Net30 Score Sample Validation Analysis

© 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

© 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™



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

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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”



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.



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



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”



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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



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.



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



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



Adverse Reason Code

Score 100 Adverse CommententaryAR001 At least one incidence of delinquency over past 3 monthsAR002 Average percent of balance delinquent past quarter is moderate to highAR003 At least one incidence of delinquency over past 6 monthsAR004 Average percent of balance delinquent past 2 quarters is relatively highAR005 Recent delinquency AR006 Average percent of balance moderate to severely delinquent over recent history is relatively highAR007 Current moderate delinquency not seen in past three monthsAR008 Incidence of moderate to severe delinquency in recent pastAR009 Incidence of moderate to severe delinquency in last 6 monthsAR010 Several incidences of moderate to severe delinquency in last 6 monthsAR011 Currently moderate or severely delinquentAR012 Currently delinquentAR013 Trend of increasing delinquencyAR014 Incidence of relatively large percent of balance moderate to severely delinquent in last 6 monthsAR015 Current percent of balance moderate to severely delinquent is relatively highAR016 Sustained level of moderate to severe delinquency over past 6 monthsAR017 Currently moderate to severely deinquent with limited delinquency over recent historyAR018 First time moderate to severely delinquent occurred in last 3 monthsAR019 A given balance is becoming more delinquent over timeAR020 Incidence of relatively large percent of balance moderate to severely delinquent in recent pastAR021 First time moderate delinquency in past 6 monthsAR022 High variance in average days outstandingAR023 Either no delinquency in past and now delinquent, or severe delinquency in past that remains at moderate levelAR024 Large month to month change in proportion of balance current to delinquent

Net30Score Adverse Reason Codes



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



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



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.



Net30Score captured 49% of all future BADs through the 10th percentile and 79.9% of all future BAD’s through the 30th percentile

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KS = 0.58; PI = 73.08

Net30Score Validation Results



Predictiveness IndexModel Screening Effectiveness

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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



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Net30Score Validation Results By Population Percentile


Average Bad Rate = 12.6%



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 CumulativeAverage Percent Accounts to BAD Percent

Number of Percent of of Accounts Pay in Rate BADAccounts in Accounts in Through >90 Days in Through

Risk Net30Score Risk Risk Risk or Not at All Risk RiskInterpretation 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



DOLLARS AT RISK (DAR) - CURRENT MONTH ALL ACCOUNTS

Cum %Number Cum % % Scored Scored

Expected of % Scored Scored A/R % A/R Accounts AccountsBAD 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.



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



Champion / Challenger Analysis

Net30Score

vs.

Bureau Generic Scoring

and Judgmental Scorecards



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



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Net30Score: KS = 0.47; PI = 57.32

Net30Score vs. GS1Comparative Analysis Results

GS1: KS = 0.24; PI = 30.2

In the First Decile, Net30Score Captured 50.3% of the future BAD’sCompared to GS1’s 26.5%, an Improvement of 89.8%



In the First Decile, Net30Score Captured 47.8% of the future BAD’s Compared to GS2’s 22.7%, an Improvement of 110.6%

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Net30Score: KS = 0.46; PI = 55.64


GS2: KS = 0.31; PI = 37.92



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Net30Score: KS = 0.46; PI = 55.64


GS3: KS = 0.21; PI = 24.16

In the First Decile, Net30Score Captured 47.7% of the futureBAD’s compared to GS3’s 16.0%, an Improvement of 198.1%



In the First Decile, Net30Score Captured 50.7% of the future BAD’s Compared to CRBS’s 12.7%, an Improvement of 299.2%

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Net30Score: KS = 0.55; PI = 67.08

Net30Score vs. CRBSComparative Analysis Results

CRBS: KS = 0.06; PI = 8.56



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



Net30Score And ScoreMiner

Click onExtreme to drilldown to thedetail for allExtreme RiskAccounts



Then, click onthe customer’saccount number to drill down tothe 12 monthTrend Report




Company Trend Report with 12 months of data and ARC’s




Filter & Query Capabilities




Watch List Summaryshows movement between risk groups from month to month




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



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.



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



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



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

Documents

Net30 Score Sample Validation Analysis