How Modern Taxation Can Benefit From Big Data Analytics

We have repeatedly discussed how predictive analytics built on Big Data capabilities can create a tremendous amount of differentiation and value for banking and insurance companies. However, one of the most important areas of public finance — taxation — is just waking up to the possibilities of using this game changing technology, with the goal of  increasing public revenues via the collection of indirect taxes. In this post, we will examine both perspectives: the taxation authority as well as the corporate under the burden of tax reporting.

National Tax Authorities Begin Information Sharing

The last few years have seen increased digitalization of national tax accounting systems. More and more citizens opt to file their returns electronically. Reporting rules for businesses (such as banks) that have wealthy account holders who have substantial financial assets spanning continents have also gone up. Different legislations have been promulgated in an effort to ensure that the exchange of information on tax matters is as seamless as it can possibly be, with the goal of curbing tax evasion.

The U.S. Government’s tax arm, the IRS (Internal Revenue Service), introduced the FATCA (Foreign Account Tax Compliance Act) in 2010 with the intention of detecting and tracking U.S. nationals that reside abroad and owe the U.S. taxes. The FATCA intends to prevent U.S. taxpayers who hold non-U.S. financial assets from avoiding taxation by ensuring that foreign banking institutions report on the U.S. account holders or face a withholding of 30% on all U.S. income.

The Organization for Economic Cooperation and Development (OECD) in conjunction with the U.S. authorities has adopted the Common Reporting Standard (CRS) as an information standard for the automatic exchange of taxpayer information. As part of CRS, more than 90 countries now share information on residents assets and incomes in conformance with the reporting standard. CRS is far wider than FATCA and imposes a significant burden on the compliance team in a bank.

As an example, both the US and the UK governments enforce compliance with CRS. This enables bilateral information sharing between both national taxation authorities. 

To that end, national and regional tax authorities have begun using Big Data techniques refined in the private sector to increase the collection of taxes from citizens while reducing fraud and waste in the system.

Across a range of national compliance regimes, Big Data can help improve both the tax collection and improve risk scoring process. It can do this by way of advanced analytics that operate on a much richer and wider set of data than was possible before. On the business side, it can help with ensuring compliance reporting and not overpaying.

Catalogued below are the important areas in which Big Data analytics can present a huge impact in the field of public taxation.

Help Taxation Authorities and Finance Departments Store and Process Data 

The point is well-made that the entire indirect taxation process is a complex process in terms of both the breadth and the depth of documentation. This is for both corporate tax departments and of the authorities. Businesses need to look at a complex range of multifaceted tax data across all of the thousands of national, state, and local jurisdictions they operate across. Not being able to store and process the data in one place induces all the challenges caused by data silos.

The data lake is a natural fit to store historical tax documents, accounts payables/receivables, expense information, business receipts, emails, call transcripts, etc. Data from various source systems that drive the tax process should be from master data, reference data (containing fresh and accurate tax rate/jurisdiction data), data from various book of record systems, ERP, finance, and legacy financial systems. The key point here is to automate this data movement and cut down manual ingestion processes thus improving both the speed and quality of the process at the first and most important step itself.

Perform Customer Due Diligence by Creating a Single View of Compliance

Given the complexity of both compliance regimes, Big Data can help automate Customer Due Diligence (CDD) and help you know your customer (KYC). This is important in helping improve both the tax collection and improve risk scoring process.

Big Data analytics can also make the CDD highly automated as opposed to a manual laborious process especially around tax avoidance watch lists or suspicious accounts. One of the first steps here is to help create a 360-degree view of entity whether a high risk individual or a corporate entity for taxation. Doing so enables better account servicing as well as provides a holistic activity of fraud. Adding a machine learning process on this can help detect micro patterns of fraud across 10s of accounts across geographies. 

KYC programs are becoming increasingly daunting undertakings due to issues such as difficulty in identifying customers across multiple lines of business, and lack of a consistent view of customer bank product use and transaction activity. Further complicating these challenges is the advent of new risks such as digital currencies, new and unique payment methods, and continued variation in global data privacy regulation — all of which are resulting in enhanced regulatory scrutiny of banks readiness in this area. 

Predicting Tax Yields (or Liabilities) More Accurately

Leveraging the ingestion and predictive capabilities of a Big Data based platform, tax authorities can create a full picture of an individual or entity across all accounts or geographies. Internal Bank compliance teams can do the same with their client accounts. This can be used to predict better tax yield and compliance numbers.

Improve Tax Fraud and Evasion by Improving Risk Scoring and Detection

The banking industry has already begun leveraging sophisticated fraud detection strategies using socio-demographic data and taxpayers behavior. Big Data can be used to look at attributes per individual that were previously missed out before even for internal structured data. Adding a machine learning process on this can help detect micro patterns of fraud across 10s of accounts across geographies. 

Big Data based analytics also operate at scale thus eliminating manual and cumbersome spreadsheet based analysis — which bedevils the ability to quickly and visually detect tax fraud and evasion.

Improve Auditability and Accuracy of Regulatory Reporting

Most businesses currently use ERP systems and engines within those to help with their taxation process. An example is to calculate VAT (Value-Added Tax) obligations using the same. These traditional tax engines and tools suffer from all the issues that plagued traditional tax data storage — operating on limited data sets which may or may not be accurate, manual processes and reconciliations lead to audits more regularly. Big Data with its focus on data quality and overall governance can help remedy these issues. It can help improve the quality, timeliness and overall confidence in the reporting thus leading to lower number of audits.

Conclusion

Opening the door to the latest data storage and processing techniques can help taxation authorities introduce a higher degree of automation into their core business functions. This will allow them to reduce manual data operations, avoid costly reconciliation  reporting discrepancies – thus reducing costly audits. This will enable them to focus on tasks such as strategic forecasting and better tax planning.