Abstract

The world of financial regulation and tax enforcement is changing quickly. Hence, it's essential to accurately store, process accurately, and audit data to find fraud and follow the rules. Traditional data warehousing and ETL (Extract, Transform, Load) processes don't always meet essential needs like data versioning, time-travel-based auditing, schema evolution, and real-time analytics. These problems make modern tax fraud analytics systems less effective because they depend on historical data being consistent and easy to trace. This paper examines how Delta Lake, an open-source storage layer that adds ACID (Atomicity, Consistency, Isolation, Durability) transactions to Apache Spark and big data environments, can help modernize tax analytics and close this gap. Delta Lake adds powerful features to data lakes, such as data versioning, rollback capabilities, unified batch and streaming support, and schema enforcement. These features are handy for tax fraud detection and regulatory compliance. 
This study looks into how Delta Lake can be used to make versioned and auditable datasets that are great for forensic analysis, long-term fraud investigation, and compliance reporting. The suggested framework lets tax authorities and banks keep unchangeable records of all changes to data. This makes sure that audit logs and data trails can always be checked and can't be changed. Delta Lake differs from traditional database solutions because it allows incremental data ingestion and maintains consistency through transaction logs. This lets users replay changes and recreate historical states of tax data with high fidelity.
We show a structured way to combine Delta Lake with old ETL pipelines and machine learning models that look at taxpayers' behavior that seems suspicious. The implementation includes ways to create automated workflows that take in, clean, check, and version data from different tax systems and outside sources of fraud intelligence. We test how well this architecture works by using synthetic and anonymized real-world tax data and comparing key performance indicators such as the accuracy of fraud detection, the completeness of the audit trail, and how long it takes to process data against traditional methods.
The results show that adding Delta Lake greatly improves the quality and traceability of tax data analytics, with a 30–50% increase in audit readiness and a significant drop in data reconciliation errors. It is also a powerful tool for continuous compliance monitoring because it lets you query historical snapshots and do rollback operations without slowing down the system. This paper also looks at the problems when you use this in the real world, like how to handle schema drift, data governance, and storage costs.
The results show that Delta Lake is a scalable, open, and compliant base for updating tax analytics systems. By using these technologies, governments and banks can find fraud more easily and strengthen data lineage and accountability in a tax system that is becoming more digital. The paper advocates for the broader adoption of Delta Lake within tax agencies, particularly those seeking to transition from static reporting systems to dynamic, version-aware analytics platforms capable of real-time monitoring and retrospective auditing.