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Maximising The Value of Analytics in Tax Compliance
 

Maximising The Value of Analytics in Tax Compliance

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An exclusive presentation by Mr. Imam Hoque, General Manager, Advanced Analytics BU, Advanced Analytics Sales - EMEA AP BU, SAS Software Ltd (United Kingdom)on ‘Maximising The Value of Analytics in ...

An exclusive presentation by Mr. Imam Hoque, General Manager, Advanced Analytics BU, Advanced Analytics Sales - EMEA AP BU, SAS Software Ltd (United Kingdom)on ‘Maximising The Value of Analytics in Tax Compliance’ The presentation was made at Government Analytics & Information Summit 2013.

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    Maximising The Value of Analytics in Tax Compliance Maximising The Value of Analytics in Tax Compliance Presentation Transcript

    • Copyr ight © 2013, SAS Institute Inc. All rights reser ved. MAXIMISING THE VALUE OF ANALYTICS IN TAX COMPLIANCE IMAM HOQUE
    • Copyr ight © 2013, SAS Institute Inc. All rights reser ved. ENTERPRISE ANALYTICS ENTERPRISE ANALYTICS ENVIRONMENT FOR TAX Intelligence Repository Data Sources Direct Tax Other Gov Sources 3rd Party Data VAT Analytics Environment Customs VAT Carousel Tobacco / oils / contraband Under- declaration Transfer Pricing Offshore Evasion Property Tax Evasion Social Insurance Healthcare Fraud and Evasion Detection / Prevention / Management Customer Contact Electronic VAT Reconciliation Taxpayer segmentation Inspection Effectiveness Taxpayer Sentiment Cost Optimisation Risk Management Forecasting Optimisation and Efficiency Enterprise environment to support an extensive range of business applications
    • Copyr ight © 2013, SAS Institute Inc. All rights reser ved. TAX ANALYTICS ADOPTION OF BEST PRACTICE CONCEPT OF OPERATIONS Tax Business Processes Core system or 3rd party data feeds Advanced analytics Social network analytics Data ingest, link and enhance Rules Anomaly detection Scoring MI & Analytics Special InvestigationRescore Events People Organisations Alert / Case Management Events
    • Copyr ight © 2013, SAS Institute Inc. All rights reser ved. TAX ANALYTICS HOW TO GET THE MOST OUT OF ANALYTICS Data Tools and techniques Working models for analysts Models Operationalise Adoption Continuous improvement Which business problems?
    • Copyr ight © 2013, SAS Institute Inc. All rights reser ved. TAX ANALYTICS COMMON OBJECTIVES SEEN BY TAX AUTHORITIES INCREASE DETECTION RATES • Which businesses / people to inspect? • Identify more sources of non-compliance • Ensure fewer large cases go undetected ACCURACY • Reduce false positives – don’t waste time • Focus on cases with higher yield EFFICIENCY • Work cases and inspections faster • Optimise customer contact strategy TOTAL COST OF OWNERSHIP • Avoid high risk expensive custom developed systems A recognition that the problem is continuously changing Move to develop “self- sufficient” analytics operations in house
    • Copyr ight © 2013, SAS Institute Inc. All rights reser ved. TAX ANALYTICS WHERE IS THE TAX GAP? (HMRC UK EXAMPLE) Customer contact strategy Debt collection Detect and Inspect Customer contact strategy
    • Copyr ight © 2013, SAS Institute Inc. All rights reser ved. TAX ANALYTICS WHAT ARE YOUR KEY TAX GAP CONSTITUENTS? Tax gap Offshore Spreading salaries Undeclared property Small business hidden earnings Businesses never registered or don’t file Bonuses as expenses ?
    • Copyr ight © 2013, SAS Institute Inc. All rights reser ved. TAX ANALYTICS APPROACH TAKEN BY LEADING TAX AUTHORITIES Data Sources Live and batch input data Tax systems Other Gov Department 3rd party public data Financial institutions Out of jurisdiction Data Integration / Entity Resolution / Networked Data Personal Small Business Corporate Analytics / Detection and Alerting Prioritized alerts which cases to work Hybrid Analytics Model End User Services Reporting / explore & search data Case Management
    • Copyr ight © 2013, SAS Institute Inc. All rights reser ved. TAX ANALYTICS DATA INGEST AND INTELLIGENCE GENERATION Data ingest DI / DQ SNA Content Categoriser Extract entities from text Single view of entities Link entities & records Create discrete networks Data integration tools allow data sources to be rapidly integrated and data quality to be measured and addressed Single views of entities are produced through advanced data matching techniques Where free text exists within documents or in record fields, it is processed. Entities such as: people, places, businesses, addresses, phone numbers, account numbers, etc. are extracted automatically as entities. All entities are exhaustively linked together and large networks are generated in the data Filtering algorithms are used to identify the discrete socially bounded networks, such as fraud rings or crime gangs. Statistics regarding these networks are added A comprehensive intelligence warehouse of all claim and policy history and relationships
    • Copyr ight © 2013, SAS Institute Inc. All rights reser ved. TAX ANALYTICS A HYBRID MODEL IS REQUIRED TO DETECT TAX GAPS Social Network Analysis Database Fuzzy Matches Text Mining Predictive Modeling Anomaly Detection Automated Business Rules PROJECTS ARE SIGNIFICANT SO ENSURE THE BEST MODELS ARE USED Analytic Decisioning Engine
    • Copyr ight © 2013, SAS Institute Inc. All rights reser ved. TAX ANALYTICS CASES FOR INSPECTION AUTOMATICALLY GENERATED Inspector’s work queue High risk cases Low risk risk cases
    • Copyr ight © 2013, SAS Institute Inc. All rights reser ved. TAX ANALYTICS MODELS SCORE ON 3 LEVELS Event Entity Network • Tax return • Customer contact • Change in circumstance • Person or business • Change in behaviour • Entire history • Entity outlier analysis • 3rd party data • All entities in a business • Linked with known risk • Network variables
    • Copyr ight © 2013, SAS Institute Inc. All rights reser ved. TAX ANALYTICS BETTER TO PROVIDE REASONS NOT JUST A SCORE Risk of income under declaration Income inconsistent with property post code Income not progressing in line with inflation Land registry indicates 4 properties No rental income declared Accountant links this person to others with undeclared rental income 65% Risk of income under declaration
    • Copyr ight © 2013, SAS Institute Inc. All rights reser ved. TAX ANALYTICS CASE WITH REASONS AUTOMATICALLY GENERATED Data provided to support investigation Reasons why the case was created automatically populated Basic case details provided in one place
    • Copyr ight © 2013, SAS Institute Inc. All rights reser ved. TAX ANALYTICS SNA NETWORK OF THE UNDERLYING DATA AND RISKS Diagrams for all cases are automatically generated by the system
    • Copyr ight © 2013, SAS Institute Inc. All rights reser ved. TAX ANALYTICS SNA HAS DELIVERED THE LARGEST STEP CHANGE Yield POPULATION █ ANALYTIC DECISIONING ENGINE WITH SNA █ ANALYTIC DECISIONING ENGINE WITHOUT SNA █ RANDOM I.e. if you examine 50% of the population, you would expect to find 50% of the fraud If the accuracy of a model doubles through the inclusion of network level variables alone, this means an investigation team is able to find twice the amount of fraud with the same number of referrals!
    • Copyr ight © 2013, SAS Institute Inc. All rights reser ved. TAX ANALYTICS STRUCTURED APPROACH TO ASSESSING THE PROBLEM What you know you know What you know you don’t know What you don’t know you don’t know Existing debt Projections from known case typologies Few or no examples, projections difficult Optimise debt collection and customer contact Encode known MO’s as models and execute across data, sample alerts and project Outlier analysis. Data exploration. Learn from other Tax authorities. Learn from other sectors. Encode, sample and project
    • Copyr ight © 2013, SAS Institute Inc. All rights reser ved. TAX ANALYTICS MODEL TUNING AND ACTION PRIORITIZATION? Non-compliance Alerts • Multiple models for different non-compliance typologies • Generate alerts for potential non compliance Segment by propensity to treatment types • Post • Call centre • Inspection Yield • Optimise for likelihood to recover • Optimise for fiscal value Deterrent impact • Geographic • Typology • Key influencers
    • Copyr ight © 2013, SAS Institute Inc. All rights reser ved. TAX ANALYTICS OTHER EXAMPLES: DEBT COLLECTION OPTIMISATION HMRC – +20% resource savings, immediate cash flow benefits, call centre improvements Swedish Tax Office – 30%-75% resources savings across different channels Philippines Bureau of Internal Revenue – 10% on collections • Segment Characteristics • Control-based Selections • Recommended Information • Recommended Channel • Scheduled Execution
    • Copyr ight © 2013, SAS Institute Inc. All rights reser ved. TAX ANALYTICS SUCCESSFUL ADOPTION APPROACH • Establish super user community • Involve them in strategy, plans and model designInvolve users • Start simple, let initiatives bed in • Ramp up sophistication as a programme of workIt’s a journey • Don’t leave users guessing “Why?” • Where possible get the most out of transparent toolsAvoid black box • Ensure you pick challenges where results are easy • Simulate first, limit roll-out until provenFocus on results • Give users the data they need, make them efficient • Think carefully about delivery mechanismsEnable users • Always gauge reaction and enhance • Structured plan for roll-outs, training, etc.Pilot and iterate
    • Copyr ight © 2013, SAS Institute Inc. All rights reser ved. TAX ANALYTICS MAKE USERS MORE EFFECTIVE Fewer false positives: - Avoid wasted time Case file - Rapidly understand case Data exploration - Quick and easy first pass investigation Initial contact Work Case Evidence file Rapid triage Dropped Cases – occur earlier in the cycles with less work Yield based on weight of evidence alone Yield Maximum effort required Model % % % % % % Percentages determine efficiency % %
    • Copyr ight © 2013, SAS Institute Inc. All rights reser ved. TAX ANALYTICS A JOURNEY TO TAX COLLECTION EXCELLENCE Capability Yr1 Yr2 Yr3
    • Copyr ight © 2013, SAS Institute Inc. All rights reser ved. FPS FINANCE (BELGIUM) - VAT CAROUSELS Business Problem VAT Carousel fraud was a billion euro problem for the Belgian Government. This fraud typology is a high velocity fraud: “Carousel frauds are like floods. It is futile to believe that after the storm we can put water back in the riverbed with buckets. What is needed is to build dykes to prevent overflows”. The Tax department is overloaded with data relevant for VAT Carousel Fraud (600.000 tax-payers, 5.000.000 VAT returns, 24.000.000 Intra-community transactions). SAS Approach The solution is used to detect companies with a high probability of being involved in VAT Carousels. The SAS Hybrid approach provides ultra-early detection as from the first suspect VAT return or other suspicious behaviour. The models are highly accurate (80% true positive rate). This makes the SAS Hybrid approach the ideal tax auditor. Results The result is a reduction by 98% (from 1.1 billion € to 0.029 billion €) of the VAT Carousel fraud. VAT Carousels are now a controlled phenomenon. Highlights • VAT Carousel fraud reduced by 98% (from 1.1 billion € to 0.029 billion €) • Ultra-early detection • SAS hybrid approach provides a high accuracy model (80% true positive rate)
    • Copyr ight © 2013, SAS Institute Inc. All rights reser ved. Missing Trader1 VAT IN=0 VAT OUT=21 Due VAT=21 No VAT is paid by the supplier on an intracom delivery. The VAT is due in the destination country. The Missing trader disappears without paying the due VAT Profit Taker VAT IN=21 VAT OUT=0 Due VAT=-21 No VAT is due by the Profit Taker as the goods are shipped out of the country via an intracom delivery. The VAT “paid” by the profit taker (21) is claimed back from the Tax agency. Value of goods: 100 VAT: 21% Societe Buffer VAT IN=21 VAT OUT=21 Due VAT=0 The buffer company only exists to hide the Missing Traders from the Profit Taker Belgium France TAX ANALYTICS VAT CAROUSEL FRAUD
    • Copyr ight © 2013, SAS Institute Inc. All rights reser ved. sas.com QUESTIONS? IMAM.HOQUE@SAS.COM