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Analytics as an interactive planning tool

While the full impact of Dodd-Frank is yet to be felt, it’s clear that banks will face meaningful pressure on profits as a result of compliance with the legislation. As banks develop new plans to shore up profitability, comply with regulations and hold onto high-value customers, analytics can play a critical role.

I’m not just talking about the traditional application of analytics: to predict individual customer risk or behavior as part of the segmentation for a given decision. That’s important, surely, but by focusing on this we narrow the benefit analytics can bring.

What I’m proposing is broader: The use of analytics in the planning process. Bringing analytics into strategy discussions and portfolio planning can give you a structure for problem-solving that accelerates and improves strategic decision making. It’s a topic I cover in more detail in my new Insights white paper.

Analytics can be used to facilitate an iterative approach to planning via construction of an “interactive map” of the problem you’re trying to solve, complete with decision levers, reactions to decisions and ultimate objectives. By mapping the connections between these, you can lay bare the assumptions behind your planning, and use those assumptions—along with any relevant data—to “see what happens” when you change some of the factors.

Our clients have used these decision models very successfully, often as part of a decision optimization process. They’re ideal for the planning process because they enable you to:

  • Identify and test drivers of the business
  • Forecast and simulate results of changing decision strategies / profit models
  • Balance complexity of regulatory constraints, capital allocation, liability management
  • Optimize results under a network of constraints and objectives

All decision makers have a view of the way a particular business situation operates whether they recognize it formally or not. The great advantage of the decision model concept is that it makes that view explicit and ties it to an objective like maximizing sales or profit. It also facilitates knowledge management and transfer within the bank as expertise and experience from multiple experts can be incorporated into the model.

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