Technology, Tax Compliance & the Digital Future of Revenue Enforcement
The newly appointed Principal Chief Commissioner of Income Tax for Andhra Pradesh and Telangana, Suresh Battini brings an unusual profile to one of the most consequential revenue positions in the country. An engineer by academic formation, an IRS officer by vocation, and a technology pioneer by practice — he has spent over three decades using software, data analytics, and digital forensics to transform how tax enforcement works in India. In this exclusive interview with Naresh Nunna of Neo Science Hub, he speaks about the science behind modern tax administration: from the computerisation of TDS returns in the 1990s, to Faceless Assessment, to the role of AI and Big Data in the landmark November 2025 raids on Hyderabad’s hospitality sector — an operation that demonstrated, conclusively, that in India’s integrated digital economy, evasion is no longer a sustainable strategy.
Contents Inside
SECTION I — THE ENGINEER IN THE OFFICER
You are an engineer by academic background who joined the Indian Revenue Service. How has that engineering mindset shaped your approach to tax administration over three decades?
Engineering trains you to look at systems — to understand inputs, outputs, processes, and failure points. When I joined the field in 1994 and was posted to a corporate circle in Bengaluru, I noticed immediately that the biggest bottleneck in tax administration was not intent or manpower — it was the absence of efficient systems. Tax calculations were being done manually, returns were paper-based, and the possibility of errors and delays was enormous. So my first instinct was the same as any engineer’s: identify the inefficiency and build a solution. I personally developed software to automate tax calculation and return processing. That was not part of my job description. It was a problem-solving reflex.
That was the mid-1990s — a time when computerisation of government systems was virtually unheard of. What happened next?
By 1996, I moved to the TDS wing, and there I found an even larger opportunity. TDS return processing was entirely manual, prone to errors, and slow to yield compliance feedback to deductors. I led what I believe was the first-ever computerisation of TDS returns in the country. We developed software and distributed it to nearly 4,000 deductors — entities responsible for deducting tax at source on salaries, contractor payments, and various financial transactions. They filed returns electronically. We processed those returns, identified defects, and communicated corrections immediately. The result was remarkable: tax collections from that wing grew at nearly four times the previous rate. That experience confirmed something I have held to since — technology does not merely improve administration. It transforms it.
“Technology does not merely improve administration. It transforms it. That has been my conviction since 1996, and everything that has happened since has reinforced it.”
From an NSH perspective — a magazine focused on science and technology — the Income Tax Department is not a sector our readers typically associate with technological innovation. Would you challenge that perception?
Absolutely, and I think it is one of the most important misconceptions to correct. The Income Tax Department today is one of the most IT-integrated governance systems in India. We were early adopters of e-filing, we built the Centralised Processing Centre in Bengaluru in 2010 which processes crores of returns computationally, and we introduced the Faceless Assessment Scheme in 2020 — a system that, I say with confidence, has no parallel anywhere in the world. We use AI-driven anomaly detection, Big Data analytics, database forensics, and real-time data integration across multiple third-party platforms. This is frontier technology applied to governance. The science and technology community should be paying close attention to what is being built here.
SECTION II — FACELESS ASSESSMENT: A GLOBAL FIRST
The Faceless Assessment Scheme is often discussed in policy circles, but NSH readers would be interested in technical architecture. How does it work, and why is it significant from a systems design perspective?
At its core, Faceless Assessment solves a fundamental problem of information asymmetry and human discretion. In the traditional model, a taxpayer and an assessing officer were in the same jurisdiction, often knew each other, and interacted face to face. That created multiple opportunities for the exercise of discretionary judgment — sometimes legitimate, sometimes not. Faceless Assessment eliminates that entirely. A taxpayer in Hyderabad may have their return assessed by an officer in Pune, through a technology platform, with no geographic or personal connection between them. Neither party knows who the other is. The entire process — notices, responses, hearings, orders — happens through the digital interface. The system was made possible only by the technology available to us today: secure cloud infrastructure, digital document exchange, identity verification, and cryptographic audit trails.
Has it worked as intended? Transparency is the stated goal — has it been achieved?
Transparency has genuinely increased, and I say that not as a promotional statement but as an observable fact. When a taxpayer cannot meet an officer in person, the entire basis of the interaction shifts. Every communication is on record. Every decision has a documented rationale. Corruption requires informality — a phone call, a meeting, a personal relationship through which influence can be exercised. Faceless Assessment removes that substrate. Corruption has come down significantly as a result. There are still isolated incidents arising from other processes, and we address those through continuous monitoring. But the fundamental architecture of the scheme is sound. And I will say this: it will become a model for other countries. No other tax administration in the world has done this. India built it. That is a genuine technological achievement.
“Faceless Assessment will become a model for other countries. No other tax administration in the world has done this. India built it. That is a genuine technological achievement.”
The new Income Tax Act, 2025 — which came into force in April this year — is being described as a simplification exercise. From a technology standpoint, what does simplification mean in practice?
Simplification in law has a direct technological analogue: reducing complexity reduces the surface area for error, misinterpretation, and manipulation. The old Act had 819 sections. The new Act has 536 sections, with a dramatic reduction in total volume and a shift to plain, accessible language. For our digital systems — the e-filing platforms, the processing algorithms, the assessment modules — simpler law means cleaner code, fewer edge cases, and more reliable automated processing. The essence of the law, its substantive provisions and obligations, remains the same. What has changed is the architecture. It is the difference between legacy code written over sixty years with multiple patches and rewrites, and a clean, well-structured codebase designed with modern principles. The outcome for the taxpayer is the same: but the reliability, the accessibility, and the maintainability are fundamentally better.
SECTION III — DATA-DRIVEN ENFORCEMENT: THE SFT ECOSYSTEM
Can you explain the Statement of Financial Transactions framework — and how it represents a shift from reactive to proactive tax enforcement?
Yes, let me explain whatStatement of Financial Transactions- SFT-based pre-filling actually means in practice, because I think most people underestimate how significant a shift this represents.
Under Section 285BA of the Income Tax Act, a wide ecosystem of entities — banks, mutual funds, registrars, payment aggregators — are mandated to report high-value financial transactions directly to the department. This data flows into what we call the Annual Information Statement, the AIS, along with the Taxpayer Information Summary and Form 26AS. Together, these systems build a consolidated, third-party verified financial picture of every taxpayer. And critically — that picture is now used to auto-populate their Income Tax Return on the e-filing portal before they even sit down to file.
Think about what that means. When you sell property, make a large cash deposit, invest in mutual funds, pay a significant credit card bill, or send money abroad — we already have that information. Your return arrives pre-filled with it. The era of selective disclosure is, for practical purposes, over.
From an administrative standpoint, this transforms our posture entirely. We move from a reactive model — wait for the return, audit it, investigate discrepancies — to a proactive one, where the data precedes the declaration. It strengthens risk assessment, enables non-intrusive verification, and fundamentally changes the compliance dynamic in favour of transparency.
I will be honest about the limitations too. The system is not perfect. Incorrect reporting by third parties, duplication, timing mismatches, wrong PAN linkages — these issues arise and must be addressed. Not every income stream is captured through SFT. So taxpayer diligence remains essential: verify your pre-filled data carefully, and wherever you find a discrepancy, provide feedback through the AIS portal. The system works best as a dialogue between the taxpayer’s own records and ours — not as a substitute for personal responsibility.
But the direction is clear and irreversible. SFT-based pre-filling is not a convenience feature. It is the foundation of a transparent, data-driven, and ultimately fairer tax system.
What does this mean practically for tax evasion? Is the common assumption that large transactions can be hidden still valid?
It is not valid for large transactions, and it is becoming increasingly invalid for medium ones as well. The data coverage we have today means that for any transaction that touches the formal financial system — a bank account, a registered property, a listed investment — we will have a record of it. The only space where evasion remains possible is in purely cash transactions that never enter the banking system. And even there, SFT data creates triangulation: if someone is operating a large business but depositing no cash, that absence itself is a signal. Nil cash deposits for a high-volume establishment are not an explanation — they are a red flag. In the future, it will simply not be possible for people to evade at scale. Even if they do not pay immediately, they will have to pay subsequently.
“Nil cash deposits for a high-volume establishment are not an explanation — they are a red flag. In the future, it will simply not be possible to evade at scale.”
SECTION IV — THE HYDERABAD HOSPITALITY RAIDS: TECHNOLOGY AS BOTH WEAPON AND SHIELD
The Hyderabad hospitality sector investigation is being described as a watershed in technology-driven tax enforcement. From a purely scientific standpoint, what made this operation methodologically distinctive?
What made it distinctive was the convergence of multiple independent data streams, each of which told part of the story, and together created an evidentiary picture that was impossible to refute. We did not rely on a single source of evidence, which any competent defence counsel could challenge. Instead, we constructed a multi-layered forensic architecture: digital platform data from food delivery aggregators, SFT cash deposit data from banks, PoS terminal transaction logs, database backend forensics, UPI transaction trails, physical invoice books, and test purchase documentation. Each layer independently pointed to the same conclusion. Each layer corroborated the others. That convergence is what made the evidence unassailable.
The investigation reportedly began not with the raids themselves but with a year-long preliminary analytical phase. Can you explain that approach from a data science perspective?
Responsible investigation demands that you build your evidentiary base before you act, not after. The preliminary phase used publicly accessible platform data — transaction volumes from food delivery services — and cross-referenced them against tax filing databases. Establishments processing lakhs of rupees in monthly orders but reporting minimal income, or not filing at all, were identified algorithmically. This gave us not just targets but confidence: confidence that the discrepancies were real, systematic, and substantial enough to warrant the significant resources involved in a search operation. It also gave us specific hypotheses to test during the raids. We were not going in blind. We were going in with a data-derived map of what we expected to find — and the forensics confirmed it. That is the scientific method applied to investigation.
The investigation uncovered sophisticated manipulation of PoS database systems — bulk deletion of cash transactions, exploitation of RDBMS architecture. How does an engineering background help in understanding and countering such schemes?
Enormously. The manipulation involved a detailed understanding of how Relational Database Management Systems work — specifically, the difference between what a user interface displays and what the underlying database actually contains. The operators, or their technology vendors, understood that ACID properties — Atomicity, Consistency, Isolation, Durability — govern how transactions are recorded. They exploited the gap between frontend visibility and backend persistence. They deleted transaction entries from the interface while believing the data was gone. What they did not fully account for was that database systems retain rollback segments, undo logs, and transaction journals for system integrity purposes. These are not visible to end users. But they are accessible to forensic investigators who understand the architecture. Our teams reconstructed six years of billing patterns from data the operators believed had been permanently erased. From an engineering standpoint, the lesson is this: you cannot truly delete data from a well-designed database system. You can only move it out of sight.
“You cannot truly delete data from a well-designed database system. You can only move it out of sight. The operators learned this at considerable cost.”
The UPI exploitation was particularly sophisticated — personal staff accounts used as payment collection nodes, rotated every sixty days to defeat banking AML velocity checks. This is essentially a technical attack on India’s flagship fintech infrastructure. How do you assess it?
It is a technically sophisticated attack, yes — and it reveals a genuine architectural vulnerability in peer-to-peer payment systems. UPI was designed for consumer convenience: any two bank accounts can transact instantly. That openness is its strength and, in this case, its exploited weakness. By routing commercial revenue through personal accounts — essentially building a human mule network — the operators decoupled the payment trail from the business entity. The rotation every sixty days was a deliberate exploit of AML velocity threshold algorithms: each account was abandoned just before it would have triggered automated banking alerts. From a systems security perspective, this is a known class of attack — fragmenting a large signal into many small ones to defeat threshold-based detection. The response must be equally technical: AI-driven pattern recognition that identifies P2M behaviour on P2P accounts, regardless of individual account volumes. The NPCI has the data to build this. It is a solvable problem.
The investigation also identified a ‘hybrid database architecture’ — essentially two parallel ledgers, one clean and one suppressed. Invoice sequencing gaps revealed the deletion pattern. This sounds like forensic accounting at the level of computer science. Is that a faircharacterisation?
It is precisely fair. The investigators were not merely checking books — they were doing database forensics. The invoice sequencing technique is deceptively simple but tremendously powerful: invoices are generated sequentially, and gaps in that sequence are mathematically impossible under normal operations. When we found invoices jumping from 4523 to 4789, the 265 missing invoices were not cancelled orders — the statistical impossibility of that explanation was itself evidence. That traditional technique, combined with the modern capability to extract and analyse database logs, server backups, and deletion timestamps, created what I would describe as a forensic chain of custody with scientific rigour. We could demonstrate not just that records were missing but precisely when they were deleted, by which user account, and in what pattern — consistently timed to month-end GST filing deadlines. That temporal correlation removed any remaining ambiguity about intent.
The investigation also uncovered severe food safety violations alongside the financial fraud — rat infestations, HACCP violations, compromised cold chain integrity. NSH covered this intersection extensively. Is there a systemic connection between financial opacity and public health risk?
There is a direct, logical, and quantifiable connection — and I think it is one of the most important dimensions of this case for public understanding. Tax evasion is not an abstract financial crime. When an establishment suppresses half its revenue, it cannot officially spend proportionally on operational necessities — pest control, cold storage infrastructure, staff hygiene training, sanitation systems — without creating reconciliation anomalies that draw regulatory attention. The financial deception and the operational underinvestment are structurally linked. Every consumer who ate at these establishments was unknowingly subsidising that evasion with their health. The ₹600 crore hidden from the government represents not just lost public revenue but the cumulative underinvestment in the basic safety obligations that these businesses owed to their customers. The raids exposed both simultaneously because they were two faces of the same failure.
SECTION V — THE ROAD AHEAD: AP, TS & NATIONAL IMPLICATIONS
As Principal Chief Commissioner for AP and Telangana — two states with a fast-growing technology sector, significant pharmaceutical and manufacturing industries, and a rapidly expanding startup ecosystem — what is your technological vision for tax administration in this region?
The Telugu states present a fascinating opportunity precisely because of that technological density. A jurisdiction with strong IT sector presence, active pharmaceutical manufacturing, and a growing startup ecosystem means a taxpayer population that is digitally literate, data-generating, and increasingly integrated with formal financial systems. That is an ideal environment for the data-driven compliance model to work at its full potential. My priority is to ensure that the awareness infrastructure keeps pace with the regulatory evolution. The new Income Tax Act introduces new sections, new forms, new procedural requirements. Errors that arise from ignorance are a different problem from evasion. We are planning major awareness programmes across both states to ensure that taxpayers — from salaried individuals to large corporates — understand what has changed and what is required. Compliance must be enabled, not just enforced.
The investigation methodology developed in the Hyderabad hospitality case is reportedly being shared with field offices across the country as a replicable framework. What is the broader national implication?
The national implication is a fundamental shift in the compliance environment for every cash-intensive sector in India — not just hospitality. The combination of techniques we deployed: platform data cross-referencing, SFT analysis, PoS forensics, UPI network mapping, database log analysis — this is a replicable investigative model. The methodology can be applied to any sector where digital platforms generate independent transaction records. Retail, logistics, real estate services, healthcare, education — anywhere that Swiggy and Zomato equivalents exist, or where payment aggregators process volumes, we have the raw data to build the same kind of evidentiary architecture. The message is not punitive. It is systemic: the era in which sophisticated income suppression could be sustained indefinitely against tax authority investigation has ended. The digital infrastructure of the Indian economy has, paradoxically, become the most effective audit trail ever built. The honest taxpayer has nothing to fear from this evolution. The evasion model, at scale, is no longer viable.
“The digital infrastructure of the Indian economy has become the most effective audit trail ever built. The evasion model, at scale, is no longer viable.”
What would you say to young engineers and technology professionals who are considering careers in public service, specifically in institutions like the IRS?
I would tell them what my own career has demonstrated: the problems in public service are engineering problems. They are systems problems, data problems, process optimisation problems, security architecture problems. The Income Tax Department is not a bureaucracy that processes paper. It is a large-scale data organisation that manages one of the most complex information ecosystems in the country. The work of building Faceless Assessment, of constructing the SFT framework, of developing AI-driven anomaly detection systems, of conducting database forensics on multi-location enterprises — this is engineering work of the highest order, with direct consequences for national revenue and public welfare. If you want to build systems that matter at scale — systems whose outputs affect hundreds of millions of people — there is no better laboratory than Indian public service. The tools are modern. The problems are real. And the impact is measurable.
