How can CFOs leverage Accounts Receivable Data: Key Insights From EY, Atradius, and HighRadius Experts

What you’ll learn

  • What are the key impacts CFOs can create using advanced data analytics in Account Receivable?
  • How to build the right approach to exploring the CFO’s data gold mine?

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In the face of unpredictable economic conditions, businesses must respond to changes quickly and efficiently. For the office of the CFO, this implies a transformation that requires them to become better decision-makers, and thereby the organization’s game-changers. As improving cash flow and driving better business outcomes become the need of the hour, the Accounts Receivable process has made it to the top of every CFO’s priority list. Elena Bail, Senior Manager of Transaction Advisory Services at EY, Brad Anderson, Digital Transformation Manager at HighRadius, and Dirk Hagener, Director of Group Marketing & Communication at Atradius highlighted how Accounts Receivable data remains a missed opportunity for most CFOs at the recent talk show hosted by HighRadius. Nearly $4 billion was trapped in Accounts Receivable across 1,000 companies in the US, as reported by The Hackett Group in 2020. The Accounts Receivable landscape has not changed much, and the rapid data proliferation has not made it easy for CFOs to find their way to the eye of the storm. 74% CFOs believe that growing data volumes have an impact on their roles, as reported in EY’s DNA of the CFO 2020 survey. Highradius

The Analytical Impact - Results Across Key Accounts Receivable Verticals

In an extremely dynamic business environment, it becomes crucial that CFOs fully understand the impact they can create with every move they decide to make. Here are the key direct and indirect impacts that CFOs can create with effectively data-driven decisions.

Ripple Effect of CFOs’ Data-Driven Decisions: Key Direct Impacts on Accounts Receivable

  • Risk Awareness & Its Results

    Backed by data-driven models, CFOs can gain insights into the risks related to each customer or team and create transparency across finance functions. With that, the office of the CFO becomes well-positioned to manage those risks proactively by drawing strategic, data-powered plans focusing on three key components –  process, pricing, and service, and implementing strategic decisions based on those insights. When clubbed with the right business operating model and management practices, advanced data analytics powered by artificial intelligence and machine learning can help CFOs create crucial impacts across Accounts Receivable processes:

    • Reduce DSO

      With advanced data analytics, finance leaders can leverage data to predict customer behavior, late payments, and potential invoice aging. Such crucial information allows organizations to take proactive actions to manage customers’ accounts before they fall behind.

    • Lower Bad Debt and Automated Writeoff

      Better customer segmentation, including innovative behavioral segmentation, with data analytics driven by AI/ML, enables effective collections processes and strategy implementation. It can positively impact the collections agenda, from the management of bad debt and reduction in automatic write-offs to easy dispute resolution and prevention of revenue leakage.

    • Boost Recovery Rate and CEI

      Lack of customer prioritization, incorrect or delayed invoicing, and dearth of flexible payment formats are a few key factors that impact recovery rate and CEI negatively. Predictive data analytics powered by AI/ML helps analyze customer data in the earlier credit stages and identify accounts that are potentially at higher risk, enabling Accounts Receivable teams to segment them accordingly and introduce specific credit and collections policies.

    • Enhance Team Productivity

      Advanced analytics techniques can help CFOs interlink several datasets and generate actionable insights with 360° real-time visibility through smart and dynamic dashboards, eliminating time-consuming, error-prone, low-value manual tasks in Accounts Receivable departments. Easy access to the right data reduces the operational wait time, freeing up analysts’ time and improving the Accounts Receivable team’s productivity. Finance leaders can assign those resources to manage other critical processes and exceptional cases.

    • Improve Credit Review and Cash Flow

      Advanced data analytics tools help mitigate credit risks and decrease the customer onboarding time, thus, improving cash flow and customer experience. They help consolidate customers’ transactional and sales-performance data, track changes in their credit and payment profiles, analyze customer intelligence (such as calls to service centers and complaints), and assess other risk indicators (such as news alerts, bankruptcy, and court filings). The tools eliminate the need for periodic reviews with alerts and suggestions on revised credit terms in real-time.


    The Data-Powered CFO: Key Indirect Impacts on Accounts Receivable


    • Analyze Customer Trends

      Advanced data analytics models enable micro-segmentation levels, which lets finance leaders move away from a ‘one glove fits all approach’ to more specific and targeted approaches. These models also enable CFOs to simulate business scenarios, and using actionable data insights, pre-define strategies and actions for such scenarios, predict customers’ payment behavior, and thereby improve the company’s cash flow.

    • Improve Strategic Decision-Making

      Powered by AI/ML, advanced analytics models can enhance sales forecasting by analyzing historic and real-time transaction information. They take internal (cash flow, revenue growth, etc.) and external factors (competitor activities, economic projections, etc.) into consideration and correlate them. These simulations let CFOs foresee the possible outcomes, based on which smart, strategic, and proactive decisions can be taken. They also offer detailed insights into customers’ profiles and key areas where value is derived from, so that the CFO can improve the business’ strategic positioning.

    • Contribute to Business Profitability

      By implementing advanced analytics models on historic data, finance leaders can effortlessly identify the “right customers”, gain accurate insights into customer value, and offer each customer the best terms, pricing, and services based on their value. They can also make data-driven decisions to eliminate rogue spending and invoice fraud by including credit risks and other key data that may not have been visible with legacy systems. Advanced data analytics models also help in strategic positioning by identifying opportunities to improve business outcomes and tighten up the bookkeeping and internal controls to adjust expenditures across the organization through improved communication and collaboration.

    • Ease of Cross-Departmental Collaboration

      74% of finance leaders claimed that aligning decisions with other departments remained their most time-consuming roadblock in individual decision-making in a poll that was conducted as part of the talk show. With advanced data analytics tools, finance leaders can get streamlined data from multiple departments and pre-define decision-making across the entire Accounts Receivable process. Leveraging data-driven insights minimizes the enormous effort and time taken for collaboration, and advanced analytics tools offer dynamic dashboards that act as a single transparent source of truth for every key stakeholder across the organization.

3 Ways to Build the Right Data - Driven Approach for the Office of the CFO

By investing in advanced data analytics, CFOs are not only preserving the relevance of finance operations but also playing a key role in aligning investments with business strategies. But to make sure that this investment is yielding results, the office of the CFO must assure 3 key factors:

  1. Ensuring Data Quality: Standardized data collection processes can be a starting point to ensure the right data quality, and it starts with automation. Applying smart automation at relevant sources ensures minimal manual intervention in data sourcing. Advanced technologies can give high-quality data that is extracted in real-time, which will enhance the CFO’s custom data-driven decision model.
  2. Prioritizing Internal and External Data Sources: In an era of rapid data proliferation, the identification of relevant data sources is an important step.Highradius

    Finance leaders have all essential financial and process data, including customer profiles, past payment behaviors, and credit profiles, at their disposal.  They can harness this data to make proactive decisions. For example, if it is observed that a customer’s payment behavior is changing, finance teams can take immediate follow-up actions for the same. Other than internal data, CFOs can even consider ESG rating and social media metrics.  ESG rating indicates customers’ exposure to long-term environmental, social, and governance risks. Considering the ESG rating as a data source gives more insights into their potential risks and growth opportunities. Another emerging external source is social media, which generates terabytes of nontraditional, unstructured data. Data also flows in from sensors, monitored processes, and external sources, ranging from local demographics to weather forecasts. Based on all these factors, CFOs can analyze customers’ potential and anticipate their behavior.

  3. Applying the Right Analytics on Data: 80% of CFOs consider providing data analytics-driven insights into cost and effectiveness improvement opportunities an imperative. Throwing data scientists’ teams at Accounts Receivable data, hoping to find commercially viable insights may not be the right approach to generate timely, positive Accounts Receivable results. To effectively monetize the data, CFOs should seize opportunities with out-of-the-box AI/ML capabilities that not only help transform enterprises, but also the scope, responsibilities, and power of the office of the CFO itself. AI/ML has the power to disrupt the traditional finance back-office and mine data for insights for the front office.

It’s also important for CFOs to have a pragmatic approach to data – understanding that leveraging overengineered data models is not the CFO imperative but practicing data-driven decision-making is.  And what makes advanced data analytics tools a long-term strategy for the office of the CFO is the fact that they have the scope to evolve and accommodate the increasing requirements of the CFO and the business.

Finding the Right Data Partner - A Brief CFO Checklist

The process doesn’t end with selecting the right approach. The next step is to select the right technology partner for your data approach. Here is a list of the crucial questions that a CFO may want to consider answering before selecting the technology partner:

  • What is the organization’s data strategy and finance transformation roadmap to accommodate the evolving needs of finance?
  • What data questions would the office of the CFO like the analytics model/tool to answer, and what decisions should it support?
  • How well can the vendor address the CFO’s specific pain points with the right data and technology?
  • What functionalities and features can the advanced data analytics tool offered by the vendor bring to the table other than addressing the key problems?
  • How flexible is the tool to accommodate the organization’s growing finance data needs and business goals over the long term – with a keen focus on finance data architecture, governance, and management?
  • What is the vendor’s support and maintenance plan for the post-implementation phase?

Undoubtedly, the future of finance will be data-driven, and CFOs should move toward building future-ready data models that explore the Accounts Receivable data goldmine. Now, the right move for the office of the CFO is to embrace the data explosion and accessibility and invest in and implement the right technologies focused on specific business goals.


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HighRadius Integrated Receivables Software Platform is the world's only end-to-end accounts receivable software platform to lower DSO and bad-debt, automate cash posting, speed-up collections, and dispute resolution, and improve team productivity. It leverages RivanaTM Artificial Intelligence for Accounts Receivable to convert receivables faster and more effectively by using machine learning for accurate decision making across both credit and receivable processes and also enables suppliers to digitally connect with buyers via the radiusOneTM network, closing the loop from the supplier accounts receivable process to the buyer accounts payable process. Integrated Receivables have been divided into 6 distinct applications: Credit Software, EIPP Software, Cash Application Software, Deductions Software, Collections Software, and ERP Payment Gateway - covering the entire gamut of credit-to-cash.