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1. Risk Profiling Presentation STRATEGIC MANAGEMENT OFFICE (SMO) Tax Data Management Department (TDMD ) Prepared by the Risk Profiling Team

2. Agenda Objectives, Team Formation, etc. RISK PROFILING OVERVIEW. K Means, Scorecard,, Machine Learning,, feature selection, etc WORKFLOW SO FAR . Data, Stakeholder buy in, industry benchmarks, staff strength e CHALLENGES $ SOLUTIONS Discussions, Recommendations, etc. NEXT STEPS

3. Risk Profiling & Analysis- Overview

4. What is Risk Based Auditing Why Risk Based Audit? Audit resources are finite so its important to identify areas of highest risk and adopt resources to deal with them accordingly, through a range of data driven interventions. Directs resources to areas of high risk Sector approach identifies sectors most at risk of non-compliance – sector approach Adopting ways of dealing with large taxpayers – size/turnover approach Identifying government and organizational priorities – Strategic approach Tax type approach How does it work? Data driven interventions might include Segmentation by Trade Segmentation by size Strategic Tax Type, etc Risk based selection direct audit resources to risks. 2. Identifying risks before audit makes proactive use of auditors time . What does it do? RISK 3. Proactive use of time increases yield 4. Higher audit yield produces better revenue 5. Better revenue help drive economic growth.

5. Tax Audit Process Content Here Content Here Content Here Pre-Audit Activities Case Selection based on risk profiling, referral from management, etc Approvals from directors Assignment of cases. Creation of working papers Communication with taxpayer Team review Field Audit Activities .1. Meeting with Taxpayer 2. Request for documents 3. Review of documents 4. Exit or close out meeting 5. Forming an audit opinion Post Field Audit Activities Generation of reports Review of consolidated reports Communication of findings to taxpayer

6. Traditional Vs Modern Approach with Pre-Audit Activities Manual Review and Analysis Use of Emerging Technologies Before Moist cases came from manual risk profiling, review and analysis of taxpayer data. Cases were also generated based on referral from Management and other triggers. After With the advancement in big data technologies, AI and machine learning, tax administrations all over the world are now leveraging these technologies to help automate the pre audit activities in order to save time and other resources.

7. Objectives of Risk Profiling Team Risk Engine Development EDA and Analysis Extract, Clean explore and analyze all taxpayer data using key metrics such as annual bank transactions, liquidity and solvency ratios, efficiency/ activity ratios, profitability ratios and tax adequacy ratios . - Segmentation Taxpayers would be grouped based on their sectors to compare the above ratios against industry standards and benchmarks. Soring and Ranking The use of machine learning algorithms to score and rank taxpayers based on selected metrics. Feedback The Risk Engine would be equipped with appropriate feedback mechanism for monitoring and evaluation purposes. Automation Automate process for updates as well as a data pipeline for new taxpayers Risk Engine and Database Creation of a risk profiling database for the risk engine which stores the profiles

8. Risk Profiling and Analysis- Workflow

9. Workflow so far Use of Machine Learning Algorithms Use of Score-Card algorithms Creation of Risk Profiling Database Communication with Audit We used the K Means algorithm to first segment and profile our taxpayers. Management was able to locate high risk taxpayers with a total of 2 to 4 trillion in estimated turnover with no record of tax paid for the period in review. . One of the short comings of the K means was interpretability. We now applied the scorecard algorithms for allocating scores on selected features. For 25k taxpayers. We also had the problem of assigning weights to the features as well We have been able to create a database for the risk profiling engine. This will be populated with the risk profile of all taxpayers which will be automated and updated. We have recently been in communication with audit to strengthen out knowledge on the risk profiling process done so far.

10. Risk Profiling and Analysis- Challenges

11. Challenges Risk Team Challenges Clean Data Stakeholder buy -in Industry data Staff Strength Challenges with audit Challenges with Tax promax 1. Un-available data on industry benchmarks Industry Data Team Motivation 2. No team members 3. Trainings. Staff Strength Bank transactions data Incomplete taxpayer info . Data from tax pro Other integrations Clean Data Stakeholder Buy In

12. Risk Profiling and Analysis- Discussion

13. THANK YOU Insert the Subtitle of Your Presentation