How Can Big Data Be Used To Reduce Tax Evasion?



Be it an individual, a local company, or an MNC, the crux of any successful tax compliance and tax collection system is to ensure a sound set of information and profound insights into the journey of taxpayers. Billions of dollars around the world across tax administrations are lost each year due to frauds, evasions, non-collections, and non-compliance. Today, governments all over the globe are implementing big data for taxes. They do this with the main aim of ensuring compliance and eliminating waste. Tax evasion is a major problem which is faced by every economy in its overall business environment. Both individuals and business owners always have more than one method to complete a taxable transaction.


They conduct tax planning to evaluate the various tax options. Using this, they determine the methods to use to conduct both personal and business transactions to reduce or eliminate tax liability. Having said that, tax planning is legal whereas tax evasion is illegal. People often use the terms tax avoidance and tax evasion synonymously. Before moving on further, let’s first understand the basic definitions of tax planning, tax avoidance, and tax evasion.


Tax Planning vs. Tax Avoidance vs. Tax Evasion


Tax planning refers to the act of reducing tax liability by applying the legal scripts and morals provided by law such as rebates, exemptions, deductions, and relief. It makes use of the benefits offered by law. Tax avoidance refers to the assessee taking advantage of loopholes in the Act to reduce the tax liability to the minimum. Tax evasion is illegal as it refers to the act of deliberately suppressing income or sale by increasing expenses. It is done in the attempt of reducing tax liability by applying unfair means.


What Is Big Data And How Does It Benefit Companies?


It refers to extremely large sets of data which are analyzed to reveal trends, patterns, and associations relating to human behavior and its interactions. This may refer to both structured and unstructured data. The characteristics of big data such as velocity, variety, and volume were first identified by Doug Laney, an analyst at Gartner, the global research and advisory firm. It has the potential to be mined for information and is made use of in machine learning projects and other advanced analytics applications.


As companies generate large and disparate chunks of data on a regular basis, companies are now hiring big data analysts to analyze their data and provide opinions on ways to use that data to benefit their businesses. Companies use big data to improve overall operations, increase profits, create personalized marketing campaigns, and improve customer service.


Some of the ways in which big data helps in reducing tax evasions are:


  • Using The Services Of Data Analytics Providers To Check For Linkages In Cash Deposits

This will focus on grouping data based on linkages or relationships. In the case of firms, it will focus on employees, company directors, main employers, and addresses of all its offices. In the case of individuals, the grouping will be based on email ids, common addresses, mobile numbers, joint ownership of assets, investments, and bank accounts. It will also have information from the existing databases of the tax department. In case of any fraud, these will help in immediate identification.


  • Implementation Of Big Data And Advanced Analytics Platforms

This will help in integrating and exploiting the various data sources to help tax departments efficiently discharge their responsibilities and bridge the tax gap. This helps build integrated views of the tax filers and individual tax submissions and also helps use resources optimally by responding in a targeted manner. It empowers respective departments in confronting tax malpractices such as tax evasion, transfer pricing manipulations, circular trading, hawala dealings, etc. With malpractices constantly evolving, the big data analytics platform can help identify data patterns and unearth complex business relationships. This is made possible by running algorithms and models to cross-reference purchases submitted by dealers against the corresponding information of sales to understand the nature, quality, and volume of data. These advanced analytics approaches help in reducing false positives, empowering authorities to run scenario models, and provide ultra-early detection. A few progressive government departments have understood the benefits of big data for tax evasion and have commenced their journey in this path and have started to reap benefits.


  • Hybrid Approach Fraud Detection Model

It uses typical tax evasion determinants to assess the perpetrator’s characteristics and is based on a different reliability level factor integration. The creation of the tax evasion model is based on the business rules, anomaly characteristics, social analysis, and fraud monitoring models of the individual and the environment.


  • Business Intelligence And Tax Evasion Simulation Model


This helps online mediums to recreate conditions where tax evasion processes take place and mitigate all the information onto a single platform. This helps analyze transactions and customer activity and allows for data validation and verification, developing new fraud models, and tuning the existing ones to create reports and improve fraud detection efficiency.




Overall, tax evasion is a socio-economic problem in all societies of the world. The effectiveness and value of big data depend greatly on the skills and expertise of the analysts who are tasked with understanding the data and formulating proper queries to aid in directing big data analytics projects. Organizations are now waking up to the benefits that big data offers to their businesses like the ability to work faster and stay agile gives businesses the perfect competitive edge which they did not have before. Big data helps create smarter businesses by harnessing data in the right manner and extracting value from it. The global business scene is becoming more efficient and faster, thanks to big data and big data analysts.