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Net30 Score Sample Validation Analysis
1.
© 2008 PredictiveMetrics,
Inc. Confidential & Proprietary Portfolio / Collection Scoring Models Net30Score Overview October 2010 Sam Fensterstock - Director of Business Development Statistical Scoring Solutions Maximizing results by understanding risk
2.
© 2008 PredictiveMetrics,
Inc. Confidential & Proprietary 2 © 2010 PredictiveMetrics, Inc. 2 Established to provide higher quality analytics and predictive scoring for B2B and B2C applications in a customer service oriented environment. Analytical staff is comprised of Ph.D. and master level econometricians and statisticians with extensive experience developing predictive decision tools. Pioneers of using “internal A/R and performance data” to drive statistical portfolio scoring models. Have proprietary software and state-of-the art hardware in a SAS 70 II environment designed specifically for production of sophisticated analytical applications through the web. PMI is not a data or a software company, it is a statistical modeling company that specializes in collection scoring as well as provide solutions for credit line analysis and new application approval. Celebrating 15 Years of Predictive Scoring Excellence About PredictiveMetrics (PMI) Turning Probabilities into Profits™
3.
© 2008 PredictiveMetrics,
Inc. Confidential & Proprietary 3 © 2010 PredictiveMetrics, Inc. 3 Manufacturers PMI’s Customers - A Tribute to Our Success Integrity Portfolio Funding LP Investments Wholesale / Dist Debt Buyers Collection Agencies Utilities Transportation Financial Services Other GE Equipment Services Europe B.V
4.
© 2008 PredictiveMetrics,
Inc. Confidential & Proprietary 4 © 2010 PredictiveMetrics, Inc. 4 How Can Statistical Scoring Effectively Streamline The Collection Process? Most collection departments manage and prioritize collections based on the aging of customer accounts. The customer who owes the most money and is aged out the furthest has the highest priority in the collection process. Using the traditional aging method can result in customers who owe low dollars, but are high risk to be ignored, while conversely a lot of time is spent on customers who owe higher dollars, but are not at risk, because they are easy to collect from. Collection scoring determines customer risk and uses that risk level in conjunction with aging as the primary driver of a collection strategy for the on-going management of the customer. Better resource allocation – have collectors focus on those accounts that have the highest probability of probability of delinquency and are the greatest value of risk to your company, not just those that owe you the most money for the longest period of time. Helps companies transition from “Aging Based Collections” to “Risk Based Collections”
5.
© 2008 PredictiveMetrics,
Inc. Confidential & Proprietary 5 © 2010 PredictiveMetrics, Inc. 5 Customer A Low Risk Owes $25,000 in payments; over 30 days past due Probability of BAD (PBAD) is 2% Dollars at Risk (DAR) is 2% x $25,000 or $500 Risk Based Collections Example Customer B Very High Risk Owes $25,000; less than 30 days past due Probability of bad (PBAD) is 50% Dollars at Risk (DAR) is 50% x $25,000 or $12,500 Aging based collections says the collection focus should be on Customer A as they owe 2.5x’s the amount of money and further aged. Risk Based Collections says that the initial early month collection focus should be on Customer B as their DAR is 25x’s the DAR for Customer A.
6.
© 2008 PredictiveMetrics,
Inc. Confidential & Proprietary 6 © 2010 PredictiveMetrics, Inc. 6 Sample Collection Strategies Based On Validation Results Extreme Risk Very High Risk High Risk Moderate Risk Low Risk Very Low Risk Don’t release the order, mitigate risk, and implement aggressive collection treatments Release order and implement aggressive collection treatments from the day of release Release order and implement moderate collection treatments from day of release Utilize normal collection treatments and target for revenue generation and auto clear
7.
© 2008 PredictiveMetrics,
Inc. Confidential & Proprietary 7 © 2010 PredictiveMetrics, Inc. 7 What Type Of Scoring Provides You With The Most Predictive Risk Assessment? Bureau Scoring – Statistical scores that use the bureau’s data and are designed for new customer credit risk evaluation. Scores are the same for all users and predict the same event. Judgmental / Rules Based Scoring Models – Uses internal and bureau data in a scorecard platform that is designed for new account and credit line decisions. Scorecard variables, weights and score ranges are subjectively determined. Output is a rank ordering of customers and does not quantify risk. Statistical Portfolio / Collection Scoring Models – Internal A/R and performance data drive the model, but can combine bureau or other external data. Statistics determines scorecard variables and weights. Output is a specific probability of a future delinquency. Specifically designed for managing existing customer risk for collections, credit line management, order releases, and calculating bad debt reserves. Predictive scoring for managing your portfolio is not a “One Size Fits All”
8.
© 2008 PredictiveMetrics,
Inc. Confidential & Proprietary 8 © 2010 PredictiveMetrics, Inc. 8 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0 10 20 30 40 50 60 70 80 90 100 Percentile %FutureBadCapture Statistical Scorecard Bureau/Generic Scorecard Judgmental Scorecard Random Greatest Risk Least Risk Which Score Is Best For Managing Your Portfolio? In the worst 10% of the portfolio the statistical score captured 89.9% more future BADS than bureau scores and 299% more than the judgmental scorecard STATISTICAL BASED SCORING ALWAYS OUTPERFORMS JUDGMENTAL AND BUREAU SCORING
9.
© 2008 PredictiveMetrics,
Inc. Confidential & Proprietary 9 © 2010 PredictiveMetrics, Inc. 9 Net30Score™ For Collections A portfolio monitoring tool designed to help companies assess the future risk of existing customers and the probability of delinquent payment and write-off A statistical decision model for monthly accounts receivable portfolio management. For trade credit, primarily Net/30, 10-day terms, but can be adapted to handle any terms. Leverages the predictive power of internal data including details on current and past payment behavior. Designed to predict the specific probability of any GOOD customer becoming a BAD customer at some point over a 6 (six) month period, from the date of the score. Client determines the BAD definition for the model. The model can be enhanced with bureau data.
10.
© 2008 PredictiveMetrics,
Inc. Confidential & Proprietary 10 © 2010 PredictiveMetrics, Inc. 10 Net30Score Monthly Outputs 1. Net30Score – Scale of 0.01 to 100, with 0.01 being the worst score and 100 being the best 2. Probability of Bad (PBAD) – The probability that a customer will go bad within 6 months from the date of score** 3. Risk Class – Used as the basis for applying collection strategies 4. Trend (Available through ScoreMiner) – Is the customer getting better or worse and at what rate 5. Dollars At Risk (DAR) – The dollar value for a customer that is at risk, computed by multiplying the Probability of Bad by the outstanding A/R balance** 6. Adverse Reason Code 1 7. Adverse Reason Code 2 8. Adverse Reason Code 3 Explains why a customer scored the way they did Delivered through PMI’s web application ScoreMinerSM or through flat file transfer **these are unique variables that are only PMI delivers
11.
© 2008 PredictiveMetrics,
Inc. Confidential & Proprietary 11 © 2010 PredictiveMetrics, Inc. 11 Adverse Reason Code Score 100 Adverse Commententary AR001 At least one incidence of delinquency over past 3 months AR002 Average percent of balance delinquent past quarter is moderate to high AR003 At least one incidence of delinquency over past 6 months AR004 Average percent of balance delinquent past 2 quarters is relatively high AR005 Recent delinquency AR006 Average percent of balance moderate to severely delinquent over recent history is relatively high AR007 Current moderate delinquency not seen in past three months AR008 Incidence of moderate to severe delinquency in recent past AR009 Incidence of moderate to severe delinquency in last 6 months AR010 Several incidences of moderate to severe delinquency in last 6 months AR011 Currently moderate or severely delinquent AR012 Currently delinquent AR013 Trend of increasing delinquency AR014 Incidence of relatively large percent of balance moderate to severely delinquent in last 6 months AR015 Current percent of balance moderate to severely delinquent is relatively high AR016 Sustained level of moderate to severe delinquency over past 6 months AR017 Currently moderate to severely deinquent with limited delinquency over recent history AR018 First time moderate to severely delinquent occurred in last 3 months AR019 A given balance is becoming more delinquent over time AR020 Incidence of relatively large percent of balance moderate to severely delinquent in recent past AR021 First time moderate delinquency in past 6 months AR022 High variance in average days outstanding AR023 Either no delinquency in past and now delinquent, or severe delinquency in past that remains at moderate level AR024 Large month to month change in proportion of balance current to delinquent Net30Score Adverse Reason Codes
12.
© 2008 PredictiveMetrics,
Inc. Confidential & Proprietary 12 © 2010 PredictiveMetrics, Inc. 12 Scoring outputs are typically delivered either daily, monthly or quarterly an in many formats 1. Hosted web based reporting & query system All reports are exportable to Excel, PDF or CSV formats 2. Flat ASCII, XML or CSV file for uploading and integration into internal systems ERP systems – SAP, Oracle, JDE, PeopleSoft Legacy AR Workflow and collection applications – SAP FSCM, Oracle Advanced Credit & Collections, GetPaid, Forseva, eCredit, CFORIA and WorkflowAR 3. Standard Monthly Reports delivered in Excel spreadsheets Minimal IT resources necessary to get most scoring solutions up and running Net30Score Is Easy To Implement
13.
© 2008 PredictiveMetrics,
Inc. Confidential & Proprietary 13 © 2010 PredictiveMetrics, Inc. 13 Net30Score Validation Analysis PMI is provided with 18 or more months of; month-end aging data plus other internal data elements. Most recent 6 months of data are put on the side and used as the performance period The oldest 12 months of data is then applied to the Net30Score model and uncovers past trends, magnitudes and payment patterns, and formulizing this information to predict future payment performance. The middle 6 months of data (the last six months of the data that was applied to the model) is then used to validate the model. We score the six months of data and assess the future probability of BAD for each customer. We then go to the performance period and compare the models predictions to the actual customer performance to evaluate the model’s ability to differentiate future problem payers from future timely payers. This process helps quantify how accurately UtilityScore predicted future customer payment behavior. PMI will perform FREE validation analysis for your company to prove to you the predictiveness of our models
14.
© 2008 PredictiveMetrics,
Inc. Confidential & Proprietary 14 © 2010 PredictiveMetrics, Inc. 14 Net30Score Validation Data Characteristics 46,623 Average Number of Account Observations per Month Over Validation Period (7,836) Average Number of Accounts per Month Already Bad at Time of Score (Special Score 999) (4,578) Average Number of Accounts per Month for Which there Was Insufficient Information to Score (Special Score 998) (73) Average Number of Accounts per Month Balance > $750K (Special Score 997) 34,136 Average Number of Accounts Per Month Model Scored in Validation Test Average Number of BADs Over Performance Period per Month 4,294 (Bad Rate = 12.6%) Bad Definition: More than 10% of monthly outstanding balance goes 90 or more days past due, or incidence of write-off greater than $100 over the 6 month period following scoring. Model Validated Using 18 months of historical data. Validation Period: Most recent six months of data is used as performance period. Sufficient Information to Score: At time of score, Customer is present in accounts receivable database two or more months, with at least one month showing a non-zero outstanding balance, relative to month of score and prior five months.
15.
© 2008 PredictiveMetrics,
Inc. Confidential & Proprietary 15 © 2010 PredictiveMetrics, Inc. 15 Net30Score captured 49% of all future BADs through the 10th percentile and 79.9% of all future BAD’s through the 30th percentile 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0 10 20 30 40 50 60 70 80 90 100 Percentile %FutureBadCapture Net30Score Random Greatest Risk Least Risk KS = 0.58; PI = 73.08 Net30Score Validation Results
16.
© 2008 PredictiveMetrics,
Inc. Confidential & Proprietary 16 © 2010 PredictiveMetrics, Inc. 16 Predictiveness Index Model Screening Effectiveness 0 10 20 30 40 50 60 70 80 90 100 Percentile Rank Order From Highest To Lowest Risk 0 20 40 60 80 100 Model Random % Of Bad Captured Perfect Area X Area Y Calculating The PredictiveMetrics PI Assuming a 10% BAD Rate A perfect model would screen out 100% of the BADS in the worst 10% The Predictiveness Index measures the Models “perfect” percentage P = (Area X + Area Y) is the capture rate from perfect predictiveness M = Area Y is the capture rate of the estimated model PI = M/P A PI of 55 suggests that the estimated model captures 55% of the perfect area
17.
© 2008 PredictiveMetrics,
Inc. Confidential & Proprietary 17 © 2010 PredictiveMetrics, Inc. 17 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60% 65% Future90+Days Delinquency 10 20 30 40 50 60 70 80 90 100 Percentile Net30Score Validation Results By Population Percentile Greatest Risk Least Risk Average Bad Rate = 12.6%
18.
© 2008 PredictiveMetrics,
Inc. Confidential & Proprietary 18 © 2010 PredictiveMetrics, Inc. 18 Net30Score Validation Results By Risk Class For more efficient operations, 6 risk groups are utilized and accounts assigned based on their collection score. In the Extreme Risk category, the probability of an account going BAD is 71.9%. We can expect that 7 out of every 10 accounts will not pay on a timely basis. On the other hand, in the Very Low Risk category, the probability of an account going BAD is 1.0%. Therefore, we can expect that only 1 of every 100 accounts will not pay on a timely basis. Average Number of Accounts per Month in Validation Sample - 34,136 Cumulative Number of Cumulative Average Percent Accounts to BAD Percent Number of Percent of of Accounts Pay in Rate BAD Accounts in Accounts in Through >90 Days in Through Risk Net30Score Risk Risk Risk or Not at All Risk Risk Interpretation Range Category Category Category (BAD) Category Category Extreme < 19.47 1,751 5.1% 5.1% 1,259 71.9% 29.3% Very High 19.48 to 39.34 2,055 6.0% 11.2% 898 43.7% 50.2% High 39.35 to 44.26 1,949 5.7% 16.9% 507 26.0% 62.0% Moderate 44.27 to 62.49 10,340 30.3% 47.2% 1,320 12.8% 92.8% Low 62.5 to 83.35 4,431 13.0% 60.1% 169 3.8% 96.7% Very Low > 83.35 13,610 39.9% 100.0% 140 1.0% 100.0% ANALYSIS OF NET30SCORE OUTPUT
19.
© 2008 PredictiveMetrics,
Inc. Confidential & Proprietary 19 © 2010 PredictiveMetrics, Inc. 19 DOLLARS AT RISK (DAR) - CURRENT MONTH ALL ACCOUNTS Cum % Number Cum % % Scored Scored Expected of % Scored Scored A/R % A/R Accounts Accounts BAD Rate Accounts Accounts Accounts in in DAR in DAR in DAR Score in Risk in Risk in Risk Thru Risk Risk Risk Risk Risk Thru Risk RISK CATEGORY Range Category Category Category Category Category Category Category Category Category Extreme Risk < 19.47 71.7% 1,702 4.9% 4.9% 24,129,333$ 6.1% 16,710,068$ 29.8% 29.8% Very High Risk 19.48 to 39.34 45.2% 2,136 6.2% 11.1% 63,396,529 16.1% 28,130,525 50.2% 80.0% High Risk 39.35 to 44.26 24.8% 1,662 4.8% 15.9% 12,935,784 3.3% 3,166,173 5.7% 85.7% Moderate Risk 44.27 to 62.49 12.2% 9,416 27.3% 43.2% 53,761,952 13.7% 6,812,505 12.2% 97.8% Low Risk 62.5 to 83.35 3.5% 4,656 13.5% 56.7% 23,132,345 5.9% 989,653 1.8% 99.6% Very Low Risk > 83.35 1.1% 14,970 43.3% 100.0% 7,140,577 1.8% 222,106 0.4% 100.0% Total Scored Accounts 11.8% 34,542 100.0% 184,496,520 46.9% 56,031,031 100.0% Percentage of Scored A/R Portfolio at Risk 30.4% Bad At Score 999 100.0% 7,757 108,113,392 26.3% 108,113,392 Too Thin to Score 998 . 4,358 9,066,129 2.2% - Other 997 . 74 91,401,917 22.2% - Total Non Scored Accounts 12,189 208,581,438 53.1% 108,113,392 Total All Accounts 46,731 393,077,958$ 100.0% 164,144,422$ Percentage of Total A/R Portfolio at Risk 41.8% Validation Results By Dollars At Risk The Extreme and Very High Risk Categories, while representing only 11.1% of the scored population, contains $44,840,593 of the Dollars at Risk or 80.0% of the Dollars at Risk. Assuming a 20% collection effectiveness rate in the Extreme and Very High Risk categories, you would improve cash flow by $8,968,119 and reduce losses accordingly. This is another indicator of Net30Score’s ability to segregate accounts into GOOD and BAD groupings, thereby providing the basis for better allocation of credit and collection department personnel.
20.
© 2008 PredictiveMetrics,
Inc. Confidential & Proprietary 20 © 2010 PredictiveMetrics, Inc. 20 Net30Score Validation Analysis Summary Net30Score captured 79.9% of all future BAD’s within 30% of the scored population demonstrating excellent separation of GOOD vs. BAD accounts. The Very Low and Low Risk Groups represent 52.9% of the scored population and have an average BAD Rate of 2.4%. These classes of accounts are possible candidates for auto-accept and increased credit lines. Likewise, the Extreme, Very High and High Risk Groups represent 16.8% of the scored population and have an average BAD rate of 47.2% and contain 85.7% of the Dollars at Risk. These classes of accounts are possible candidates for aggressive collection strategies and auto-reject of additional credit applications. On a dollar basis for the current month, the Extreme, Very High Risk and High Risk Groups contain 85.7% of the dollars at risk of the scored population. The validation results will prove to you, using your own data, how powerful a tool Net30Score is for assessing the future risk with your customers
21.
© 2008 PredictiveMetrics,
Inc. Confidential & Proprietary 21 © 2010 PredictiveMetrics, Inc. 21 Champion / Challenger Analysis Net30Score vs. Bureau Generic Scoring and Judgmental Scorecards
22.
© 2008 PredictiveMetrics,
Inc. Confidential & Proprietary 22 © 2010 PredictiveMetrics, Inc. 22 Some comparisons to leading credit bureaus’ generic scores: Commercial Bureau Generic Score 1 (GS1) 72,294 Accounts Had a Valid CBPI 2,674 BADs in Test (BAD Rate = 3.7%) Commercial Bureau Generic Score 2 (GS2) 42,333 Accounts Had a Valid CBCS 1,547 BADs in Test (BAD Rate = 3.7%) Commercial Bureau Generic Score 3 (GS3) 42,079 Accounts Had a Valid CBBS 1,514 BADs in Test (BAD Rate = 3.6%) Customer Judgmental Rule-Based Score (CRBS) 68,030 Accounts Had a Valid CRBS 5,274 BADs in Test (BAD Rate = 7.8%) Net30Score vs. Bureau Scores
23.
© 2008 PredictiveMetrics,
Inc. Confidential & Proprietary 23 © 2010 PredictiveMetrics, Inc. 23 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0 10 20 30 40 50 60 70 80 90 100 Percentile %FutureBadCapture Net30Score GS1 Random Greatest Risk Least Risk Net30Score: KS = 0.47; PI = 57.32 Net30Score vs. GS1 Comparative Analysis Results GS1: KS = 0.24; PI = 30.2 In the First Decile, Net30Score Captured 50.3% of the future BAD’s Compared to GS1’s 26.5%, an Improvement of 89.8%
24.
© 2008 PredictiveMetrics,
Inc. Confidential & Proprietary 24 © 2010 PredictiveMetrics, Inc. 24 In the First Decile, Net30Score Captured 47.8% of the future BAD’s Compared to GS2’s 22.7%, an Improvement of 110.6% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0 10 20 30 40 50 60 70 80 90 100 Percentile %FutureBadCapture Net30Score GS2 Random Greatest Risk Least Risk Net30Score: KS = 0.46; PI = 55.64 Net30Score vs. GS2 Comparative Analysis Results GS2: KS = 0.31; PI = 37.92
25.
© 2008 PredictiveMetrics,
Inc. Confidential & Proprietary 25 © 2010 PredictiveMetrics, Inc. 25 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0 10 20 30 40 50 60 70 80 90 100 Percentile %FutureBadCapture Net30Score GS3 Random Greatest Risk Least Risk Net30Score: KS = 0.46; PI = 55.64 Net30Score vs. GS3 Comparative Analysis Results GS3: KS = 0.21; PI = 24.16 In the First Decile, Net30Score Captured 47.7% of the future BAD’s compared to GS3’s 16.0%, an Improvement of 198.1%
26.
© 2008 PredictiveMetrics,
Inc. Confidential & Proprietary 26 © 2010 PredictiveMetrics, Inc. 26 In the First Decile, Net30Score Captured 50.7% of the future BAD’s Compared to CRBS’s 12.7%, an Improvement of 299.2% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0 10 20 30 40 50 60 70 80 90 100 Percentile %FutureBadCapture Net30Score CRBS Random Greatest Risk Least Risk Net30Score: KS = 0.55; PI = 67.08 Net30Score vs. CRBS Comparative Analysis Results CRBS: KS = 0.06; PI = 8.56
27.
© 2008 PredictiveMetrics,
Inc. Confidential & Proprietary 27 © 2010 PredictiveMetrics, Inc. 27 Net30Score And ScoreMiner (Screen Shots In Appendix) The following reports are available: Analysis of Risk Groups (scored accounts only) Analysis of Risk Groups (all accounts) Analysis of Dollars at Risk (DAR) Comparative Portfolio Analysis of Dollars at Risk Aging by Risk Group Watch List PMI’s ScoreMiner web-based data mining, report and query system maximizes the benefits of Net30Score
28.
© 2008 PredictiveMetrics,
Inc. Confidential & Proprietary 28 © 2010 PredictiveMetrics, Inc. 28 Net30Score And ScoreMiner Click on Extreme to drill down to the detail for all Extreme Risk Accounts
29.
© 2008 PredictiveMetrics,
Inc. Confidential & Proprietary 29 © 2010 PredictiveMetrics, Inc. 29 Then, click on the customer’s account number to drill down to the 12 month Trend Report Net30Score And ScoreMiner
30.
© 2008 PredictiveMetrics,
Inc. Confidential & Proprietary 30 © 2010 PredictiveMetrics, Inc. 30 Company Trend Report with 12 months of data and ARC’s Net30Score And ScoreMiner
31.
© 2008 PredictiveMetrics,
Inc. Confidential & Proprietary 31 © 2010 PredictiveMetrics, Inc. 31 Filter & Query Capabilities Net30Score And ScoreMiner
32.
© 2008 PredictiveMetrics,
Inc. Confidential & Proprietary 32 © 2010 PredictiveMetrics, Inc. 32 Watch List Summary shows movement between risk groups from month to month Net30Score And ScoreMiner
33.
© 2008 PredictiveMetrics,
Inc. Confidential & Proprietary 33 © 2010 PredictiveMetrics, Inc. 33 CFO Analyze how your business’s A/R risk is changing over time Produce reports for Sarbanes-Oxley Audits and loss revenue forecasting Understand the dollars at risk impacting your bottom line and reducing losses Determine how every analyst, collector, or sales personnel’s accounts are doing relative to their peers and risk level Credit and Collections Management Monitor the effectiveness of collection strategies for the entire portfolio, important segments, or individual accounts Filtering and reporting capabilities to review and analyze information by segmenting accounts Know the number of accounts within each risk group Create custom watch list based upon your query ScoreMiner is a web-hosted report and query system has many features that benefit all levels of the business ScoreMiner Benefits For Senior Management And Credit And Collections Professionals
34.
© 2008 PredictiveMetrics,
Inc. Confidential & Proprietary 34 © 2010 PredictiveMetrics, Inc. 34 Net30Score Revalidation Of Existing Models Model is re-validated every 12-18 months after implementation. PMI uses the score, the date the score was produced and monthly performance data to re-validate the model’s performance. PMI will suggest either a refit or re-estimate of the model when there is greater than a 15% point reduction in performance, based upon PMI’s Predictiveness Index (PI).* Ensures Net30Score is being maintained and if not then your Net30Score will be updated to reflect more recent data. Continually monitor the predictiveness of Net30Score through annual re-validation of Net30Score *The Predictiveness Index is an algebraic calculation of predictiveness based upon the area under the Lorenz Curve compared to a perfect predictor.
35.
© 2008 PredictiveMetrics,
Inc. Confidential & Proprietary 35 © 2010 PredictiveMetrics, Inc. 35 Process Automation Improved throughput time by automating the order approval process for Low Risk customers Increase partnering with sales – help grow revenue with the right customers Reduce credit/sales negative interaction – help each other Shorten your order-to-cash cycle – deploy collection strategies from day of sale Drive exception-based activity Analysts only work on the higher risk accounts Increased volume of accounts without increasing staff size Consistent Decisions Align with corporate objectives and policies Reduce subjectivity and tenure related issues Support policy and regulatory compliance Best Practices - Use of Net30Score In The Decision Processes
36.
© 2008 PredictiveMetrics,
Inc. Confidential & Proprietary 36 © 2010 PredictiveMetrics, Inc. 36 Using risk to drive the prioritization of collection activity Given that the vast majority of companies have a finite number of resources to be applied to collection activities, resources must be deployed effectively to ensure a collection process that is likely to generate the most cash! Risk-Based collection strategies ensures a more focused collection effort Your internal systems can drive the prioritization activity and increase productivity By using the future prediction of customer delinquency to proactively change current customer behavior will ensure that the future delinquency does not happen By having a plan that is consistently applied — collectors, sales, and customer service will all help change customer behavior without having to re-think daily activities Best Practices - Use of Net30Score In The Decision Processes
37.
© 2008 PredictiveMetrics,
Inc. Confidential & Proprietary 37 © 2010 PredictiveMetrics, Inc. 37 ROI For Net30Score 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%) 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) PMI’s clients have realized several key financial and operational benefits from implementing Net30Score
Editor's Notes
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