1. Tinton Falls, NJ Charlotte, NC www.predictivemetrics.com 732-530-9303

2. About PredictiveMetrics (PMI) Turning Probabilities Into Profits

• Established to provide higher quality analytics and predictive decision scoring in a customer focused environment.

• Analytical staff is comprised of Ph.D. and master level econometricians and statisticians.

• Experts in developing and implementing statistical-based predictive decision systems for collections, recovery and debt buying/selling.

• Have proprietary software and state-of-the art hardware in a SAS 70 II environment that is designed specifically for production of sophisticated analytical applications.

• Innovators of utilizing accounts receivable and placement data, which is proven to be the most predictive data and its FREE.

• Successfully pioneered and continuously develop more efficient and effective predictive analytical decision solutions.

Celebrating 14 years of predictive scoring excellence

3. Manufacturers Integrity Portfolio Funding LP Investments Wholesale / Dist Debt Buyers Collection Agencies Utilities Transportation Financial Services Medical MOUNT CARMEL HEALTH SYSTEMS Southern Credit Recovery PMIs Customers - A Tribute to Our Success

4. Benefits of Using PredictiveMetrics A Consultative Approach: Your business expertise combined with leveraging information, technology, and statistics, PredictiveMetrics provides you with superior predictive decision solutions. Success Drivers Data and Modeling Expertise We know statistics and how to apply data from any source, i.e., your data, bureau data, etc. providing you with the most predictive, cost-productive solutions. Cutting Edge Technology We utilize advanced hardware and software designed for the most complex analytics. Innovators of more effective statistical techniques. On Going Customer Service We are there before, during, and after the analytical process begins. Your models are well maintained and predictiveness kept intact through on-going validation. Just Ask Our Customers They are reducing costs, better utilizing resources, accurately targeting accounts based on risk or liquidation, and maximizing profits. Consumer Collection Customers Achieving substantially higher collections by knowing payers and dollars to be collected or for portfolio management knowing who will likely go delinquent. Financial Services, B2B Trade, Utilities, Leasing Customers Reducing write-offs and delinquencies. Streamlining operations. Developing strategies to control risk. Proven Results

5. Focus in Predictive Analytics for Collections A large portion of PredictiveMetrics solutions are for collections

• Data Sources

• A/R Data

• Placement Data

• Collection Data

• Socio-Economic Data

• Demographic Data

• D&B

• Experian Commercial

• InfoUSA

• PayNet

• Experian Consumer

• Equifax

• Trans Union

• Financial Statement

6. PMIs Consumer Scoring Products

• DebtBuyerScore SM - Quickly determines the price to bid on a bad debt portfolio maximizing profits

• CardCollectionScore SM - Identifies dollars and payers for collecting credit and charge card bad debt

• CollectionStrategyScore SM Identifies the probability that a customer will go severely delinquent or to loss for more cost-effective collection prioritization

• LegalCollectionScore SM Suits are costly! Knowing who will pay and how much are crucial for cost-effective decisions

• MedicalCollectionScore SM - Liquidate more medical debt by knowing who will pay and how much

• PriorityScore for Collections SM - Suite of collection scoring models designed for late-stage and post charge-off collections that is co-branded with Experian and blends your account-level data and Experian's credit data

• UltraCollectionScore SM - Optimizes collections by identifying payers and dollars

• UtilityScore SM for Collections - Work the right accounts by knowing which accounts are likely pay and which are likely to go severely delinquent or to write-off

Specializing in providing industry/finance specific models

7. PMIs Commercial Scoring Products

• Net30Score SM - B2B portfolio management tool that effectively streamlines credit and collection decisions; Multiple versions to best fit your needs

• ScoreMiner SM - A web-based credit/collection scoring and data mining application that leverages the predictive power of our Net30Score, UtilityScore, LeaseRiskScore and custom portfolio model output

• UtilityScore SM for Collections - Work the right accounts by knowing which accounts are likely pay and which are likely to go 90+ days delinquent or to write-off

• AccessLink SM SAP NetWeaver Process Integration - Helps customers easily extract the necessary data to take advantage of utilizing PredictiveMetrics' industry- and finance-specific and custom behavior scores for pro-active decisioning.

• LeaseRiskScore SM - Collection prioritization model for B2B leasing companies

• Lease Decision Scores - Suite of decision tools based on lease performance data from several major leasing companies combined with Experian commercial and/or consumer data

Specializing in providing industry/finance specific models

8. There are numerous benefits of using internal performance data Greater predictiveness Higher hit rate Segmenting accounts Receive two scores Leverages the predictive power of your FREE, easily accessible A/R or placement data = Yields higher returns Uses clients A/R or placement data allowing creditors and collectors to score thin or no file accounts = Decision more accounts Receive a segmented score based on risk or type of debt, and age of debt = Target the right accounts Receive traditional payer score and a unique dollar score for bad debt = More profitable collection prioritization Advantages of Utilizing A/R and Placement Data

9.

• All of PMIs models are empirically derived multivariate statistical models

• No bureau data or personally identifiable information is required to produce accurate scores - leverages internal A/R or placement data, which is proven to be the most predictive in this type of model and its FREE

• Several reports are available: Pre Charge-Off - Dollars at Risk; Trend; Watch List, Distribution of Accounts by Aging and Risk Class Post Charge-Off - Segmentation for Payers and Dollars; Summary of the state of the portfolio of submitted accounts; demographic breakdown; Quality of data reporting; Cost benefit and expected profitability

• No IT resources required to implement, file transfer through secure encrypted FTP in SAS 70 II environment

• Score all accounts to determine overall risk or collectability and liquidation on an account and portfolio basis

• Clients include, but are not limited to: Several divisions of GE Capital, Florida Power & Light, 3M, Edward Don, Wright Express, Mount Carmel Health, Southwest Credit, Penn Credit, National Enterprise, Capio, Blue Tarp, and Boulder Credit

Focus in Predictive Analytics for Collections

10. Case Study: Pre Charge-Off Collection Scoring Recession Period

• Prior to working with PMI, customer was using internally developed judgmental based scoring model that was more than 9 years old and never validated

• • Based on 3 data variables: Pay weight Length of service Age of arrears

• • Variables and weights were subjectively determined for the scorecard

• Customer decided to implement statistical behavior collection models, developed by PredictiveMetrics and the results were:

• • A savings of $1.2 million in losses over 12 months

• • Reduction in collection costs by creating a process to allocate resources more efficiently

• • Created a segmentation of their accounts into different risk groups

• • Implemented targeted collection strategies for each risk group

• • Used scores to develop a process to forecast write-off rates

• • Monitored changes in customer behavior over time

Customer was looking to reduce their high operational collection costs and headcount as well as improve cash flow by prioritizing collections based on risk

11. Case Study: Pre Charge-Off Collection Scoring Results Recession Period Customer saved $1.2 million in write-offs in the first year compared to their competitors utilizing PMIs scoring technology and their own internal data Pre charge-off collection scoring Customer experienced substantially better results

12. Greatest $ Collected Least $ Collected Value of using internal placement data blended with socio-economic data Focus in Predictive Analytics for Post Charge-Off Collections

13. Properly segmenting accounts is important because you make your treatment decisions based on knowledge of the segment Focus in Predictive Analytics for Post Charge-Off Collections $10,243,686 $20.51 1.84% 7.51% $556,059,274 100.00% 499,529 Total: 100.00% 100.00% $99,560 $1.79 1.13% 2.08% $8,820,016 11.11% 55,483 F 99.03% 88.89% $156,528 $2.32 0.68% 3.08% $23,133,566 13.50% 67,447 D 97.50% 75.39% $253,254 $3.97 1.06% 4.64% $23,940,079 12.78% 63,821 C3 95.03% 62.61% $651,278 $7.54 1.45% 5.62% $44,988,001 17.29% 86,366 C2 88.67% 45.33% $1,218,156 $12.64 1.39% 6.91% $87,868,863 19.29% 96,343 C1 76.78% 26.04% $2,472,452 $30.76 1.85% 10.49% $133,532,931 16.09% 80,366 B3 52.64% 9.95% $1,055,923 $56.03 1.96% 16.19% $53,887,194 3.77% 18,846 B2 42.33% 6.18% $717,277 $72.72 1.95% 20.91% $36,867,638 1.97% 9,863 B1 35.33% 4.20% $878,713 $99.70 2.22% 25.70% $39,532,227 1.76% 8,814 A3 26.75% 2.44% $1,080,638 $155.71 2.58% 31.15% $41,825,617 1.39% 6,940 A2 16.20% 1.05% $1,659,908 $316.78 2.69% 34.96% $61,663,141 1.05% 5,240 A1 (J) (I) (H) (G) (F) (E) (D) (C) (B) (A) Payments Number Payments per Account to Balance of Payment Balance Distribution Number Rating Cumulative Payment(s) Payments Incidence Number Dollar Actual Results Account Scoring Distribution

14. Cost benefit and profitability analysis

• Portfolio was scored and segmented into 11 groups, A through F; A highest liquidation per account and F the lowest

• Profit associated with providing equal collection effort and costs are $5,248,406

• After applying a collection strategy to minimize costs on less profitable segments, segments C1 through F, and increasing costs or remaining at $10 on higher scored segments, A1 through B3 = GREATER PROFITS

Focus in Predictive Analytics for Post Charge-Off Collections

15. Statistical Collection Scoring Advantage

• Statistical-based models typically deliver 10% to 300%, or more, improvement in predicting risk or liquidity over non-scientific methods

• Segmenting accounts based on risk or liquidity allows for different collection treatments to be applied based on cost, effort and profitability

• PMI conducts a Free historical back test (retrospective analysis) on your own portfolio using actual payment performance information allowing you to see how the scores would have worked, if you had used the scores to segment, prioritize and create strategies and tactics

• Score all accounts using your internal data (even bureau no hits)

• Allows for increased collection department performance without adding staff

• Advanced reporting is provided with the scores to set up cost-effective collection strategies

• Our models bring greater efficiency and effectiveness to the collection process, substantially reducing the cost of collections and optimize resources