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Create a Bad Debt Early Warning System Meeting Start Time will be 10:03 Pacific Time PowerPoint Presentation
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Create a Bad Debt Early Warning System Meeting Start Time will be 10:03 Pacific Time

Create a Bad Debt Early Warning System Meeting Start Time will be 10:03 Pacific Time

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Create a Bad Debt Early Warning System Meeting Start Time will be 10:03 Pacific Time

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Presentation Transcript

  1. Create a Bad Debt Early Warning System Meeting Start Time will be 10:03 Pacific Time

  2. Agenda • Session objectives • Current challenges facing AR departments • Review of current credit tools • Introduction of payment trending as a new tool • Late payment calculation discussion • Rules based risk identification • Controlling customers via inbound order hold/release process • Challenges • Opportunities for improvements • ERP enhancements • Questions

  3. Cforia Software • Cforia is a market leader in AR automation software with 140 accounts world-wide managing over $80 Billion in yearly AR • Dozens of ERP applications supported including SAP, Oracle, JDE, PeopleSoft, etc… • AR automation is designed to: • Reduce bad debt • Decrease DSO • Improve deductions processing • Headcount avoidance or reduction

  4. Chris Caparon • VP of Professional Services for Cforia Software • Education: BSEE, BSCE from the University of Michigan • 10 years of ERP implementation experience • 7 years of managing Cforia’s software services team • Personally lead over 100 AR automation projects

  5. Customers

  6. Challenges Accounts receivable is a top asset on your company’s balance sheet Its the most at-risk balance sheet asset from the credit crisis Cash flow is a priority Decreasing or flat sales Tighter credit policies Staff reductions the norm World-wide DSO will be increasing for the foreseeable future As companies will hold onto their money longer Companies with less access to credit will be a challenge

  7. Challenges Continued Deductions and charge backs from retailers will increase as their margins tighten Bad debt is a new and real threat Your AR is a bank for your customers Are they a good risk?

  8. "Watch your receivables like a hawk." - Jerry York, CEO of Harwinton Capital on the coming recession, during CFO Rising, March 2008

  9. How to Create a Bad Debt Early Warning System

  10. Credit Control– Current State Customer Credit -Current State Tools

  11. Hierarchy of Tools • Agency reviews – (DnB, Experian, etc…) • Establish initial credit limits • Latency of data • Each database has holes • Intuition and business intelligence • Credit and peer groups • Collector/analyst interaction • Media and news • Financials and credit scoring • When to trigger? • Credit checking in ERP system • Held order management

  12. New Tools and Techniques • Payment Trending to identify at-risk customers • Leveraging payment trend information locked within your accounting system to alert you to customers at risk • As customer liquidity decreases - their payment timing degrades over time • Deploying that business intelligence within your ERP system by upgrading your credit checking and order management logic • Credit limit management

  13. Payment Trend Analysis • We are going to explain how you can mine this information within your own databases

  14. Let’s Discuss Terminology • DSO vs. DBT • DSO (Days Sales Outstanding) • Excellent business metric • Calculation is inappropriate due to sales variable • DBT (Days Beyond Terms) • Extremely predictive tracking mechanism • It can be calculated from everyone’s database

  15. Terminology Continued • Credit Rating vs. Credit Risk • Credit Rating • Agency driven • Credit Risk • Internal assessment based upon your decision criteria • Can be very different than ratings

  16. What DBT Trending Information Looks Like

  17. Establish Guidelines • The amount of degradation is a key control mechanism • Place thresholds in your calculations to notify the credit managers when customers cross the line • Drives credit risk classification • Use that as a trigger mechanism to engage your existing credit scoring tools or use Cforia’s consolidated credit reporting platform • Credit limit changes • Credit risk establishment

  18. Consolidated Credit Reporting • Edgar Online provides financials up-to-date within 24 hours including: P&L, Balance Sheet, Cash flow and ratios over 4 years • Equifax USA offers financial trade (bank info); Commercial credit cards, LOC’s (Business Lines of Credit), Business Loans and Leases, and industry specific trade data reporting millions of tradelines. • Access to consortium of 350 banks credit databases (SBFE) • Equifax Canada has the largest and most complete database for Canadian Commercial Credit Reports for companies going back over 100 years.

  19. Consolidated Credit Reporting • Experian has the most Commercial Collections with over 250 agencies reporting business bad debts and thousands of companies reporting industry trade data. • Experian also provides Business Owner Scores and credit reports with FICO scores from Experian and Fair Isaac. • Graydon International houses the most comprehensive International database, with over 60 million Commercial Cedit Reports from more than 130 countries. • LexisNexis provides Bankruptcy, Tax Liens, Judgments, Corporate Info and UCC’s.

  20. A Quick Demo of a Credit Alert System

  21. Create a set of rules that Automate sections of your AR database - In this case, a credit rule (#1)

  22. Assign different risk classes (sub group) based On % DBT percentage

  23. The sub group detail is where you set your thresholds

  24. The list of companies are displayed for review

  25. Automated Credit Reports

  26. The DBT Methodology

  27. Collectables – Clean receivables Disputes – Dirty receivables Deductions- Dirty receivables AR Database’s are Complex • Clean Receivables • Collectables : transactions that are not disputed & whose receipt can be forecast • Dirty Receivables • Disputes : transactions that disputed and not paid • Deductions : transactions that are customer debits

  28. Headquarters Parent Parent Customer Customer Customer Customer Customer Invoice Deduct Credit Invoice Deduct Invoice Credit Invoice Invoice Customer Structures are Challenging

  29. The Failure of ERP • Most, if not all, ERP systems fail to deliver accurate DBT data • ERP systems are woeful for creating trending over time information • Rely on BI tools and report writers • The data delivered does not reflect the customer’s actual payment patterns • The underlying calculations are not transparent or flexible to meet the unique needs of companies

  30. DBT Considerations • Create a DBT value that accurately scores the customers payment pattern • Understand that they can be negative! • Select time intervals (week, month, 90 days) • Also look at open invoice DBT • Filter out deductions, credits, disputed late pays • How do you flag disputes in your systems? • Can be product dependent • Service vs. spare parts • Minimum transaction value • Weighted average • Set to zero for certain customer types • Employee, demo’s, inter-company, etc… • Measure at the customer or parent level? • Different calculations by customer type?

  31. Trigger for Action • Once a customer hits a threshold, it is a call to action • Automatic customer, sales rep notification via AR Automation tool • Credit reviews • Financial statement requests and credit scoring • Credit group discussions • The reevaluation of their credit limit

  32. Credit Limits and Customer Control

  33. ERP System Challenges • Credit checking process in all unmodified ERP systems does not meet the needs of most businesses • Math used to check credit in ERP systems • (Open AR + Open Orders)/credit limit • Blanket Orders and seasonality create problems • We need to change how your order entry credit checking system works • As a result, order hold & release can be very manual and labor intensive

  34. Goals • Create an order credit checking process that aligns with your optimal credit policy • Minimize the number of nuisance holds to enable your credit department to manage by exception

  35. Additional Criteria Required • Credit Risk • Collection strategy • DBT trending • Specific age bucket values • What about disputes and deductions? • # late payments over time period • Date of last credit review • Commit windows to filter out future orders • Last order date

  36. ERP Improvements • This is a hard modification to most systems • Order entry program must be opened • User exits may exist • Requires IT support and programming • ERP may not have enough data to support your desired decision tree • What is the right criteria to make you trust and defend the result?

  37. Conclusion • Establish criteria for DBT calculations • Benchmark customer DBT data • Evaluate current triggers for reviewing credit files • Put scheduled automated process in place • Month end is a great start • Tune your order credit checking process to eliminate nuisance holds • Eliminate non value adding activities

  38. For additional information, email 871 9687