For corporate treasurers trying to navigate new federal regulations, the road to compliance may be buried under massive amounts of data.
Today’s regulatory landscape is complex and in flux. Dodd-Frank, FBAR, SEPA, and FATCA all require organizations to improve their tracking of, and reporting on, specific metrics. Treasury and finance teams must be able to deliver accurate, complete details on thousands—if not millions—of transactions, and do so very quickly. They must cull and analyze this data, which changes on a daily basis, while accounting for issues such as data availability, system functionality, and data integrity. In many companies, the traditional tools, such as spreadsheets and applications developed by end users, cannot keep up.
The pain doesn’t end with regulatory compliance. The same issues around data volume, complexity, and transparency that arise in compliance efforts also arise anytime the finance team presents financial figures to interested parties, either inside or outside the organization.
To ensure that their reporting is transparent and accurate across the board, the finance and treasury staff must be able to conduct front-to-back auditing, and must be able to respond quickly to ad hoc requests. They must be able to address evolving reporting requirements, accommodating rules that may differ between jurisdictions yet may overlap. And they must be able to handle exponentially increasing volumes of data.
In the changing regulatory and reporting landscape, being able to quickly and efficiently extract insights from data has become a necessity rather than a luxury. Corporate treasury and finance managers are coming under increasing pressure to adapt their reporting processes and infrastructure to take advantage of the “big data” their organization is collecting. And if they decide to implement an analytics solution to meet compliance requirements, they should simultaneously leverage the solution to improve their function’s performance in other areas.
Because data analytics can provide valuable insights on a spectrum of treasury and finance topics, the treasurer and CFO should not view deployment of an analytics solution as strictly an IT project. Instead, they need to involve themselves in selection and implementation of the software to ensure that it will meet their needs. In doing so, they should keep in mind four goals:
Finding the right insights. By taking a proactive role in their company’s data analytics activities, corporate treasurers can improve their ability to meet their financial oversight and reporting responsibilities.
For example, in the area of analytics sometimes described as “descriptive” analytics, companies with significant third-party sales channels often run analytic tests to ensure that monthly commissions are calculated correctly. These tests may not only verify the accuracy of financial reports, but also raise red flags around people, processes, or systems. Finance and treasury managers who are paying attention can address those red flags to improve overall operations or customer satisfaction.
Another example is the story of the finance team at a large manufacturer that took the lead in spearheading the company's predictive analytics project. Now finance uses the predictive analytics solution to detect patterns in the filing of warranty claims, which enables them to reduce the impact these unplanned events have on the company’s cash management.
When finance or treasury staff are compiling information into a standard report, they can use that report to uncover patterns or potential issues. In addition to this type of capability, a true analytics solution enables staff to answer specific questions by drilling down into data records, and even the fields in the records, as these are often the digital DNA of the business. An analytics solution that offers these capabilities can give a treasury and/or finance team not only more accurate reporting, but actionable insights.
Building in operational efficiencies. While ad hoc data analyses are important for answering one-off questions posed by treasury staff, executives, the board, or regulators, automation is a crucial feature of an analytics product. Automation of routine data analysis simultaneously saves staff a great deal of manual effort and enables more analyses to run against larger data sets than human analysts could accomplish on an ad hoc basis. An automated process also enables a finance team to catch red flags more quickly and act on them before they impact the business—or before a regulatory agency finds the problem.
As treasury teams consider automation, they often find it useful to look at the number of manual reconciliation processes that are part of the monthly close. Many large companies spend hundreds of hours each month on what are essentially analytic processes that could be automated. In evaluating solutions, look for scheduling capabilities that don’t require the involvement of IT programmers, but instead can be built and automated by financial analysts.
Heading off data issues. Poor data quality can result in a compliance catastrophe. When a company compiles reports by automatically consolidating and integrating information across many different departments and disparate source systems, the treasury and finance teams need to be confident that the information is completely accurate. In many companies, inconsistent data values and formats are prevalent.
The problem is that you don’t know what you don’t know. For example, if data records are incomplete, how can the financial analysts be sure that all the records were processed? The finance team needs to consider what happens to a record that does not process: Is that recorded in error logs? Is it examined to understand the root cause of the problem?
A good risk test is to examine how many spreadsheets are actually feeding into the general ledger and then examine some of the more complex spreadsheets that treasury and finance staff are using. Look at those spreadsheets’ lineage, at who maintains them, at how many linked spreadsheets feed the final calculations, etc. It’s important to work backward from the data and business rules (formulas) to their sources, and to look at the tests that are embedded in treasury and finance processes to ensure data does not change between import and analysis, as well as to ensure that formulas are accurate.
Analytics software offers companies the opportunity to verify that the data in their financial reports has been integrated effectively. Treasury and finance professionals can use analytics software to run tests on their aggregated data, using different rules, to check data quality. For example, many large institutions abide by financial data standards to assure that the raw material of their reports is of high quality, but they need to run tests to see whether their data sources are in compliance with those standards.
Look for analytics software that makes it easy to assemble rules, easy to run data through the rules, and easy to see the data that does not meet the rules at a record level. If the devil is in the details, be sure you can see the details. Also, look for software where rules can be changed easily but changes are documented. Hard-coded, rigid solutions are usually short-lived.
Improving transparency. New regulations emphasize transparency, requiring auditors to understand the lineage of a company’s financial data, especially information around derivatives trades and investments. By involving himself or herself in the selection and implementation of an analytics software package, the corporate treasurer can ensure that every step in the process of consolidating and reporting financial data is transparent. Pay particular attention to the visibility the software provides into the data lineage and to how different data points are joined, filtered, enriched, or manipulated before final output reaches the G/L.
Visibility into the full trail that financial data follows, from original source to financial statements, enables corporate treasurers to ensure that the figures they’re reporting and relying on are accurate. It can also enable treasury teams to see trends and relationships between different data silos.
Data Analytics in Action
One firm we work with is in the business of conducting financial audits and recently found itself drowning in data. To generate an audit for a client company, the firm would draw on thousands, if not millions, of complex transactions, which would come from multiple IT systems. The exponential increase in clients’ data volumes was preventing traditional audit tools from providing the necessary insights. However, by implementing a data management and analytics program, this firm has been able to develop comprehensive insights in situations where high transaction volumes make sample-based audits ineffective. For example, this firm set up its analytics software to detect suspicious accounting records and abnormal operations. The software flags transactions that are entered manually, performed outside normal working hours, or that have amounts which differ substantially from similar transactions.
Pursuit of fraud is just one example of the types of insights analytics software can open up for a company. In an organization that is implementing an analytics solution, it would behoove the treasurer and finance managers to involve themselves in the selection and implementation process. Effectively harnessing the software can enable leaders in treasury and finance to improve the performance of their function, in regulatory compliance and a host of other areas.
Increased regulations are an unavoidable reality that corporate treasurers face over and above the standard reporting they must routinely produce for company stakeholders. And it is a reality that is constantly morphing, making it challenging to keep compliance standards and reporting needs up-to-date and effective. However, enabling technologies like agile data analytics platforms tailored for financial needs allow for full adherence to regulations with minimal friction.
The financial winners across the globe over the next decade will be those that use their data most effectively, keep regulators happy, and keep stakeholders satisfied, all while improving operational efficiency. Corporate treasurers who embrace the right technologies will be invaluable players in this success.
Drew Rockwell is Lavastorm Analytics' chief executive officer. Drew joined Lavastorm in 2002 to lead its transformation to a global analytic software and services company. He has more than 30 years of executive and management experience in the communications and software industries.