Tax Evasion Goes Digital: Criminals Shift To Novel Crypto Instruments – Analysts

bitcoinistОпубліковано о 2026-05-22Востаннє оновлено о 2026-05-22

Анотація

Italian police uncovered a tax fraud case worth over $1 million where the suspect used novel Bitcoin-based tools, the Ordinals protocol and BRC-20 token standard, to conceal undeclared capital gains. The individual allegedly created and sold tokens, funneling profits back into a primary Bitcoin wallet in a repeated cycle to avoid tax records. Analysts from Chainalysis note that while tax evasion using cryptocurrency is not new, methods are becoming more creative, with bad actors increasingly turning to NFTs, DeFi, and new token standards. However, a fundamental weakness exists: the blockchain provides a permanent, unchangeable record of all transactions. Blockchain intelligence tools can trace these transactions and link them to individuals, especially when combined with data from exchanges. This case demonstrates that technical novelty does not guarantee anonymity. The tax gap remains a significant issue globally, with studies showing low reporting rates among crypto owners. As new digital assets generate wealth, the discrepancy between on-chain activity and declared income is drawing increased scrutiny from investigators worldwide.

An Italian police unit cracked a tax fraud case worth over a million dollars — and at the center of it was not a secret bank account or a shell company, but Bitcoin inscriptions.

A New Way To Hide Old Money

Italy’s Economic and Financial Police Unit in Foggia uncovered a scheme in which a suspect allegedly used the Bitcoin Ordinals protocol and the BRC-20 token standard to generate and conceal roughly 1 million euros, or about $1.1 million, in undeclared capital gains.

According to blockchain analytics firm Chainalysis, the suspect created tokens using those tools, listed them on marketplaces, sold them for far more than they originally cost, and funneled the profits back into a primary Bitcoin wallet.

The cycle repeated — earnings went straight into new inscriptions, keeping the money moving and off tax records.

Introduced in 2023, the Ordinals protocol works by assigning a serial number to a satoshi, the smallest unit of Bitcoin, and embedding data such as images or text into a Bitcoin transaction. The BRC-20 standard builds on that by letting users deploy, mint, and transfer tokens directly on the Bitcoin blockchain.

Tax Authorities Playing Catch-Up

Tax evasion through crypto is not new. What is changing is how creative the methods are getting. Chainalysis said bad actors are increasingly turning to NFTs, decentralized finance protocols, and emerging token standards in hopes of keeping wealth hidden from authorities. The firm published its findings Wednesday.

BTCUSD now trading at $77,065. Chart: TradingView

Compliance data suggests the problem runs deep. A study released in March found that only 32% to 56% of US crypto owners report their gains to tax authorities. In Norway, that figure dropped to just 12%, based on research published in August 2024.

Meanwhile, the US Internal Revenue Service puts the country’s gross tax gap — the total taxes legally owed but not collected — at around $606 billion.

A Trail That Never Disappears

Despite the technical creativity behind schemes like the one in Italy, Chainalysis said there is a built-in weakness in using crypto to hide money. The blockchain keeps a permanent record of every transaction, and that record cannot be changed or deleted.

The Fatal Flaw Of Crypto Fraud

Blockchain intelligence tools are capable of rebuilding a complete financial network and comparing it with information crypto exchanges are required to disclose, making it possible to trace transactions back to suspected tax cheats. Officials said the Italian case shows that technical novelty does not equal anonymity.

As new types of digital assets continue to appear and generate income, analysts say the gap between actual on-chain wealth and what people declare on their taxes will draw more attention from investigators around the world.

Featured image from Tax Central, chart from TradingView

Пов'язані питання

QWhat novel digital tools were allegedly used by a suspect in Italy to conceal undeclared capital gains?

AThe suspect allegedly used the Bitcoin Ordinals protocol and the BRC-20 token standard to generate and conceal the undeclared capital gains.

QAccording to Chainalysis, what are some of the crypto-based methods increasingly being used by bad actors to hide wealth from tax authorities?

AAccording to Chainalysis, bad actors are increasingly turning to NFTs, decentralized finance protocols, and emerging token standards to hide wealth.

QWhat is the inherent weakness of using crypto to hide money, as highlighted by the article?

AThe inherent weakness is that the blockchain maintains a permanent, unchangeable record of every transaction, creating a traceable trail.

QWhat is the estimated gross tax gap in the United States, as mentioned in the article?

AThe US Internal Revenue Service estimates the country's gross tax gap to be around $606 billion.

QBased on the article's example, what does the Italian tax fraud case demonstrate about technical novelty in crypto?

AThe Italian case demonstrates that technical novelty in crypto does not equal anonymity, and transactions can be traced back to suspects using blockchain intelligence tools.

Пов'язані матеріали

Upbit Rebalances 864B SHIB In Internal Wallet Move

South Korean cryptocurrency exchange Upbit moved a total of 864 billion SHIB (worth approximately $4 million) between its internal wallet addresses. The activity, involving transfers of 384 billion SHIB outbound and 480 billion SHIB inbound, occurred following a 36% SHIB price rally. On-chain data indicates this was a routine internal wallet rebalancing operation by the exchange, not a sell-off or liquidation event. The article cautions that while large on-chain transfers are highly visible, they require proper context and wallet labeling to interpret correctly. In this case, the movement between known Upbit addresses does not inherently signal a change in market supply or selling pressure for SHIB.

bitcoinist10 хв тому

Upbit Rebalances 864B SHIB In Internal Wallet Move

bitcoinist10 хв тому

Can Generative Models Finally Be Trained End-to-End? The Core Is a For Loop

This article introduces a novel training paradigm for generative models called Explorative Modeling (XM), which enables true end-to-end training. Traditionally, powerful generative models like autoregressive and diffusion models are not trained end-to-end. They are trained to predict a single small step but require iterative multi-step sampling for inference. This "exposure bias" leads to error accumulation and limits performance. The core challenge XM addresses is "mode blurring." In generative tasks, a single input (e.g., "generate a dog") corresponds to many valid outputs (multiple modes). Standard training objectives like reconstruction loss force the model to average these modes, producing unrealistic, blurry outputs. To avoid this, existing models break generation into many small, almost deterministic steps, sacrificing end-to-end training. XM tackles this by restructuring the training loop itself. Its key insight is to amplify "generative expressivity." For each training input, instead of generating one sample, the model generates K candidate outputs. Only the candidate closest to the real data is used for computing the loss and updating the model via backpropagation. This simple "best-of-K" mechanism is implemented as a short for-loop. By exploring multiple possibilities, the model learns to distribute its guesses across different modes rather than collapsing to their uninformative average. The paper demonstrates that "exploration" acts as a new, powerful scaling axis. Gains from XM increase with model size, data scale, and compute. Experiments show improvements in FID scores for image generation and significant efficiency gains, sometimes outperforming larger models without exploration. When pushed to the limit, XM enables fully single-step, end-to-end generative models. In robotics tasks, an "Explorative Policy" matched the performance of a 100-step Diffusion Policy with a single forward pass, drastically improving inference speed. While the best-of-K concept is not entirely new, the authors' contribution lies in formally understanding it as a direct method to boost generative expressivity without fragmenting the generation process. This work suggests that as models scale, enhancing exploration during training may become crucial for overcoming fundamental performance bottlenecks.

marsbit10 хв тому

Can Generative Models Finally Be Trained End-to-End? The Core Is a For Loop

marsbit10 хв тому

A Maximum of 20 Submissions Per Person? DeepMind Researcher Satirically 'Petitions' Against ICLR's New Rule

The International Conference on Learning Representations (ICLR) 2027 has introduced a new submission rule limiting authors to a maximum of 20 papers per conference. This policy, announced due to a 68% surge in submissions for ICLR 2026 that strained the review system, aims to ensure fair and informative peer review. Google DeepMind researcher Dan Roy responded with a sarcastic petition on X, calling the 20-paper cap "absurd" and claiming it would slow AI progress. His post mockingly argued that AI agents can now produce incremental research and that large language models (LLMs) already handle all reviewing, so output should be maximized. This satire critiques the broader trend of AI's role in academia, referencing a 2025 study that found about 21% of ICLR 2026 reviews were likely fully AI-generated. Roy's underlying point questions whether simply capping submissions addresses the systemic issue of AI potentially flooding conferences with agent-written papers and AI-assisted reviews. The debate highlights tensions between managing submission volume and maintaining research quality as AI tools become pervasive.

marsbit14 хв тому

A Maximum of 20 Submissions Per Person? DeepMind Researcher Satirically 'Petitions' Against ICLR's New Rule

marsbit14 хв тому

Coldcard Vulnerability Leads to $89 Million Theft, Sparking Largest On-Chain Migration Since FTX Collapse

A security vulnerability in Coldcard hardware wallets led to the theft of 1,367.05 BTC (approx. $89 million) from 4,585 addresses, according to Galaxy Research. The flaw, caused by insufficient randomness in seed phrase generation for specific firmware versions, triggered a massive migration of funds as users moved assets to new, secure wallets. This event caused the largest on-chain movement of small Bitcoin transactions since the FTX collapse, distorting key market indicators like active addresses and exchange deposits, and significantly dampening market sentiment. The incident also highlighted challenges in cybersecurity investigations, as researchers reported that safety guardrails on mainstream U.S. AI models hindered the analysis of attack patterns, forcing them to use an open-source Chinese AI model for forensic work. This underscores a dilemma in balancing AI security restrictions with the need for rapid response in tracing stolen crypto assets.

marsbit1 год тому

Coldcard Vulnerability Leads to $89 Million Theft, Sparking Largest On-Chain Migration Since FTX Collapse

marsbit1 год тому

Annual Salary of Millions Competing for Electricians, Meta Rushes to Open Its Own Technical School

The AI boom is facing an unexpected bottleneck: a severe shortage of skilled construction workers and electricians. As tech giants like Meta, OpenAI, and Alphabet race to build massive data centers—such as OpenAI's $16 billion "Stargate" project—they are hitting a critical labor wall. The U.S. needs an estimated 130,000 more electricians, 240,000 construction workers, and 150,000 supervisors by 2030 for AI infrastructure alone, but tens of thousands of electrician jobs go unfilled each year. While AI companies offer high premiums, with electricians earning up to $280,000 annually, worker scarcity still causes massive losses—delays on a single project can cost $14.2 million per month. The complexity of building AI data centers, which require immense power (equivalent to powering hundreds of thousands of homes), sophisticated electrical systems, and advanced liquid cooling solutions, demands highly skilled technicians who are in short supply. To combat this, companies are investing heavily in training. Meta has committed $115 million to a free training school offering tuition, housing, and stipends, targeting 5,000 new workers. OpenAI is partnering with unions to secure skilled labor. These efforts are paying off, with a significant rise in Gen Z interest in trade schools over college. However, the power demands are staggering. AI data centers are driving a rapid surge in electricity consumption, projected to account for up to 12% of U.S. power use by 2028 and raising costs for consumers. Furthermore, the construction boom is project-based, leading to a potential future glut of trained workers once building peaks, which could depress wages industry-wide. The race for AI supremacy now depends as much on skilled hands as on advanced chips.

marsbit1 год тому

Annual Salary of Millions Competing for Electricians, Meta Rushes to Open Its Own Technical School

marsbit1 год тому

Торгівля

Спот