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Analytics and Risk

Examples from Research & Analytics Branch

Duncan Cleary dcleary@revenue.ie

http://www.linkedin.com/in/duncancleary

Research & Analytics Branch

DATA - INFORMATION - KNOWLEDGE

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Revenue’s Business Context

‘To serve the community by fairly and

efficiently collecting taxes and duties and

implementing Customs controls.’

www.revenue.ie

Total Receipts €31.5 Billion (2010, Net)

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Research & Analytics Branch

Conduct analyses to transform data into information primarily using SAS software.

Evidence based projects, predictive analytics, segmentation, forecasting etc. using data from Revenue and other sources.

Enables Revenue make better use of its data and provides an improved understanding of the taxpayer population.

The results are used to better target services to customers and to improve compliance.

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Target

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Rules

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…not a duck

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8

Not a duck…

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+

+ =

Rules combined are better…

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But where are the ducks?

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Case Study: Use of Predictive Analytics in Revenue

Revenue’s Risk system; uses ~300 business rules to quantify risk.

Goal: use predictive analytics to extend from risk to predicting likelihood of yield, if audited.

Pilot model and if successful bring to production.

Show how analytics can assist development of

effective business strategies for Revenue.

Optimise use of Revenue resources.

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Data and Variables:

Considerable effort at Data Integration stage. (use SAS DI Studio, scalable, semi auto).

Data Quality! Risk system data is opportunistic.

Business Context and understanding.

Rules that fire/ don’t fire, binary and frequency.

Derived variables, such as monetary risk and behaviour scores created by risk system.

Target variables: Audit Outcomes (e.g. yield).

Demographic variables, Geography, Sector etc.

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Help for finding ducks

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SAS Credit Scoring Module

Banking Analogy: Likelihood of a case defaulting on a loan, based on their profile and the profiles of cases who have defaulted in the past.

Credit Scoring techniques applied in this model where the likelihood of a case to yield, based on their profile and the profiles of cases who have yielded in the past.

Model creates a scorecard and probability of yield for the cases base.

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Training the assistant…

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Results SAS Credit Scoring Module in SAS Enterprise Miner.

Target: Any yield over €2500= ‘1’, < €2500 = ‘0’

Cut off point e.g. p= 0.65: misclassification of 23% (77% hit rate).

Number of cases: can continue to select until quota is filled, based on decreasing probability to yield. Scorecard can be used to assess cases.

0200400600800

100012001400160018002000

0.95

- 1.0

0

0.85

- 0.9

0

0.75

- 0.8

0

0.65

- 0.7

0

0.55

- 0.6

0

0.45

- 0.5

0

0.35

- 0.4

0

0.25

- 0.3

0

0.15

- 0.2

0

0.05

- 0.1

0

Yielding Cases

Non Yielding Cases

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Scorecard Extract

All cases are asssigned a score

based on their profile as per the

model. Cut offs can be set to

increase likelihood

The less points that a case

scores, the more likely it is to

yield if audited.

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…?

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Unseen data scored, i.e. cases that have not been audited in period

List of cases with scores based on propensity to yield according to model.

Cut off set high (e.g. 0.70 probability).

Extending the Model: Reduce Misclassification

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3:1

Hits vs. Misses in Pilot Region

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€

€

€

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An auditor in the field…

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So what next? ‘Operationalise’ this approach

Developing more models for the business (e.g. Yield, Sectoral, Regional, Liquidation, ‘Phoenix’ Directors, Real Time Risk, etc.)

To evaluate models through field testing, in co-operation with Revenue Regions

Extract more value from the data & info we already have, better training data.

To make analytics more central to how Revenue performs its work

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Other Work Audit Yield Models 2 stage: monetary risk as a target, cost of audit

Liquidation Models ‘Balloon’ Payments

‘Phoenix’ Directors/ Group Risk

Model Evaluation

Real Time or Look Back Prevent and Detect, Customs, VAT, Excise

Customer Segmentation

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Two Stage Model: Test

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Two Stage Model: Test cont’d

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Liquidation Model Assessment: Probability Distributions

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SNA: Social Network Analysis

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2 Directors with many companies in common

One director red according to REAP

Other director and most companies green

Methods of propagating content(risk) through a network

Linked to Associated Companies

Directors

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Identifying Risky Cases

Cases flagged by predictive model: Cases flagged by algorithm:

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Customer Segmentation for Risk

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Questions?

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Dr. Duncan Cleary

Revenue, Planning Division, Research & Analytics Branch

t: 00353-(0)1-4251414| e: mailto:dcleary@revenue.ie

http://ie.linkedin.com/in/duncancleary

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