Governments have spent a decade digitising their tax infrastructure. E-invoicing mandates, continuous transaction controls and structured data frameworks now span countries on all continents. While digitisation solved data collection, enforcement is still a daily challenge and hence revenue gaps are not closing.
As of today, in most jurisdictions, audit coverage remains below five per cent of the registered taxpayer base, which leads to sophisticated fraud schemes that exploit the infrastructure designed to expose them. So, admittedly, the problem is not insufficient data, but insufficient intelligence extraction from this data.
Whether the PEPPOL 5-corner model is followed in a country, or 4 or any customised model, a structured copy of every invoice of commercial activity is available. While the format validation, duplicate detection and structural checks happen, what is missed is the fraud signals. Like counterparty integrity, network risk, lapsed supplier registration, mismatched product codes and rates, the questionable network of buyer-sellers, and a host of others. While the signals exist they need to be identified, extracted, interpreted and differentiated.
AI-Led Risk Intelligence
This is what AI-led risk intelligence comes in, doing what traditional audit cannot do. Especially with huge data sets that are manually impossible to sift. AI-based risk intelligence models crunch the volumes of data and help shift the question from how many taxpayers can be audited to which cases will produce the highest yield and the strongest deterrent effect.
Carousel and missing trader schemes are designed to make near-accurate trade chains, so that each transaction and player in the chain looks compliant. But the buyer-seller graph is embedded in e-invoice data that needs identifying circular or anomalous flow structures, often deep at the transaction level.
Traders’ behaviours also evolve with their experience and avoid well known traps. Catching a disappearing trader reducing activity gradually to avoid detection, or a new entity accumulating ITC claims at a rate no legitimate business at that growth stage could justify becomes impossible to the human eye.
Commercially active entities appearing as suppliers or buyers in structured invoice flows are often absent from the taxpayer registration master, the unregistered economy. The signals, patterns, trends, networks, registration data and evidences are almost hidden in plain sight, to a manual look.
Being documented and known domain issues, but with a huge magnitude and scale, manual systems fail to detect and act in the desired time, necessitating a technology solution led with AI.
Architecture of Intelligent Enforcement
The good news is that there is a solution. AI provides pattern discovery no rule set could anticipate. The analytical framework that converts e-invoice and return data into defensible enforcement decisions has three layers, and the sequencing matters.
The foundation is a rule-based weighted scoring model; a deliberate design choice, not a compromise position. Tax enforcement decisions must be auditable and legally defensible. Expert-defined rules encoding known fraud signals, including declared-versus-invoiced mismatches, delayed or nil returns, sudden activity spikes, frequent amendments and circular trading patterns, produce scores that can be explained in proceedings, challenged by legal representatives and refined by domain experts without requiring data science expertise.
High-risk entities receive immediate scrutiny. Medium-risk cases enter active monitoring. Low-risk taxpayers are managed passively, releasing enforcement capacity for where it matters most.
Statistical benchmarking of sectors and industry can help flag margins significantly below norms which are statistically anomalous and that warrant examination. ITC claim rates exceeding norms within sectors would throw a leakage signal visible at the population level. And thus, risk heatmaps built from this analysis allow policy teams to direct enforcement at highest-risk segments with evidence behind the decision, not intuition.
To supplement this, graph analytics applied to buyer-seller networks identifies circular trading structures. AI models trained on real data show emerging fraud patterns before they become established typologies. For forecasting, predictive models can be trained to score new registrations and new high-value trading relationships for their resemblance to known anomaly behaviour, flagging them before fraud actually occurs.
This progression is additive, not substitutive. The rule-based layer provides regulatory defensibility. Statistical benchmarking provides population-level sector intelligence. All three are necessary in a complete enforcement architecture.
What AI-Led Governance Provides
It is well known that technology alone cannot produce the intended results as it is not the silver bullet; organisational conditions that allow risk intelligence to function are less commonly discussed, and more commonly where implementations fail.
Undoubtedly, data acquisition and its quality is fundamental. Fragmented registration data, inconsistencies between invoice records and return filings, or poor linkage across data sources produce unreliable scores that undermine models and the confidence in technology which can question the credibility of the AI-led risk intelligence programmes. The learning: data governance cannot be deferred.
With AI models, it is utmost important that explainability is provided to the users, since enforcement is the task. Questions will be asked and there have to be answers, the right answers too. A risk score that cannot be explained cannot initiate a formal investigation, cannot withstand an appeal and will not be trusted by the investigation teams expected to act on it. Every score must carry an evidence trail by design, not as an afterthought.
And performance has to be measured against successful outcomes, not just alerts generated, which is not true value. Tax authorities could also create metrics like audit yield, case closure rates, revenue recovered per investigation and voluntary compliance improvement in targeted segments to determine success of AI-led risk intelligence programmes.
Shift That Is Already Underway
With more and more data being accumulated in data warehouses, by the minute, hiring more auditors is not the solution any tax authority should be looking at. The most consequential change in tax enforcement will come from authorities that learn to treat their data as an active enforcement instrument.
Efficient and proactive clearance regimes could use risk intelligence to even make pre-clearance intervention possible by flagging high-risk invoices before credits are granted. In other regimes, AI-led near real-time scoring compresses the detection cycle substantially, from months to just days if not hours. Across any regimes, AI-led behavioural and network analytics surface the fraud typologies that rule-based manual systems are just not capable to find.
The problem needs to be understood to be a technology and analytical one. Those who use technology and build the intelligence infrastructure to act on what the data is already telling them through AI-powered indirect tax compliance will be better positioned to identify fraud, improve audit effectiveness and strengthen voluntary compliance.
The author is executive director and chief AI and data officer at Cygnet.One. Views are personal.
