How data science and big data analytics leads to better tax fraud prevention

Organisations have always had trouble when it comes to tax collection. Fortunately, data science and big data analytics can help.

Tax evasion is a huge cost to the Australian government. In 2018, ABC revealed that evasion and fudgers cost the Federal government over $8.7 billion in a single year, prompting the question: Is there a way to tackle fraud to cut losses? However, it is not just tax evasion that is causing the problem. Tax collection practices are laden with problems that make it difficult for corporations and individuals alike to follow the tax code. Fortunately, there is a solution in the form of data science and big data analytics.

Improve tax collection practices

Tax collection refers to the methods authorities use to complete different transactions, like collecting information. Here are a few ways data science and big data analytics streamlines these processes.

Faster data collection and procession

The tax code is a complex beast – one that requires a lot of data to process. However, while businesses and individuals have a hard time delivering the required information, state organisations have a hard time collecting and processing the large volume of information coming in. It significantly slows down the rate taxes are processed and dues are distributed. The slow data collection and processing time is a red tape issue, one of the largest problems state organisations have. However, data science and big data analytics can speed up data collection and processing significantly, which leads to a more efficient tax collection process.

Begin sharing information across different departments

Data science and big data analytics breaks down information silos and encourages data sharing across different organisations. Taxes are often overseen by different departments. For example, the state and national governments have their own codes to follow and little information is shared between the two segments. However, data analytics encourages data sharing because analytics benefits from a large pool of data. Sharing data leads to several benefits for state organisations like faster processing, less waste and a better chance of exposing fraudulent activities.

Data science and big data analytics prevent tax fraud

Data science and big data analytics are the perfect solutions to preventing tax fraud. Here are a few reasons why.

You can differentiate between a legitimate taxpayer and fraudster

One of the biggest problems state organisations face is distinguishing between well-meaning taxpayers and those who try to game the system to either underpay their taxes or exaggerate their income to get a larger rebate. Data science and big data analytics address this problem using data classification, clustering and trail-based pattern recognition to organise taxpayer data based on certain attributes making it easier to separate and distinguish between fraudsters and genuine payers. Data analytics can even be used to track activities in real-time.

Use different sources for analysis

Tax collection entails different variables ranging from income level to job status. Data science and big data analytics are excellent in leveraging both structured and unstructured data. The use of so many different variables leads to comprehensive analysis that allows state organisations to get in-depth insight, and gain a deeper understanding of the situation.

For example, incorporating future GDP projections allows the state to anticipate how much tax revenue they should earn in a time period. The state can then compare projections to what they actually earned to determine how much is lost from tax fraud. Other data sources to inform analysis include deadlines for application forms, declaring business losses, changed residences and so much more.

They scale down information

Tax fraud occurs because there is so much information to process and state organisations have a hard time processing this information in a timely manner, allowing fraudsters to take advantage of loopholes for their own benefit. However, with data science and big data analytics state organisations can scale down information by fusing social relationships. Using analytics, a tax fraud system can reduce the number of suspects and the doubtful transactions associated with them to make fraud detection easier than before.

Reduce fraud with analytics

Banks and many financial institutions are using sophisticated data analytics programs to detect and catch fraud in real-time. Hence, it makes sense for state organisations to take similar measures to reduce the incidents of tax fraud. Tax fraud prevention is complex because both corporations and individuals use loopholes to reduce the amount they pay in taxes or increase the amount they get back in refunds.

However, data science and big data analytics remove these complexities – making it easier to prevent fraud and protect the tax system.


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