JurisHour had earlier examined how fake invoice rackets are digitally detected under the GST framework and how data trails, analytics, and enforcement mechanisms have significantly matured over the years. The present article revisits a commonly repeated assertion in policy and academic commentary — that the GST portal requires major improvements for real-time fraud detection. From the standpoint of officers administering GST on the ground, this argument reflects a limited appreciation of the systems already in place.
The assumption that fake ITC chains mature over three to four years due to technological gaps in GSTN is misplaced. In practice, most suspicious entities are detected early. What often delays decisive action is not the absence of digital visibility but the legal, procedural, and administrative constraints governing enforcement.
The Core Misconception About GSTN Capabilities
A recurring narrative suggests that GSTN lacks real-time analytical capability and relies largely on post-facto scrutiny. This overlooks the fact that GST enforcement today is driven by live data streams, continuous risk scoring, and automated mismatch detection. Officers do not wait for physical raids or manual audits to identify fraud; alerts and risk indicators are system-generated and continuously updated.
The GST portal’s role is not merely transactional but supervisory. The real limitation lies in how far automated actions can go without explicit legislative backing.
Registration Controls and Risk-Based KYC Are Already Embedded
Strengthening registration through Aadhaar authentication, biometric verification, and linkage analysis is often presented as a future reform. In reality, these controls were implemented nearly three years ago as part of a risk-based registration framework.
Applications are auto-scored using PAN–Aadhaar linkage, address validation, common mobile numbers, emails, IP addresses, and historical cancellation data. Where risk thresholds are breached, biometric authentication and physical verification are triggered. Officers receive clear system-generated hints and risk flags during processing.
Notably, nearly sixty percent of high-risk applications never proceed to completion, indicating that shell entities are being filtered out at the entry stage itself. Periodic e-KYC and re-verification mechanisms are also visible to officers through internal dashboards. The challenge lies not in the absence of these tools, but in the sheer scale of applications relative to available manpower.
AI-Driven Risk Scoring and Graph Analytics Are Operational
Another frequently cited recommendation is the use of AI, machine learning, and invoice network analysis to detect circular trading and missing trader fraud. These capabilities are not aspirational; they are already operational through systems such as BIFA.
GSTIN-level risk scores are continuously generated based on ITC-to-tax ratios, filing behaviour, turnover anomalies, counterparty risk, and sectoral benchmarks. Invoice graph analytics map dense trading clusters and circular chains, automatically identifying conduit entities and missing traders. In fact, graph analytics is among the most frequently used analytical tools by intelligence and enforcement formations.
Detection, therefore, is rarely the bottleneck. The more complex issue is translating risk signals into legally sustainable enforcement actions within prescribed procedures.
Integration of e-Invoice, e-Way Bill, and Return Data Is Largely Complete
There is also a perception that GST data streams operate in silos and need real-time integration. In reality, e-invoicing, e-way bills, GSTR-1, GSTR-3B, and GSTR-2B are already systemically reconciled. Mismatch reports are auto-generated and pushed to officers for action.
Rule 86A-based ITC blocking operates on system-generated risk inputs, subject to officer approval. The only significant gap is the absence of mandatory banking or UPI data integration, which cannot be introduced without explicit governmental and legislative mandate. GSTN, as a technology platform, cannot independently access financial transaction data.
It is also important to note that fully automated ITC blocking beyond prescribed thresholds is not supported by the current legal framework. GSTN can flag and recommend, but punitive or restrictive actions must be grounded in statutory authority.
Public Risk Flags and the Risk of Defamation Litigation
Proposals to display “high-risk supplier” alerts to taxpayers, enabling them to de-risk their vendor base, raise serious legal concerns. From an administrative perspective, such public risk labelling exposes GSTN and officers to defamation claims and litigation for business loss.
A system along these lines was conceptualised and even technically developed, but was ultimately shelved due to the absence of legal protection. Until the law expressly shields data-driven risk disclosures, such features remain legally untenable, regardless of their technical feasibility.
Similarly, graded interventions such as automated suspension of e-way bills or outward supplies cannot be implemented purely through system design. Without explicit statutory authorisation, such actions are vulnerable to judicial challenge.
Data Quality and Officer Adoption of Analytics Tools
Concerns regarding poor data quality in addresses, HSN classification, and bank details have been substantially addressed through standardisation and validation rules built into the portal. Data quality today is significantly higher than in the early years of GST.
What varies is the degree to which officers actively use advanced analytics tools. Platforms like BIFA and ADVAIT are available and robust, but their effectiveness ultimately depends on officer initiative, training, and administrative drive. Technology can assist, but it cannot compel enforcement.
The Real Constraint Is Legal, Not Technological
From a bureaucratic standpoint, the GST system today offers unprecedented data visibility and analytical depth. The narrative that fraud persists due to technological inadequacy misidentifies the real constraint.
The limiting factors are legal sustainability, fear of adverse judicial scrutiny, lack of statutory protection for automated enforcement, and human capacity. Until the law clearly empowers technology-led interventions and assigns responsibility for algorithm-driven actions, the GST portal will remain deliberately cautious.
The question, therefore, is not what more GSTN can technically do, but what the legislature and policy framework are willing to authorise.
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