Tax Fraud Prevention using Data Analytics

Fight against Tax Fraud and Tax Evasion, Procedures for mitigating risk presented by Tax Fraud, What it means to you when monitoring Tax Transactions and Tax Compliance to Combat Fraud

Course Description

The Association of Certified Fraud Examiners estimates that fraud costs organizations worldwide $3.7 trillion a year and that a typical company loses five percent of annual revenue due to fraud. Fraud attempts are expected to even increase further in future, making fraud detection highly necessary in most industries.

This course will show how learning tax fraud patterns from historical data can be used to fight fraud. Combining theoretical and practical insights, the use of predictive analytics and descriptive analytics are discussed.

We present methodologies to solve these issues. Moreover, we present techniques from robust statistics and digit analysis to detect unusual observations that are likely to be associated with fraud.

This workshop will give you the best opportunity to be able to transfer their newly acquired knowledge and skills into the workplace.

All the models and scenarios are designed and delivered from a tax perspective ensuring that participants are able to fully relate to the material and thus increasing their understanding and knowledge retention.

The Model outlines how to make the interview simple and is developed through the following phases:

Planning and Preparation
Introduction – Engage and Explain
Initiate a First Account
Topic setting – varied and extensive retrieval of information
Challenges of inconsistencies
Closure

Course Outline

Introduction and motivation

Importance of tax fraud detection
Definition and Types of fraud
Tax Fraud detection challenges
Fraud analytics process model

Data preprocessing

Types of variables
Visual data exploration
Missing values
Standardizing and transforming data
Coarse classification

Social Networks for Fraud Detection

social networks and applications
is fraud a social phenomenon?
social network components
visualizing social networks
social network metrics
community mining
social network based inference (network classifiers and collective inference)
from unipartite toward bipartite graphs
featurizing a bigraph
fraud propagation
case study

Supervised Methods for Fraud Detection

target definition
linear regression
logistic regression
decision trees
ensemble methods: bagging, boosting, random forests
neural networks
dealing with skewed class distributions
evaluating fraud detection models

Unsupervised Methods for Fraud Detection

unsupervised learning
clustering approaches: hierarchical clustering, k-means clustering, self-organizing maps
peer group analysis
break point analysis

Fraud Analytics: Putting It All to Work

quantitative monitoring: backtesting, benchmarking
qualitative monitoring: data quality, model design, documentation, corporate governance

Why should you Attend

Tax Evasion and Tax Fraud are at the cornerstone of an effective Tax compliance. Tax Fraud Evasion program.
Effective monitoring and proper understanding of the topic are required to accurately describe the transactions on a Suspicious Activity from customers/ vendors/ taxable persons.
This workshop on Tax Fraud Investigation will address the types of transactions and give you examples of how to drill down further to assess those risks.

Target Audience?