Looking at the research out there, it’s clear that how we handle databases for internal revenue systems is pretty complicated. Academic papers, government reports, and stuff from the industry all say the same thing: a solid data setup is key to getting more taxes, making sure people follow the rules, and stopping tax cheats.
Initially, everyone focused on relational database models (RDBMS) due to their structured query language (SQL) and data accuracy (ACID properties). As data grew larger and faster, and the need to combine various types of information arose, these models began to struggle. Consequently, people started exploring other options.
Recent studies recommend NoSQL databases, particularly graph databases, to connect taxpayers, businesses, and assets, aiding fraud detection. Data warehouses and BI tools help analyze data for revenue prediction, risk assessment, and audit selection.
Machine learning (ML) is also becoming prominent in internal revenue systems. It is used for everything from automatically checking tax returns to predicting how taxpayers will behave. This helps tax agencies get ahead of the game and identify people who might not be following the rules.
A lot of research also discusses how an internal revenue system keeps data safe. It’s super important to have strong controls, encrypted data, and methods to mask people’s info so that only authorized people can access it. We need to protect taxpayer info and follow rules.
Research also emphasizes the importance of having data governance. This means having clear rules for ensuring data is good – accurate, complete, and consistent – throughout its life.
To design a good internal revenue system, consider organization operations, rules, and data security risks. The system should manage tax administration and ensure reliable tax collection and payment.
We still need to figure out the best way to integrate new innovations like blockchain and federated learning into these internal revenue systems. These could enhance transparency, security, and efficiency.