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

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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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

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 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

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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%FutureBadCapture
Net30Score
Random
KS = 0.58; PI = 73.08
Net30Score Validation Results

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

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55%
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65%
Future90+Days
Delinquency
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Percentile
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 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

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.

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%)
 42,333 Accounts Had a Valid CBCS
 42,079 Accounts Had a Valid CBBS
 Customer Judgmental Rule-Based Score (CRBS)
 68,030 Accounts Had a Valid CRBS
Net30Score vs.
Bureau Scores

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%FutureBadCapture
Net30Score
GS1
Random
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%

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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%FutureBadCapture
Net30Score
GS2
Random
Net30Score: KS = 0.46; PI = 55.64
Net30Score vs. GS2
GS2: KS = 0.31; PI = 37.92

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%FutureBadCapture
Net30Score
GS3
Random
Net30Score: KS = 0.46; PI = 55.64
Net30Score vs. GS3
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%

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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%FutureBadCapture
Net30Score
CRBS
Random
Net30Score: KS = 0.55; PI = 67.08
Net30Score vs. CRBS
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 on
Extreme to drill
down to the
detail for all
Extreme Risk
Accounts

Then, click on
the customer’s
account number
to drill down to
the 12 month
Trend Report

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

Filter & Query
Capabilities

Watch List
Summary
shows 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

Net30 Score Sample Validation Analysis

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