Leveraging Advanced Analytics to Improve Tax Collection Performance

1. Leveraging Advanced Analytics to Improve Tax Collection Performance September 19, 2011

2. Agenda • Introduction to Opera Solutions • The Opera Collections Diagnostic • The Delaware Recent Case Study • Opera Collector Workstation Demo • Opera Collections Insight Cube™ Demo • Questions

3. Mission OPERA’S MISSION Drive significant, sustained profit growth by transforming raw Big Data flows into Signals and Actions through the application of Machine Intelligence and Human Insight

4. Opera’s Unique Assets + + + PEOPLE STACK DELIVERY SOLUTIONS Scientists Data Specialists Business Domain Experts Software Developers Sales & Marketing Professionals Proprietary Signals and Analytics Flexible, efficient, high-capacity, scalable Hosted management/ delivery of solutions Highly secure, flexible Ongoing insertion directly to front lines of operations Global 250– transformative Big Data solutions for Global 250 Vertical – Creation and hosted delivery of solutions for specific business/functional problems

5. Opera’s Unique Capabilities and Infrastructure Superior Machine Learning Expertise • ~160 scientists; 20+ disciplines • Adaptive learning platforms and models Analytics converted to better business performance “Lean” Delivery Infrastructure Superior Signal Identification • Signals Library • Deep expertise in variable transformation and selection • Secure, flexible • Supported by the Insight Bureau

6. Vektor™ Scalable Big Data Analytics and Signals Processing Platform Our Vektor™ platform allows for rapid implementation with low IT investment. It has the flexibility to address customers' requirements for both inbound data streams and outbound "directed actions."

7. Opera’s Suite of Solutions Vertical Solutions Creation and hosted delivery of Vektor-platform-based solutions aimed at specific industry/functional problems Premium Solutions Working with Global 250 in financial services, government, healthcare, and other selected sectors to transform their data reserves into enterprise value OperaDynamic Marketing Auto Auction Pricing Optimizer Attrition Reduction Touch Curricula OperaPerformance Accelerator Financial Advisor Performance Hospital Performance Education Performance Collections Performance Signals Hubs Data Equity Assessment/Growth Transformative Solutions OperaSpend Intelligence OperaWaste, Fraud and Abuse Financial Services Fraud Healthcare W/F/A Revenue Leakage BIQExploratory Analytics

8. Opera’s Experience Across the Collections Cycle Current Account Opening Delinquency Charge-Off Treatment Offers Credit Allocation Early Delinquency Customer Contact Resource Management External Channels Preemptive Intervention Enabling Capabilities Collector’s Desktop Collections Insight Cube™ • Acquisition/ Underwriting • Exposure Management • Early Identification • Phone Treatment • Customer and Treatment Alignment • Sales Practices Applied to Collections • Outside Agency Allocation • Initial Line Assignment • Pricing Modifications • Accelerated Handoffs • Channel Management • Treatment Optimization • Collector Productivity • Legal Channels Past Experience • InitialPricing • Proactive Rehabilitation • Rehabilitation Programs • Post • Write-offRecovery

9. More About Opera • Opera just announced that it has obtained its first-ever outside equity funding: an $84 million minority investment • This is one of the largest investments for a private Big Data predictive analytics firm to date • Silver Lake Sumeru was the lead investor, with Accel-KKR also making a significant investment • In the recent Netfix Prize competition, Opera tied for first place (based on model performance), beating out 41,000 teams from over 180 countries • Opera was recently named Private Company of the Year by the New Jersey Technology Council, the region’s premier trade organization • Opera has been designated a Minority-Owned Firm by: • The State of Virginia • The City of New York • The City of Philadelphia • The Port Authority of New York and New Jersey • The State of New Jersey • The State of New York • The State of Delaware • The State of Illinois

10. Agenda • Introduction to Opera Solutions • The Opera Collections Diagnostic • The Delaware Recent Case Study • Opera Collector Workstation Demo • Opera Collections Insight Cube™ Demo • Questions

11. Objectives and Approach An Opera Collections Diagnostic identifies and prioritizes collections enhancement opportunities ensuring near term impact, and sustainable performance lift • Identify collections enhancement opportunities with meaningful impact and sustainable performance lift OBJECTIVE • 6-8 week effort (assuming immediate availability of data) • Review of book of business, existing processes and infrastructure • Identification of collections enhancement opportunities through analytics and operational improvements • Quantification of financial impact and prioritization of the initiatives APPROACH • Prioritized list of opportunities with associated financial impact • Implementation blueprint to capture the identified opportunities • Immediate kick-off of near term initiatives DELIVERABLES

12. Collections Diagnostic Process • Opera utilizes a proven methodology to uncover and prioritize opportunities across the collections spectrum DELIVERABLES FOCUS • Detailed view of book of business • Collections processes and procedures • Current /historical performance • Book of business profile • Collections operations map • Infrastructure map I. Establish Baseline • Identify collections enhancement opportunities by • leveraging account-level data • Understanding collections environment • Assess financial impact • Collections enhancement map • Financial impact II. Identify Enhancement Opportunities • Prioritize opportunities based on: • Financial impact • Level of effort required • Develop implementation plans for each opportunity • Prioritized opportunities • Implementation blueprint III. Develop Implementation Blueprint

13. Collections Diagnostic Benefits • The Opera Collections Diagnostic is a high impact, low risk opportunity • Revenue opportunities typically fall in the range if 15% to 20% of total delinquencies for each client • Opera has identified in excess of $1 billion combined improvement opportunities • Minimal client capital and human resource investment is required • Opera often ties its compensation to results

14. Agenda • Introduction to Opera Solutions • The Opera Collections Diagnostic • The Delaware Recent Case Study • Opera Collector Workstation Demo • Opera Collections Insight Cube™ Demo • Questions

15. State of Delaware Case Study • Opera recently completed a Collections Diagnostic for the State of Delaware Department of Revenue • DOR management has graciously agreed to share the highlights of this study with today’s audience • All of us at Opera thank Delaware Director of Revenue Patrick Carter, Deputy Director of Revenue Colleen Yegla and Sharon Ferrara, Manager of the Bureau of Tax Collection for their collaboration and support during the course of this project

16. Portfolio Overview: Potential Opportunity A significant portion of the Delaware portfolio that has never made a payment - $180MM. There is a substantial opportunity to improve collections performance for this group of accounts Inventory Overview by Payment History As of May 2011, Balances and Collections in Millions Opera can help to capture an incremental10 – 15% of this group Paid 12-24 Months Last Payment >24 Months Total Balances Paid in Last 12 Months No Payment in 12 Months Never Paid

17. Account Distribution by Income Range Debtors who make more than $50K make up only 34% of the total population, but they make up over 50% of the balances and payments; isolating these debtors may increase efficiencies in the tax portfolio Account Distribution by Income Balance in $ 000,000’s and Payments in $ 000’s Average Balance Average Payment >$100 $9,256 $3,085 $50-100 $6,376 $1,794 $25-50 $3,631 $1,058 $0-25 $3,951 $779 Accounts Original Balance Total Payments

18. Balance, Collections, and Liquidation Heat Map Segmenting the portfolio by income band and number of accounts per person shows that the low income, high account taxpayers perform much worse than the high income and low account debtors Delinquent Balances Payments The lowest performing segment consists of low income individuals with multiple cases Liquidations Most attractive segment consists of high income customers with one account

19. Cumulative Payments Post-Placement by Income Band After 18 months, the marginal increase in collections from one month to the next is very small for the lower income ranges $ Collected by Income Level Collections in $MM $ Collected, in MM 6.5 6.0 5.5 >100K 5.0 4.5 4.0 50 - 100K 3.5 3.0 2.5 2.0 D. 25-50 1.5 1.0 0 - 25K 0.5 0.0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 Months Since Placement

20. Sample: Account Prioritization by Income Range Debtors who make more than $50K make up only 34% of the total population, but they make up over 50% of the balances and payments; isolating these debtors may increase efficiencies in the tax portfolio Account Distribution by Income1 Accounts in 0’s, Balance in $ 000,000 and Payments in $ 000’s >$100 $50-100 DOR can benefit from accelerated outsourcing of accounts of taxpayers earning less than $20k/year and redirecting released resources to higher-value accounts $25-50 $0-25 # of Accts Original Balance Total Payments 1 Income data from bc_case_history file and RETURN_AGI field from all accounts with AGI information

21. Potential Strategy Changes By shifting resources away from low income and low balance accounts, DOR can significantly increase the efforts against larger accounts, increasing collections in the more liquid high income segments ILLUSTRATIVE Sample Strategies Using Income as a Differentiator OCA OCA OCA Dialer OCA Work effort proportionate to % of total balances for taxpayers with income of$25+ Benefit based on hypothetical 20% improvement based on reallocation of resources and improved automation; potential savings not included

22. Opera Recommendation Engine: How It Works Opera will provide daily updates and prioritized lists of accounts to work to DOR collections management. Lists will be created based on Opera’s recommender models built using DOR data • ----- • ----- • ----- • ----- • ----- • ----- • ----- • ----- • ----- • ----- Delaware Data Sets: Delinquency Data Income Data Other Data Sources Opera Recommendation Engine Account 1 Account 2 Account 3 Skip Trace Data: 3rd party Zip + 4 Data: Opera Bureau Account 4 Account 5 Account 6 Account 7 Account 8 Account 9

23. Opportunity Areas Overall, five areas of opportunity were identified by the Collections Diagnostic Opera provided specific recommendations for each area • AREAS OFOPPORTUNITY Reporting and Analytics Collector Performance Maximizing Payments Increasing Contact Account Prioritization

24. Agenda • Introduction to Opera Solutions • The Opera Collections Diagnostic • The Delaware Recent Case Study • Opera Collector Workstation Demo • Opera Collections Insight Cube™ Demo • Questions

25. Collector Workstation powered by CRE The Collector Dashboard, powered by the Collections Recommender Engine, provides detailed account level data and specific treatment recommendations for collectors instantly, allowing them to focus on converting contacts into payments DEMO

26. MANAGER DASHBOARD: INDIVIDUAL PERFORMANCE LOCATION TENURE TEAM CCP As of November 30, 2010 PORTFOLIO Different filters allow leaders and managers to drill down and diagnose root-causes of performance issues in real-time Customer profiles Treatment usage CCP TRAINING CONTACTS PTP% CM SITUATION BALANCE RG PROBC BAND COMMENTARY CM SITUATION BALANCERG PROBC BAND COMMENTARY CCP is significantly below average in contacting hostile CM’s. Suggest different approaches to probing hostile CM’s CCP is above average in small balance ranges but under performing in large balance accounts. Recommend second voice while negotiating with all accounts greater than $25,000. Lifetime YoY 1Y 6m 3m 1m Lifetime YoY 1Y 6m 3m 1m CCP CCP AVERAGE AVERAGE CCP CCP 100 AVERAGE AVERAGE 100 90 80 80 70 60 60 50 40 40 30 20 20 10 UPSET SICK UNEMPLOYED DISPUTE 0-1 1-3 3-5 5-10 10-25 25+ PTP KEPT % AVERAGE PAYMENT SIZE CM SITUATION BALANCE RG PROBC BAND CM SITUATION BALANCE RG PROBC BAND COMMENTARY COMMENTARY CCP is significantly underperform with unemployed Cm’s. Review talk-off strategies for unemployed and hostile customers. Recommend having supervisor to assist with unemployed CM’s for 2 weeks. Lifetime YoY 1Y 6m 3m 1m CCP is strong with CM’s with low PROBC Scores but needs to work on negotiation skills with good credit CM’s. Recommend mandatory second voice for all CM’s with PROBC above 9000 Lifetime YoY 1Y 6m 3m 1m 100 400 90 80 300 70 60 50 200 40 30 100 20 10 <1000 1000-2500 2500-5000 5000-7500 7500-9000 >9000 UPSET SICK UNEMPLOYED DISPUTE

27. MANAGER DASHBOARD: TREND REVIEW LOCATION TENURE TEAM CCP As of November 30, 2010 PORTFOLIO Performance heat map Customer identification Scenario planning Performance dashboard CONTACTS PER HOUR PTP % PTP KEPT % AVERAGE PAYMENT PERFORMANCE PERIOD vs. PTP% Team Positive and negative trends are identified visually – leaders can drill down to identify root causes of the trends. Dashboards are 100% customizable to capture site, team, and individual performance trends.

28. Agenda • Introduction to Opera Solutions • The Opera Collections Diagnostic • The Delaware Recent Case Study • Opera Collector Workstation Demo • Opera Collections Insight Cube™ Demo • Questions

29. The Opera Collections Insight CubeTM • KEY CHARACTERISTICS • COMPREHENSIVE: Integrates all sources of collections data: disparate instances and systems (e.g., phone, dialer, agency and issuer data) • USER-FRIENDLY: Users access from personal desktop, using intuitive point-and-click, highly visual interface • FLEXIBLE: Features dynamic drill-down and reporting based on user’s area of interest • DETAILED: Drills down to account number level • ACTIONABLE: Enables rapid, complete identification of opportunities and specific, tactical actions INSIGHT CUBETM TECHNOLOGY

30. The Opera Collections Insight Cube™: Overview Opera built an ‘Insight Cube’ using Radian’s default records; it can hold over 1 billion individual records and can be customized to cross-examine data quickly, efficiently, and without the use of complex queries DEMO

31. Agenda • Introduction to Opera Solutions • The Opera Collections Diagnostic • The Delaware Recent Case Study • Opera Collector Workstation Demo • Opera Collections Insight Cube™ Demo • Questions

32. Questions? Contact: Julian Romeu 646-644-3621 jromeu@operasolutions.com