“Beyond the Fundamentals: Anatomy of The Perfect Credit Department” ICTF April 7, 2014 Pamela Krank President The Credit Department Inc. (TCD)

Agenda Characteristics of “Perfect” Credit Departments Efficiencies vs Effectiveness Process/Technology/Resource Maximization Strategies for Moving toward Perfection: AR Valuation Bad debt Analysis

The Credit Department, Inc (TCD) Background Manage global trade credit for 52 mid-market companies all over the world 70,000+ bill-to customers 150,000+ open invoices daily Consulted in nearly 500 global companies’ Credit Departments

“A Perfect Credit Department”

A Generalization: Smaller vs Larger Credit Departments Smaller Credit Departments (less than 10 members) tend to be more efficient and less effective Larger Credit Departments (10+ members) are usually more effective but less efficient

Top Consulting Observations on Credit Departments 1.Insufficient focus on managing top risks 2.High cost per credit analysis, collection account, deduction management 3.Work arbitrarily assigned 4.No retrospection on missed opportunities 5.Write-offs common practice 6.Poor/incomplete use of internal data 7.Lack of sophisticated tools/technologies 8.Inconsistent decision-making 9.Policies/procedures not followed/enforced

How do we get our Credit Departments more effective while meeting our CFO’s lower cost goals?

Profile of Perfect/Ideal Credit Departments 1.One Standard Process flow 2.Credit Scorecards Utilized 3.Credit Committee Formed 4.Maximize Automation/Paperless 5.Department of Specialists 6.Best-in-Class results 7.Efficient cost structure 8.No surprises

Process Flow Credit Policy directs each process Only those who add value are a part of the process. Credit resources are defined by impact of risk on the receivables portfolio Collection activity is driven by default risk and cash flow priorities Value in diagramming processes

Credit Scorecards

Credit Committees Representatives from Credit, Finance, Sales, Marketing, Executive Build consensus on credit policy and updates Meet regularly to deal with sales forecasts vs existing credit limitations Deal with inter-company process issues Create a path for approving lines beyond credit recommendations aka, business decisions.

Technology Expectations Automate routine processes (letters, scheduling, statements, small lines) Allow the system to determine daily work queues, not individuals Expect analysts to spend time working risks alerted by the system Code every past due item with status, review dates Focus is on reporting risks and customer information to top management

Staff Resource Utilization Current Resource Utilization Example Most Effective Resource Utilization Staff size determined by history of account management Process & technology maximized first before replacing/hiring new staff Employees are all “generalists” who do the same tasks regardless of asset allocation needs. Work generally divided by alpha, region, or division/product line. Duties assigned to specialists (ie credit analysts, collectors, deduction specialists) based on numbers of credit files, past due accounts, #’s/complexity of deduction files, etc

Obstacles to a “Perfect” Credit Department –Lack of upper management support –Existing team members unable to identify process change needs/implement scorecards –Insufficient budget for technology –Change reluctance by employees –Limited resources

“No Surprise” Reporting Example

No Surprise Tool: Valuation of the Receivable Asset Assign risk probability to every credit customer Compare probability of default to exposure Assign statuses to every past due item with historical probability Calculate the risk

Existing Exposure Example Based on Risk Probability Total Aging: $16,610,000: Customer A/R Totals in these categories from scorecards: Very high VH (20%+): $500,000 High H (10%+): $760,000 Medium high MH (5-10%): $1,250,000

A/R Asset Valuation Risk of Credit Default Very high VH (20%+): $500,000 *.20 = $100,000 High H (10%+): $760,000 *.10 = $76,000 Medium high MH (5-10%): $1,250,000 *.05 = $62,500 Total reserve (part 1) based on Customer Risk of default in Credit process: $238,500 Value so far: $16,610,000-$238,500 = $16,371,500

Delinquent Account Status Reserve Example StatusPast write-off experience Existing Status Balance Predicted Defaults Bankruptcy95%$122,000=$115,900 Bad debt100%$115,000=$115,000 3 rd party85%$80,000=$68,000 Collection Attorney 60%$48,000=$28,800 Final Demand40%$180,000=$72,000 Installment Note 20%$350,000=$70,000 Total Probable Status Bad debt =$469,700

A/R Total Valuation Report Example $16,610,000 gross value ($238,500) Default Risk Probability ($469,700) Account Status Default probability Total Net Value of the Asset: $15,901,800

Bad debt Analysis Example Customer name/acct #____________________ ___________ Bad debt written off$______________ History of risk changes__/___/__ date ____score $________ line amt Within policy limits?___ yes ____ no Missed Opportunity__________________________ Comments_____________________________________

Conclusion Perfect Credit Departments are both effective and efficient We need to ensure our process, technology, and people resources match the needs of the asset There are changes and enhancements we can all make to strive toward perfection in Credit. Surprises ruin perfection….our job is to prevent them from happening

Thank you! Pam Krank The Credit Department Inc (TCD) X 203