Tax-Data-Analytics
6 posts tagged with "Tax-Data-Analytics"
- Aug 11, 2024
👩🏻💻 The Hidden Complexity of Tax Data Analytics: 10 Lessons from the Trenches 👩🏻🏭
The analysis of tax data often yields contradictory paradoxes, requiring both numerical precision as well as analytical interpretation of tax regulations. With Pillar 2 looming, understanding best practices for tax data collection, analysis, calculation, and reporting is paramount. Whether you’re an Excel expert, an Alteryx aficionado, proficient with Python, or skilled using SQL, the following is some hard won practical tips for working with real-world tax data, from data cleaning to building machine learning tax models.
- Nov 27, 2020
Technology for Transfer Pricing (🗞published)
This article is published on Bloomberg Tax 17 Jun 2021. My tax technology journey starts with transfer pricing (“TP”) given much of the TP work involves tailored data analytics and visualisation. For technical correspondence with tax authorities, I also provided econometric based statistical analyses. For multinational companies, TP is a highly visible area of tax with governments agreeing to exchange data and hence many potential technology applications. It is therefore desirable to establish a modern data-driven TP management in-house with a systematic overarching design.
- Oct 17, 2020
Peeking into the Giants: Analysing the ADIMA Database (🗞published)
This blog was first published on medium.com and Analytics Vidhya News Bytes This blog examines the Analytical Database of Individual Multinational Enterprises (MNEs) and their Affiliates (ADIMA) dataset from OECD to understand the structure and presence of the top 100 MNEs of our time across the world. Background Visibility of information on Multinational Enterprises (MNEs) has been limited at best despite their significant and growing importance in our daily lives as well as public policy area.
- Mar 10, 2020
Machine Learning Governance in Tax
Many areas of tax incorporated machine learning, but it is not a silver bullet. What are the potential risks of adopting a machine learning approach to replace manual efforts, and how could we mitigate such risks? This blog post discusses some governance measures I put in place to address these questions. Common applications The most frequent machine learning application I came across in tax falls into the category of classification prediction modelling.
- Oct 9, 2018
Data Visualisation in Tax
Tax and finance teams use an excessive amount of dashboards. The contents are often limited to simple bar charts and histograms. However, tax data is multi-dimensional which means the simple two-dimensional representations may no longer be sufficient. I believe modern data visualisation in tax should increasingly serve the users with more depth in that it could allow users to: interact with data, explore and tell a story from the data, and/or; directly enable an action point from the visualisation I explain this thinking with an example in transfer pricing below.
- Dec 12, 2017
CBCR Analysis
Background OECD’s BEPS project Action 13 provides a template for multinational enterprises (“MNEs”) to report annually and for each tax jurisdiction in which they do business the information set out therein. This report is called the Country-by-Country Report (“CBCR”). The CBCR template requires two tables of MNEs’ financial and operational information. Exploratory data analysis In the first year of submitting CBCR, I wanted to use Python instead of Excel to see how one may get more insights from the data collected in CBCR.