Summary: A survey of 20 opportunities for CFOs, Auditors, and other financial professionals to put Big Data and predictive analytics to work to create value and competitive advantage in your company.
As CFO, Controller, Auditor, or other finance professional busy with the financial controls of your company you have probably heard about Big Data and Predictive Analytics. In preparing for this informational series we thought it would be valuable to simply inventory and briefly describe all of the possible applications. So we searched the literature and talked to our informed sources to deliver this comprehensive list of 20 different areas in which Big Data can or should impact your professional life. In future articles in this series we’ll deep dive this list to give you even more insight.
What we found is that the list can be neatly separated into three components. 1.) The risk and business control components that define the traditional role of the finance and audit professional. 2.) Enhancements to internal and external audit. These are so significant they deserve their own category. 3.) Applications of Big Data and predictive analytics that promise to support finance professionals as the new pro-active stewards of better corporate performance.
Traditional Risk and Business Controls
1. Planning and Forecasting: Predictive analytics makes forecasting, rolling forecasts, and data-driven budgeting easier and more accurate with forecasts down to the business unit or product and service level for greater granularity and ease of roll-up. Big data adds new data sources and increased detail and makes the forecasts fast to update. Improvements to predictive analytics make them more accurate.
2. Strategic Financial Management: Advanced data visualization techniques make financial dashboards and score-carding easy to understand for all levels of users. Big data adds depth to the detail, speed to the updates, and the opportunity to incorporate new meaningful data types both internal to the company and from outside sources. Dashboards can track both leading and lagging indicators to give a more accurate view of the future, not just the past.
3. Enhanced Monthly Financial Closings, Statutory Reporting, and Analysis of Variances: Big data technologies can allow incorporation of both new and traditional data types at very high levels of detail and allow the data to be retrieved and processed in a fraction of the time previously required in traditional RDBMS systems. Statutory reporting now frequently incorporates the requirement for data from outside the company that needs to be integrated with internal data. New Big Data technologies can achieve this integration cost effectively. Predictive analytics and visualizations make all this new data easier to understand for quick action.
4. Compliance and Regulation: The amount of risk, time, and resource drain from these activities can be substantially reduced through application of both Big Data technologies and predictive analytics. As just one example, integrating new data sources from mergers and acquisitions, new business areas, or data from suppliers and partners now required for compliance can be much more easily and cost effectively integrated using new Big Data capture, store, and retrieval technologies.
5. Financial Risk and Insurance: Whether credit scoring business customers, evaluating the risks from complex financial transactions, or utilizing the same predictive tools used by large insurance companies to cost-out risk, predictive analytics offers the power and Big Data technologies offers the granularity and speed to make these decisions more detailed and more accurate.
6. Fraud Detection: Using the greater variety and detail of data available through Big Data to power predictive analytics to highlight potential fraud increases the likelihood of detection. It also decreases the incidence of ‘false positives’ resulting in better, faster detection and protection, and less cost in human resource.
7. Litigation: Responding to disclosure requests or preparing for litigation has traditionally required massive amounts of man power to laboriously comb through mountains of unstructured text, email, instant messages, and other forms of non-automated communications materials. That cost can be dramatically reduced and more relevant sources identified through the automated text analytics available now for the first time through Big Data and analytic technologies.
8. Asset Recovery: Detection of duplicate invoices and duplicate payments is one area where many CFOs start with Big Data. It is now practical to use automated analytic sorting to evaluate years of detailed invoice and payments to identify and recover duplicates thanks to low cost, high speed Hadoop-based Big Data storage and retrieval systems.
9. Collections: Using predictive analytics, CFOs can score potential collection claims to determine which are most likely to be recovered with least effort. In the past, companies with large volumes of smaller outstanding collections weren’t able to justify the labor cost of pursuing these. Now, predictive scoring allows us to identify those most likely to pay and to easily identify the cost-effective cutoff point at which additional labor costs would be greater than returns from the effort.
Internal and External Audit
10. Depth of Audit: In a previous article we spoke of the end of sampling. This can quite literally be true. The new Big Data technologies essentially remove the requirement for sampling by handling massive amounts of data and many different varieties of data at a cost, and in a retrieval and analysis time frame that was previously impossible. On the operational side, it is this ability to look literally at everything that now makes it possible for technology to enforce compliance in real time.
11. Self-Service Discovery: In the past, integrated and free standing IT systems needed to be evaluated individually and together by technology experts to ensure consistency and accuracy. New Big Data tools and analytics have largely automated these tests allowing continuous monitoring without the need for an increase in IT audit human resources. These new analytic tools can deliver results up to 90% faster than traditional tools. Particularly when paired with visualization, items needing scrutiny can be quickly identified.
12. Audit Test Automation: Even the most complex financial transactions can be modeled and examined with the new analytic tools shortening audit cycles, lowering cost, and allowing deeper audits.
13. Data Reconciliation: Data reconciliation across disparate systems has always been an enormously time consuming and important task of audit. Big Data analytics tests and compares transactions across multiple systems and simultaneously tests for compliance to policy and procedure.
14. Report Verification: Analytics can compare the accuracy of reports prepared by separate systems to test and ensure consistency and accuracy.
15. Recalculation: Based on the analytics ability to model and replicate complex transactions and postings, testing, reproducing, and confirming the accuracy of transactions under multiple scenarios is easy and quick.
New and Emerging CFO Roles
16. Efficiency of Execution: Companies increasingly compete based on process excellence and customer intimacy. More than ever it falls to the CFO to help the company navigate the rapidly changing external environment by constantly evaluating and recommending the right strategies and executing those with excellence and a focus on the customer. Big Data technologies and analytics empower CFOs to constantly monitor and recalibrate predictions based on the enhanced ability to understand and analyze internal processes, competitive pressures, and customer response. Quickly finding the answers to specific problems and modifying forward forecasts in near real time allows financial professionals to play an influential role in steering their companies.
17. Growth and Innovation: The same Big Data and analytic capabilities that evaluate efficiency also offer up suggestions for growth opportunities in adjacent markets or complimentary products and services. Big Data enables deeper and broader insights through analytics that can be the difference between successful and unsuccessful acquisition and business development strategies.
18. Valuation and Customer Equity: Traditional valuation techniques based on discounted free cash flows can be greatly enhanced with a clear understanding of customer lifetime value. Big Data and analytics offers a clear path to better understanding customer behavior, buying patterns, and wallet share which can be aggregated up to Customer Equity measures.
19. Customer Risk: The flip side of customer equity is customer risk. Big data and analytics offers a path to better understanding of the big three questions: why they come, why they stay, and why they go. Achieving and monitoring deeper insights into customer behaviors, loyalties, and attitude toward the company’s products and services protects the company’s financial interests. Big data and analytics allows this with new depth and accuracy and CFOs can then correlate these measures with cash flows, profits, and other financial measures to guide strategic decisions.
20. Reputational Risk: A close parallel to customer risk is reputational risk. Reputational risk can arise from customers, non-customers and even employees. It is transmitted with the speed of the internet across the many channels of social media that can make one bad incident into a viral catastrophe. CFOs and audit professionals have both recognized the need to constantly monitor and respond to threats from social media which may be customer comments about products, service, price, or quality, but may also be inappropriate leaks of sensitive or incorrect private financial or transactional plans and data. Big data and analytics now offers a way for financial professionals to develop and maintain a listening post to spot and defend against these risks which have the potential to materially impact financial results.
Bill Vorhies, President & COO – Data-Magnum - © 2013, all rights reserved.
About the author: Bill Vorhies is President & COO of Data-Magnum and has practiced as a data scientist and commercial predictive modeler since 2001. He can be reached at:
original blog at: