RISK-BASED COLLECTION KARL DEVOS 6 NOVEMBER 2014
WHAT S TOP OF MIND FOR YOU? PRODUCTIVITY REDUCE DSO RISK EXPOSURE ANALYTICS DISPUTE WORKFLOW OPERATIONS DRIVE FREE CASH FLOW RISK MANAGEMENT CASH FORECASTING 2
TOP CHALLENGES COLLABORATING WITH SALES COLLECTIONS VOLUME INCREASED RISK DATA FOR EMERGING MARKETS DSO AND PAST DUE A/R ARE INCREASING DISPUTE VOLUME INCREASED PRIORITISING COLLECTION ACTIVITY SCORING OF EXISTING A/R PORTFOLIO 3
SECRET SAUCE 5 INGREDIENTS: 1. CLEAN VIEW OF DATA 2. CREDIT POLICY 3. AUTOMATE WORK QUEUES 4. DISPUTE WORKFLOW 5. RISK BASED COLLECTIONS
GET A CLEAN VIEW OF YOUR DATA 1 5
A SINGLE VIEW OF RISK & OPPORTUNITY ORACLE JDE SAP AS400 CREDIT FACILITATION CENTRALISED REPOSITORY RULES ENGINE COLLECTIONS MANAGEMENT DISPUTE RESOLUTION CASH APPLICATION 6
DOCUMENT AND EMBED A CREDIT POLICY 2 7
ESTABLISH A SOUND CREDIT POLICY PAYMENT BEHAVIOUR BUREAU DATA EMBEDDED CREDIT POLICY ROUTINE REVIEWS CREDIT LIMIT REVIEWS ORDER HOLD / RELEASE SALES FORECASTS 8
AUTOMATE WORK QUEUES 3 9
THE CHALLENGE IS THE PROCESS PRIORITISE REPORTING 1 H PREPARE & DOCUMENT 1 H PROACTIVE CONTACT PROACTIVE CONTACT 3 H 6.5 H DISPUTES & FOLLOW-UP 2 H 1 H 10
BEST PRACTICE APPROACH TO DISPUTES 4 11
AFTER 90 DAYS, 95% OF ALL DISPUTES ARE WRITTEN OFF ROOT CAUSE ANALYSIS SALES PORTAL ACCESS DISPUTE WORKFLOW NOTIFY TRACK ESCALATE TO NEXT LEVEL TRACK FOR RESOLUTION ASSIGN TO RESOLVER 12
CASH APPLICATION PROCESSING VALIDATION DATA CAPTURE MATCHING AND REMITTANCE DATA CONSOLIDATION PLACEMENT OF EXCEPTIONS REDUCED PROCESSING FEES 13
PRIORITISE WORK QUEUES BY RISK 5 14
HOW DO YOU PRIORITISE YOUR COLLECTIONS? AGING VALUE CUSTOMER RISK GRADE CUSTOMER TYPE 88% OF COMPANIES ARE STILL USING AGE AND VALUE TO DRIVE COLLECTIONS PRIORITISATION 15
VALUE TO CLIENT EVOLUTION OF COLLECTIONS PREDICTIVE BASED VALUE / AGING BASED STRATEGY BASED ANALYTICAL COMPLEXITY 16
COLLECTION RISK: WHAT IS IT? COLLECTION RISK Analyses and predicts based on historical payment data the probability that a customer that pays well today will become an unreliable and/or late payer within the next 2 months STATISTIC MODELL that was developed to PREDICT PROBABILITY OF DEFAULT PRIORITISE COLLECTIONS ACTIVITIES 17
USING PREDICTIVE ANALYTICS CONSUMER MARKETING MEDICAL DEBT ELECTRICITY DEMANDS CREDIT CARD FRAUD 18
CONTACT CUSTOMERS BASED ON LEVEL OF RISK LOW RISK DUE DATE 5 DAYS AFTER DUE DATE 5 DAYS LATER 5 DAYS LATER i MEDIUM RISK DAY BEFORE INVOICE IS DUE DAY AFTER DUE DATE 5 DAYS LATER 3 DAYS LATER HIGH RISK 5 DAYS BEFORE INVOICE IS DUE DAY INVOICE IS DUE 5 DAYS LATER 5 DAYS LATER 19
POWER OF PREDICTIVE ANALYTICS WHO DO YOU CALL FIRST? Owes 25,000; 30+ Days Past-Due Scores Low Risk CUSTOMER A Probability of BAD is 2% Cash at Risk: 2% x 25,000 = 500 Owes 15,000; Less than 30 Days Past-Due Scores High Risk CUSTOMER B Probability of BAD is 50% Cash at Risk: 50% x 15,000 = 7,500 20
COLLECTION RISK: THE BENEFITS REDUCTION OF DAYS SALES OUTSTANDING (5%-20%) REDUCTION IN BAD DEBT WRITE-OFFS (1%-20%) REDUCTION OF THE NUMBER OF FULL TIME EMPLOYEES NEEDED TO MANAGE VOLUME (10%-35%) ROI REDUCTION IN CURRENT EXPENDITURE FOR CREDIT BUREAU DATA (20%-80%) REDUCTION IN FEES TO COLLECTION AGENCIES AND COLLECTION OUTSOURCE PARTNERS (SAVINGS DEPENDENT ON YOUR NEGOTIATED RATES) INCREASE IN REVENUE TO LOW RISK CUSTOMERS BY IDENTIFYING CROSS- SELL AND UP-SELL OPPORTUNITIES (REVENUE GROWTH DEPENDENT ON FLEXIBILITY TO INCREASE CREDIT LINES AND PRODUCT PROFIT MARGINS) 21
COLLECTION RISK: INTEGRATED OR STAND ALONE ERP1 ERP2 ERP3 GetPaid Risk Services 22
BRINGING IT ALL TOGETHER WITH GETPAID 23
HEADQUARTERS: Netherlands HEINEKEN After two and a half years, late payments have declined by half and DSO improved by 21% INDUSTRY: Beer distributor operations in 170 countries SAP HEINEKEN TODAY 65% DECREASE SAP IN LITIGATIONS 21% DISPARATE IMPROVEMENT SYSTEMS IN DSO INCONSISTENT 100% PORTFOLIO COVERAGE 24
Peter Verhoeve Sales Executive peter.verhoeve@sungard.com +31 6 5799 2086 Karl Devos Pre Sales Consultant karl.devos@sungard.com +32 496 2623 53 25