India’s tax administration is increasingly shifting from conventional, return-by-return scrutiny towards a data-driven model in which artificial intelligence, advanced analytics and information available across multiple government systems are used to identify potential tax risks.
The emerging approach is enabling tax authorities to examine a taxpayer’s activities across different tax and reporting platforms rather than relying solely on the information contained in an individual return. Data generated through Income Tax Returns, GST returns, e-invoices, e-way bills, registrations and past compliance records can potentially be analysed together to identify inconsistencies, unusual transaction patterns and other indicators of tax risk.
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Tax officials and tax professionals indicate that the objective is not merely to increase scrutiny, but to make departmental intervention more targeted by identifying taxpayers or transactions that warrant closer examination.
Tax Administration Moves from Return-Based Scrutiny to Data-Based Risk Assessment
Traditionally, tax scrutiny has largely revolved around the information disclosed by a taxpayer in a particular return or assessment proceeding.
The growing availability of digitised tax information is changing that model.
Tax authorities now have access to substantially wider datasets, including transaction information, tax returns, registration details, invoice-level information and historical compliance records. When these datasets are analysed collectively, inconsistencies that may not be apparent from a single return can become visible.
For example, turnover disclosed under GST may be compared with information available in the income-tax system. Similarly, outward supplies reported in GST returns can potentially be examined against e-invoices and e-way bills, while input tax credit claims can be evaluated against supplier-related information and the recipient’s historical compliance profile.
According to Jigar Doshi, Country Head – Indirect Tax, Ascentium India, the department’s scrutiny and audit methodology is increasingly extending beyond information furnished in an individual return.
The ability to run data-driven simulations across multiple information sources allows tax authorities to identify anomalies and potential risk indicators before deciding where further examination may be warranted.
Annual Information Statement Provides a Significant Data Pool
The Income Tax Department already has a substantial information ecosystem through mechanisms such as the Annual Information Statement (AIS).
AIS provides taxpayers with information relating to financial transactions and other income-related information available with the tax authorities. The information is sourced from multiple reporting entities and systems and provides a broader picture of a taxpayer’s financial activities.
The increasing use of analytics and AI could allow such information to be examined alongside other government datasets.
Instead of looking at a tax return in isolation, technology can potentially help identify patterns over several years, compare reported transactions with information available from other sources and flag unusual deviations.
This could include situations where reported income does not appear consistent with other financial indicators, where transaction patterns change substantially from previous years or where information reported under different statutory frameworks does not align.
GST Ecosystem Generates Invoice-Level Information
The GST system provides an even larger pool of transactional information because businesses increasingly report data through multiple digital mechanisms.
GST returns such as GSTR-1, GSTR-3B and GSTR-2B, together with e-invoices and e-way bills, create multiple points at which a transaction can be examined.
This enables automated systems to potentially identify discrepancies involving:
- outward supplies and reported turnover;
- input tax credit claims;
- differences between GSTR-1 and GSTR-3B;
- ITC appearing in GSTR-2B vis-à-vis credits claimed;
- e-invoices and e-way bills;
- transactions involving cancelled or suspicious GST registrations;
- unusual refund claims;
- historical changes in taxpayer behaviour; and
- inconsistencies across different tax periods.
The consequence is that a business may no longer be assessed solely on the figures appearing in one GST return.
Instead, the consistency of the taxpayer’s overall digital footprint can become an important factor in determining whether a transaction or taxpayer warrants further examination.
DGRAM and BIFA Strengthen GST Risk Identification
GST administration has also increasingly adopted centralised analytical systems for identifying potentially suspicious registrations, transactions and credit chains.
Platforms such as DGRAM are used in the GST ecosystem for analytical purposes, including identifying patterns associated with potentially fake registrations and suspicious input tax credit networks.
Similarly, Business Intelligence and Fraud Analytics (BIFA) functions as a risk and exception-reporting mechanism drawing upon the broader GST database.
Such systems can assist tax authorities in moving away from purely random or sample-based verification towards risk-based selection.
The significance of this development is particularly relevant in cases involving interconnected businesses. A transaction that appears routine when viewed from the perspective of one taxpayer could potentially acquire a different risk profile when examined alongside the registration status, return-filing behaviour and transaction history of other entities in the supply chain.
State Tax Departments Also Building Data Analytics Capabilities
The use of data analytics is not confined to central tax administration.
State GST administrations are also developing technology-driven systems to analyse taxpayer data and identify cases requiring scrutiny or audit.
Maharashtra’s Business Intelligence & Data Warehouse (BIDW) is one example of a state-level initiative that combines a data warehouse with an analytics layer for identifying potential cases and exceptions.
The broader trend indicates that tax administration is becoming increasingly dependent on centralised data and analytical tools at both the Central and State levels.
As these systems mature, the ability to correlate information across jurisdictions and tax periods could become increasingly important in GST enforcement.
Historical Taxpayer Behaviour May Become an Important Risk Indicator
One of the major changes brought by data analytics is the ability to examine taxpayer behaviour over time.
Instead of merely asking whether a return contains an error, analytical systems can potentially assess whether the taxpayer’s current behaviour represents an unusual departure from its historical pattern.
For instance, a sudden increase in turnover, a substantial change in ITC utilisation, an unusual refund claim or significant variations in the ratio between purchases and sales could potentially trigger a risk indicator.
Similarly, repeated discrepancies across tax periods may carry greater significance than an isolated reporting error.
This means that maintaining consistent and accurate records over time could become increasingly important for businesses.
GST Audit Could Involve Multiple Layers of Reconciliation
Tax professionals point out that businesses are now required to look beyond traditional book-to-return reconciliation.
A GST audit or departmental verification may potentially involve reconciliation among several datasets, including:
Books of account → GSTR-1 → GSTR-3B → GSTR-2B → e-invoices → e-way bills → GST registration data → supplier compliance history.
Differences between these datasets can create questions for taxpayers even where the underlying transaction is genuine.
For example, ITC may attract attention where the supplier’s GST registration was subsequently cancelled, where the supplier has failed to discharge tax or where the transaction pattern differs materially from the broader behaviour of the parties involved.
This does not automatically establish that a transaction is fraudulent or that ITC is inadmissible. However, it can increase the likelihood of departmental verification and require the taxpayer to substantiate the transaction with appropriate documentary evidence.
Income Tax and GST Data Could Provide a Wider Financial Picture
The increasing interaction between tax databases could eventually give authorities a much broader view of a taxpayer’s financial activities.
Information available under GST, income tax and customs systems can provide different perspectives on the same business.
For an importer, for example, customs-related information may provide an additional reference point for examining purchases, inventory and subsequent sales. For a manufacturer or trader, GST turnover, e-invoice data and income-tax disclosures may collectively provide indicators of the scale and nature of business operations.
The more datasets that become digitally accessible and capable of being analysed together, the greater the possibility of identifying discrepancies that would otherwise remain difficult to detect through manual verification.
Scale of GST Data Makes Automated Analysis Increasingly Important
The sheer size of the GST ecosystem makes extensive manual scrutiny impractical.
India had more than 1.65 crore GST taxpayers as of May 2026, generating a vast volume of invoices, returns and transaction-related information.
Analysing such a large dataset manually would be extremely difficult.
Technology therefore offers tax administrations the ability to process large volumes of information, identify patterns and narrow down cases that may require human intervention.
This is where AI and machine-learning-based analytical tools could increasingly play a role.
Rather than replacing tax officers, these technologies can function as a first-level risk identification mechanism, helping officers determine where their limited enforcement and audit resources should be deployed.
Businesses May Need to Adopt ‘Pre-Emptive Compliance’
The changing enforcement environment could have a significant impact on businesses and tax professionals.
Historically, compliance often focused on filing returns correctly and responding when a notice was received.
The increasing use of analytics makes a more proactive approach necessary.
Businesses may need to periodically conduct their own reconciliations across GST returns, books, e-invoices, e-way bills, income-tax data and other relevant records.
Potential discrepancies should ideally be identified and investigated internally before they appear as risk indicators in departmental systems.
This could make pre-emptive tax-risk management an increasingly important component of indirect and direct tax compliance.
AI Cannot Replace Human Decision-Making
Despite the increasing use of technology, AI-based risk identification does not by itself establish a tax violation.
An automated system may identify a transaction as unusual or assign a taxpayer a higher risk profile, but the underlying facts still need to be examined.
A discrepancy may have a legitimate commercial, accounting or legal explanation. Similarly, differences between datasets can arise because of timing differences, amendments, credit-note adjustments, cancellations, technical issues or differences in reporting requirements.
Therefore, an AI-generated alert should be regarded as an indicator requiring verification rather than conclusive evidence of tax evasion.
As one tax official emphasised, AI is an enabler rather than a substitute for human judgment.
The final tax position, assessment or enforcement action would continue to require examination of facts, applicable law and supporting evidence by the competent authority.
Taxpayers Must Prepare for Greater Data Transparency
The increasing digitisation of taxation is effectively reducing the possibility of treating each tax return as a standalone document.
A taxpayer’s GST returns, income-tax disclosures, invoices, registrations, transaction history and other digitally reported information can increasingly form part of a connected compliance ecosystem.
For businesses, this means that the focus of compliance may need to shift from simply filing returns within prescribed timelines to ensuring consistency across every reporting platform.
The emerging model is therefore one where tax administration is increasingly data-led, risk-based and technology-assisted, while taxpayers are expected to maintain records capable of explaining apparent inconsistencies whenever they arise.
As AI and analytics capabilities expand, the tax department’s ability to detect unusual patterns is also likely to improve. For businesses, the practical message is clear: tax compliance is no longer only about what is reported in a return; it is increasingly about whether the entire digital trail of the business tells the same story.
