Predicting the future is a skill most finance executives probably never expected to cultivate. Yet the “living will” requirements of Dodd-Frank and the Comprehensive Capital Analysis and Review (CCAR) regulatory framework demand that banks and financial services companies be able to efficiently and accurately forecast what’s to come.
It’s not as easy as it looks. Earlier this year, five major banks failed to convince the U.S. Federal Reserve and the Federal Deposit Insurance Corp. that their bankruptcy resolution plans were sound. Companies like JP Morgan Chase, Bank of America, and Wells Fargo were instructed to rework their living wills. If their revised plans don’t pass muster with regulators, these banks could find themselves subject to higher capital or liquidity requirements.
Politics may play a role in determining what firms will be required to forecast. Donald Trump’s campaign promises included a repeal of Dodd-Frank, which could relieve the burden on stressed financial execs. But a change of administrations won’t eliminate the need for effective data analysis and forecasting.
Competing on a view of the future
Forecasting abilities will be essential for the next generation of high-performing banks and financial institutions. Being able to anticipate future conditions, investigate potential business scenarios, model options, and assess potential outcomes will become the most valuable financial skill. And not just to meet regulatory requirements. Forecasting abilities will soon be integral to a bank’s ability to remain competitive.
I’ve talked with many CFOs about this reality. Some ask if their point tools are enough to deliver the type of forecasting they need. My answer is almost always “no.”
The complexity of forecasting requirements is growing. It’s no longer enough to whip up a plan and summarize it in a report. Regulators, board members, and investors will dig deeper. They want proof that the data underlying your forecast is solid. You must be able to defend the reporting. All of the figures must be transparent and traceable. And they want to know that your processes have integrity so they can trust the results.
Those are tough goals to achieve with a bunch of siloed point solutions, which tend to focus on a distinct line of business. Using discrete tools to assess different parts of the enterprise creates inconsistencies – and usually a need for time-consuming reconciliation processes. Often the money spent trying to consolidate results and maintain control would be better spent on innovation. Can your business afford a bottleneck exactly at the point where business users have come to expect instant insight?
Getting the full picture
It’s clear that an enterprise-wide approach to forecasting is a better solution to meet the demand for financial and risk information. By creating a common forecasting platform that incorporates all aspects of your business, you can improve data consistency, develop more efficient methodologies, and reduce the total cost of ownership of your predictive analytics practice.
I advise financial executives to look for an integrated enterprise forecasting platform that includes three components:
- A data platform that integrates data, produces high-quality data, and supports a strong governance and control framework
- A forecasting engine that supports financial modeling and helps you create forward-looking insight into what’s to come
- A selection of visualization, query, and analysis tools that lets business users consume information more easily
Properly designed and executed, this type of solution can help harmonize forecasting activities, delivering rapid responses to high-priority business decision makers and meeting regulatory requirements. The resulting insight also simplifies compliance efforts and makes them more cost-effective.
Of course, when executives hear that a solution spans the enterprise, they worry that it will be costly, take years to deploy, and require an army of consultants. It doesn’t have to be that way. We’ve helped many financial institutions take pragmatic steps to deploy a forecasting platform – by identifying a tactical opportunity, gaining positive results, and building internal expertise while exploiting synergies across the enterprise. With the right support, you can build on tactical short-term wins, creating a foundation for a longer-term forecasting strategy.
Many leading banks and financial institutions realize that regulators are expecting enhanced controls, greater transparency, and guaranteed data integrity than point solutions can offer. A hodgepodge of technologies, data sources, and computing tools simply cannot meet this goal. Is your firm ready to develop a sustainable, strategic forecasting approach?
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