the workbook that includes the model’s name, the model’s version, a brief description of what the model does, and a table of contents defining and describing the purpose of each tab
  • Application of a consistent color key so that inputs, assumptions, macros, and formulas can be easily identified
  • Grouping of inputs by source, e.g., raw data versus transformed data versus calculations
  • Grouping of inputs, processing, and output tabs together by color
  • Separate instruction sheets for data import and transformation

Spreadsheets present unique challenges to model validators. By accounting for the additional risk posed by the nature of spreadsheets themselves, model risk managers can contribute value by identifying situations where the effectiveness of sound data, theory, and analysis is blunted by an inadequate tool.