Data Analytics, Predictive Insights & Accounts Receivables Management

Data Analytics for Predictive Insights is assuming far greater importance when it comes to efficiently managing the Accounts Receivables Process. From forecasting customer payment patterns, to spotting possible credit concerns to maximizing cash flow, to improving an organization’s overall financial process, to lowering bad debt, to getting the ability to make well-informed strategic decisions, the role of Data Analytics cannot be undermined.

Businesses and organizations that still rely of siloed legacy technologies and systems have data locked up in themselves and this truly obstructs efficiency by creating an unduly complex, non-integrated and inefficient AR landscape. Integration capabilities can help create a single source of truth that can, not only enhance decision-making, but also give AR Teams and other teams a more seamless & cohesive experience that helps boost productivity and of course motivation.

MAJOR BENEFITS OF INTEGRATING DATA ANALYTICS INTO THE PROCESS OF ACCOUNTS RECEIVABLE MANAGEMENT:

1. INCREASED CASH FLOW VISIBILITY:

Data Analytics helps organizations gain a thorough understanding of their accounts receivable and helps them easily spot payment gaps and/ or delays. AR Teams can easily prevent any late surprises and manage their cash flow in a far productive manner.

2. IMPROVED DECISION-MAKING CAPABILITIES:

Another key benefit of Data Analytics is that organizations gain the ability to make informed strategic decisions regarding every aspect of the AR Management Process, from credit terms, to payment schedules, to collection strategies & tactics etc. This can be done by a simple analysis of past payment trends and customer payment behaviour.

3. INCREASED PRODUCTIVITY & EFFICIENCY

Certain aspects of Data Analytics, such as machine learning algorithms & automation, can help automate many repetitive, mundane tasks that can free up time of the AR Teams to focus on more strategic issues and/ or cultivate client connections.

4. DECREASED RISK & ENHANCED COLLECTIONS

AR Teams can use Data Analytics to foresee possible issues that can creep up at the last minute. This ability can help organizations be better prepared and lessen the effects of delayed payments or non-payments. Additionally, it can guide the overall process in terms of improving efficiencies and enhance collection procedures.

Further, Data Analytics in Accounts Receivable Management helps track and analyse the following KEY METRICS & KPIs:

  1. DSO (Days Sales Outstanding)
  2. AR aging
  3. Average days past due
  4. Collection effectiveness index (CEI)
  5. Customer payment behaviour analysis
  6. Industry wise payment analysis
  7. Account Receivable Turnover Ratio(ART)
  8. Number of Invoicing Disputes
  9. Percentage of High-Risk accounts
  10. Predicted Cash Collection
  11. Staff productivity
  12. Average collection Period
  13. Percentage collection within due date

Author

Sudarshan Banerjee

Inebura , Head of Product

Sudarshan Banerjee is a Product, Process and Automation professional. His areas of interest include Sales Force Automation Tools, Sales Process Construction, Data Science, Data Analytics, Statutory Audit and Compliance, Project Management and Change Management.

He has over 19+ years of experience in Business Development, Sales, Process Planning, Business Strategy and Product Development spanning across various domains namely ITeS, FMCG,Financial Services, Travel& E-com.

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