According to a recent IDC Technology Spotlight, transforming Accounts Receivable operations is a top priority for CFOs. IDC says organizations with intelligent AR management solutions are well-positioned to withstand any economic uncertainty and find pockets of growth. Big data and analytics can play a vital role in accelerating the AR function.
Financial leaders and staff need improved forecasting, budgeting, and improvement in working capital and cash management, supported by better process execution. Intelligent accounts receivable applications can improve productivity and reduce inefficiency in technology and business processes when big data and analytics are leveraged.
What you’ll learn
The Challenge of Accounts Receivable
Efficient and coordinated automation of accounts receivable operations, offering visibility across the entire enterprise in real-time, is a tall order. Accounts receivable (AR) is a complex environment.
Large organizations, in particular, are often running a half-dozen different financial systems. But the information needed goes beyond ERPs. AR requires information from supporting systems such as customer relationship management, collections management, and cash applications. In addition, there are a host of documents, from contracts to purchase orders, credit applications to credit memos, invoices and checks.
Add to that input from external data sources such as ACH & EDI transactions, lockbox feeds, credit bureaus, and customer payment gateways. Global companies face further operational complications of different languages, currencies, and time zones.
Information and applications are isolated, frustrating efficiency and preventing an accurate and timely view of cash position. Despite efforts to “go paperless,” the inherent isolation has prevented true automation.
Big Data for Accounts Receivable
Companies need all AR data to reside in one place. Companies need a single repository to see everything and from which their applications can operate. Cue “big data.” Big data systems can manage the variety, volume and velocity of data coming into a business.
Companies like Solix capture, deliver and process such data—including international data—in a very cost-effective manner. Big data promises low cost with high-power processing. Deploying big data is the first step in truly automating financial operations.
Without it, information remains separate and disconnected, preventing a company from fully and accurately seeing its situation. With the availability of all data in one place, intelligent technology can deliver on the promise of automation.
Analytics Translate Data into Valuable Information
The data alone does not confer value. But analytics interpret the data, yielding insights for better operations and performance management. PwC has said, “Trying to unlock the power of Big Data without data analytics is like trying to harness the power of the internet without a search engine.”
Analytics allow companies to access and process all the relevant accounts receivable data.
Emagia, an order-to-cash solutions provider, applies advanced statistical techniques and AI machine learning to its analytics to understand patterns and trends in data.
There are three types of analytics. Descriptive analytics report what has happened and why. Predictive analytics show what will happen when conditions are carried forward into the future. Prescriptive analytics allow companies to apply different conditions to find the most favorable outcomes. They enable companies to optimize conditions for best performance.
Through dashboards and reports, the analytics give finance leaders a whole new level of accuracy in their understanding, along with timeliness not possible when data had to be manually brought together and “rolled up” in spreadsheets.
Analytics and Operational Applications Improve Cash Flow
Streamlining AR operations improves cash flow. Operational automation, building on the single data repository and advanced analytics, handles all the order-to-cash (O2C) sub-processes in a very efficient and coordinated manner with little human intervention.
For example, a global agribusiness company brought in Solix and Emagia to help it address several challenges. The company has three business units, operating 100 distribution centers in 90 countries, with 40,000 customers worldwide.
The company’s goals were to pay off debt and expand its product lines and geography. The concern was liquidity and how to manage cash flow. On deployment, Emagia’s cash flow forecast engine looked at past and current information to build a statistical model based on AR data. It identified customer payment patterns across segments, product lines and regions, and which customers were risky, which were late payers.
This identification enabled the company to forecast cash flow across regions, product lines and business segments—the forecast ties into two operational departments. The first is credit management, where Emagia’s application enabled the company to grant credit limits quickly and automatically address renewals. The analytics also tie into collections management to guide a proactive strategy, including incentive structures to manage cash flow better.
The company gained confidence through tighter control on processes as cash flow forecasting interacted with the credit and collections automation. Before Emagia’s deployment, the difference between forecast and actuals was fairly high. However, six months into the deployment, the variance between forecast and actuals narrowed significantly.
This accuracy translated into better working capital management, lower borrowing and improved cash flows. The results included:
- $3 million in interest savings
- Cash forecasting accuracy improved 35 percent
- The average past-due balance decreased by $29 million
- DSO was reduced by 4.5 days
- Average deductions balance decrease from $2.9 to $1.1 million
- “Aced” SOX compliance
The company gained confidence in its expansion and growth plans.
Big Data and Analytics are Transformative
The agribusiness’s results provide an example of what analytics on a holistic data set can do. With such integrated, intelligent operational automation, companies can reduce storage and operations costs, improve control of revenue and profitability, compliance management and cash flow.
The process begins by digitalizing and aggregating all data. With all process data in one system, companies can run analytics and, through process applications, optimize operations.
With a complete view of customers, companies can make the best of customer lifetime value, determine strategies and maximize profits. Timely and accurate financial information can guide sales for revenue growth and operations for cost control.
