LEVERAGING BIG DATA & ANALYTICS TO IMPROVE EFFICIENCY. Bill Franks Chief Analytics Officer Teradata July 2013

Transcription

1 LEVERAGING BIG DATA & ANALYTICS TO IMPROVE EFFICIENCY Bill Franks Chief Analytics Officer Teradata July 2013

2 Agenda Defining The Problem Defining The Opportunity Analytics For Compliance Analytics For Efficiency Wrap-Up / Q & A 2 Confidential Do Not Distribute Without Permission

3 Closing The Tax Gap Is Crucial Tax Gap: The difference between owed and voluntarily remitted tax obligations The IRS estimates that at the Federal level, the tax gap is 15% to 17% Since most states start with the Federal AGI, this tax gap only grows larger in total 3 Confidential Do Not Distribute Without Permission

4 It Isn t Just About Compliance!! Improving efficiency and effectiveness can be just as important as increasing compliance How much money and effort is spent trying to make outdated and insufficient systems and processes work? Focus on spending more efficiently as well as collecting more revenue 4 Confidential Do Not Distribute Without Permission

5 Fraud Is Very Easy & Widespread Today Electronic filing, which was introduced to speed up delivery of refunds, has made the system more vulnerable to fraud The IRS is seeking to speed up the loading of data from W-2 payroll forms issued at the beginning of the tax season, a time lapse which gives fraudsters a window of opportunity to file using false data "We will not be prosecuting our way out of this We're going to have to make it more and more difficult for criminals to profit from this behavior," said Miller 5 Confidential Do Not Distribute Without Permission

6 The Unintended Consequences Of Being Helpful The thieves know that the IRS does not verify the employer W-2s sent with the return until after the refund is issued. Police Chief Larry Gomer said the speedy returns and the option of having your refund issued on a debit card are making it easier for criminals to pull off the fraud 6 Confidential Do Not Distribute Without Permission

7 Even Prisoners Are In On The Action TIGTA has previously suggested that the IRS wait to issue refunds until it receives thirdparty confirmation like W2s TIGTA said the IRS could also stop more fraud by improving the accuracy of its Prisoner File. The inspector general found that about 500,000 of these files, or 18%, are potentially inaccurate or missing information 7 Confidential Do Not Distribute Without Permission

8 This Isn t Just An Issue For The United States 8 Confidential Do Not Distribute Without Permission

9 Analytics Can Help With All Compliance Issues Purposeful Fraud The intent is different, but the revenue impact can be the same! Innocent Mistake Clueless & Uneducated On Requirements 9 Confidential Do Not Distribute Without Permission

10 Agenda Defining The Problem Defining The Opportunity Analytics For Compliance Analytics For Efficiency Wrap-Up / Q & A 10 Confidential Do Not Distribute Without Permission

11 Study What Private Industry Has Done Private companies have already addressed many of your issues at a deep level Financial institutions have highly sophisticated fraud processes built off of a wide range of data and tools A variety of industries have leveraged data & analytics to improve their customer service and operational efficiency Tax authorities don t need to start from scratch, they just need to borrow from others 11 Confidential Do Not Distribute Without Permission

12 Modernize Several Areas To Increase Collections Increase & Promote Compliance > Discover non-compliant taxpayers & enhance voluntary compliance > Improve agency-wide audit selection Improve Quality of Service > Reduce intrusion and burden for compliant citizens > Decrease telephone calls and follow-up > Improve business processes Optimize Workforce > Automate manual processes > Drive accuracy, efficiency and consistency > Improve access to vital information Drive Informed Business Decisions > Data driven decision making based on consistent, reliable data > Measurable reports to evaluate and modify business processes Documented & Integrated Tax Discovery Logic > Easy to defend in prosecutions > Easy to adjust and improve as behaviors change 12 Confidential Do Not Distribute Without Permission

13 The Impacts Of Modernizing Are Substantial Over $2 Billion Collected In Just 8 States! State #1 State #2 State #3 State #4 State #5 State #6 State #7 Audit resources are being used more effectively, while at the same time improving customer service. Revenue from the first discovery program was recognized in less than 70 days from the start of the program. Value added data now available to auditors and examiners for each lead, enabling them to work smarter, faster, more efficiently, & less intrusively. Generated a return on investment of 1,140% early on their project and has increased monthly revenue. Resulted in additional revenue collections to the state of over $275 million to date. Produced revenue exceeding $250 million, with thousands of leads representing multiple millions of dollars still being processed. Productivity at the agency increased with the automation of the lead generation process. State #8 Improved productivity by at least 37%. Collecting more taxes both in Sales and Franchise taxes with approximately the same number of employees. 13 Confidential Do Not Distribute Without Permission

14 Success Depends On Several Factors The availability of data for right-time analysis, not just the capture and storage of data An understanding of inefficiencies and compliance issues first, followed by automating ways to address the issues Leveraging analytics to provide prescriptive actions for right now and not simply identify historical issues 14 Confidential Do Not Distribute Without Permission

15 Agenda Defining The Problem Defining The Opportunity Analytics For Compliance Analytics For Efficiency Wrap-Up / Q & A 15 Confidential Do Not Distribute Without Permission

16 Challenges in Addressing Fraud Timeliness: Time to detection determines cost of correction Changing: Fraud continues to adapt and evolve Technology: New means of fraud enabled every day Scale: Analytics at scale across many data sources & types Consumer Concerns 42% of survey respondents believe they have been victims of online fraud 57% of respondents do not believe online companies are taking enough precautions to protect them against online fraud Ponemon Institute Consumers Reaction to Online Fraud April Confidential Do Not Distribute Without Permission

17 Performing Rich Analytics to Detect Fraud Identify suspect data Identify suspect relationships Web Logs {user, page, time} Click 1 Click 2 Click 3 Click 4 Identify suspect patterns Insurance Claims Transactions Stock Tick Data {user, payee, date} Returns Claim 1 Claim 2 Claim 3 {user, product, time} Purchase 1Purchase 2Purchase 3Purchase 4 {stock, price, user, time} Claim 4 Trade 1 Trade 2 Trade 3 Trade 4 {customer, SKU, price, date} Return 1 Return 2 Return 3Return 4 All at massive scale across multiple data sources & types 17 Confidential Do Not Distribute Without Permission

18 Start With The Simplest Analytics Make critical data available for analysis at the time a decision needs to be made Very simple fact checking can catch a lot of fraud > Does income match W2 reporting? > Does the address match W2, 1099, and other reporting? > Is income vastly different than in past years? > Has the refund payment method changed? > Has the same address been on many other returns? Much of the fraud happening today would not happen if simple checks like these were in place 18 Confidential Do Not Distribute Without Permission

19 Getting Started: Define The Relevant Metrics You must start by combining data to create metrics that will feed your analysis processes Potential metrics that show intent to defraud (or a mistake) include: > Third party financial information that conflicts with what is reported > Lifestyle beyond reported income means > Ratio of reported withholding of various taxes doesn t match expected > Tax preparer in common with other fraudulent taxpayers > Vastly different income distribution than in previous years > and many, many more 19 Confidential Do Not Distribute Without Permission

20 Basic Fraud Approaches Profile each metric to understand its characteristics. Without knowing what is normal, you can t flag things that aren t normal > Take into account special situations, sub-populations, or other factors Start with some simple thresholds, but be sure to define them right > Absolute rules - more than 5 red flags per return is cause for alarm > Ranking - we ll only worry about those returns with the highest 10% of flags > Change ranking - we care when a visible change in pattern occurs Univariate methods can find some low hanging fruit. However, it is only the starting point. You have to go deeper. 20 Confidential Do Not Distribute Without Permission

21 Going Beyond The Basics Examination of multiple metrics using composite scores adds more power > Score = Weight1*Metric1Score + Weight2*Metric2Score WeightN*MetricNScore There are many ways to define basic composite scores > Flagging each metric as 1 = ok, 0 = bad and then totaling the flags > Flagging each metric on a scale of 0 N and then totaling the flags > Ranking on each metric and taking the average rank > Applying uneven weights to your metrics in any of the above One approach won t fit all problems. You have to experiment 21 Confidential Do Not Distribute Without Permission

22 Statistical Modeling, Data Mining, & Machine Learning The most powerful method is to use statistical models to assess fraud risk > To build a predictive model, you need to identify some historical known cases of fraud > Clustering can also be used to find cases with similar characteristics. This won t predict fraud, but can identify unusual groupings of cases Various modeling options exist C3 T r a n s a c ti o n s C2 C Login Time Cluster analysis can help find cases that have similar profiles Response modeling can provide rankings on overall fraud risk Decision trees can help identify drivers of fraud and high risk cases 22 Confidential Do Not Distribute Without Permission

23 Adding Big Data: Social Media Analysis What do people say publicly? Are they living a lifestyle that exceeds their means and bragging about their new boat? Do they have any associations with known fraudsters? This data should likely be used to help an investigation rather than to target one 23 Confidential Do Not Distribute Without Permission

24 Adding Big Data: Social Network Analysis Link tax service businesses, individual preparer services, return addresses, and other address or relationship information Identify complex networks of relationships Fraud identified by connections & patterns within the graph relationships 24 Confidential Do Not Distribute Without Permission

25 Adding Big Data: Unstructured Text Analysis Explore the explanations taxpayers provide for their override of standards Analyze responses to inquiries sent to the taxpayer as part of an investigation My lender provided an incorrect value of my interest payments this year Information Extracted: Who? Bill Franks When? 12:00 on 7/7/2013 Mentioned lender documents? Yes = 1 Exact verbiage seen repeatedly? Yes = 1 Mentioned refinance? No = 0 25 Confidential Do Not Distribute Without Permission

26 Agenda Defining The Problem Defining The Opportunity Analytics For Compliance Analytics For Efficiency Wrap-Up / Q & A 26 Confidential Do Not Distribute Without Permission

27 Optimize Your Websites Tools today allow deep analysis of web browsing patterns This can help you identify information that is difficult for taxpayers to find Tune the website so you get less calls! Taxpayer satisfaction will increase alongside the cost savings Clickstream Led to a document download Clickstream Led to a phone call $ Taxpayer site navigation. Desired documents found, but only after 12+ steps! 27 Confidential Do Not Distribute Without Permission

28 Expedite Your Audit Review Process Direct your limited resources where they are most needed and where return is highest Use predictive models to identify the most likely fraud candidates Also use models to help assess whether responses are sufficient 28 Confidential Do Not Distribute Without Permission

29 Monitor Activity In Near Real Time Have analytic processes looking for anomalies continuously For example, did a large number of returns from a single apartment complex all get filed on the same day? Identify IP addresses that are not associated with a known preparer, but that are suddenly filing lots of returns 29 Confidential Do Not Distribute Without Permission

30 Use The Latest Customer Service Analytics Enhance call routing and agent workflow Use predictive routing to identify what a taxpayer is likely calling about Identify common paths that service agents take through internal sites, streamline, and standardize them 30 Confidential Do Not Distribute Without Permission

31 Conclusions Tax authorities are far behind in the effort to apply big data and analytics to their processes It all starts with the fundamental availability of data and the appropriate access to that data Don t just think about catching fraud and taxpayer errors. Also consider improved service and operational efficiency We will all be better off if tax authorities can be more efficient. We ll collect more and have to spend less! 31 Confidential Do Not Distribute Without Permission

32 Contact Information If you have further questions or comments: Bill Franks 32 Confidential Do Not Distribute Without Permission