Tax evasion, usually the largest component of an informal economy, is a persistent challenge over history with significant socio-economic implications. Many socio-economic studies investigate its dynamics, including influencing factors, the role and influence of taxation policies, and the prediction of the tax evasion volume over time. These studies assumed such behavior is given, as observed in the real world, neglecting the "big bang" of such activity in a population. To this end, computational economy studies adopted developments in computer simulations, in general, and recent innovations in artificial intelligence (AI), in particular, to simulate and study informal economy appearance in various socio-economic settings. This study presents a novel computational framework to examine the dynamics of tax evasion and the emergence of informal economic activity. Employing an agent-based simulation powered by Large Language Models and Deep Reinforcement Learning, the framework is uniquely designed to allow informal economic behaviors to emerge organically, without presupposing their existence or explicitly signaling agents about the possibility of evasion. This provides a rigorous approach for exploring the socio-economic determinants of compliance behavior. The experimental design, comprising model validation and exploratory phases, demonstrates the framework's robustness in replicating theoretical economic behaviors. Findings indicate that individual personality traits, external narratives, enforcement probabilities, and the perceived efficiency of public goods provision significantly influence both the timing and extent of informal economic activity. The results underscore that efficient public goods provision and robust enforcement mechanisms are complementary; neither alone is sufficient to curtail informal activity effectively.

Michael Cohen will not face sanctions after he cited fake legal cases in a court filing generated by artificial intelligence, a federal judge said Wednesday. Cohen, former President Trump's onetime fixer and lawyer, had pleaded guilty to tax and campaign finance violations and is currently under supervised release. He has repeatedly sought to have his sentence reduced, and in his most recent attempt, Cohen provided his attorney with fabricated case citations he later admitted were generated by Google's AI chatbot, formerly known as Bard. U.S. District Judge Jesse Furman said the false citations were "embarrassing and certainly negligent" in a 13-page order that denied Cohen's fourth motion for early termination of supervised release. But the judge found that Cohen, who had said he misunderstood how AI works and did not intend to cite fake cases, had not acted in "bad faith" and that neither he nor his lawyer, David Schwartz, should be sanctioned.

New Delhi, Sep 14: Finance Minister Nirmala Sitharaman on Wednesday lauded the efforts of the Central Board of Indirect Tax and Customs (CBIC) and underlined the need for training Personnel for better utilisation of Artificial Intelligence and data analytics. "You keep doing good work, generating revenues, you are noticed for it, Prime Minister made a special appreciative mention recently on level of GST revenues," she said while inaugurating'Kendriya GST Parisar', the housing complex for CGST officers of Mumbai zone in Kharghar, Navi Mumbai. The Finance Minister also spoke about the need for training CBIC officials for better utilisation of Artificial Intelligence, data-analytics, IoT and other technologies to identify fraudulent practices, such as detection of tax evasion. She was also appreciative of the Central Intelligence Unit team of CGST Mumbai Zone for fantastic data analysis and mining tools used for large scale tax evasion resulting in big recovery and booking of offenders. She also lauded the effort of the department to take care of future needs and development required for the second phase for welfare of officers and staff.

This paper presents a computational evolutionary game model to study and understand fraud dynamics in the consumption tax system. Players are cooperators if they correctly declare their value added tax (VAT), and are defectors otherwise. Each player's payoff is influenced by the amount evaded and the subjective probability of being inspected by tax authorities. Since transactions between companies must be declared by both the buyer and seller, a strategy adopted by one influences the other's payoff. We study the model with a well-mixed population and different scale-free networks. Model parameters were calibrated using real-world data of VAT declarations by businesses registered in the Canary Islands region of Spain. We analyzed several scenarios of audit probabilities for high and low transactions and their prevalence in the population, as well as social rewards and penalties to find the most efficient policy to increase the proportion of cooperators. Two major insights were found. First, increasing the subjective audit probability for low transactions is more efficient than increasing this probability for high transactions. Second, favoring social rewards for cooperators or alternative penalties for defectors can be effective policies, but their success depends on the distribution of the audit probability for low and high transactions.

Court documents released in August revealed that Swiss tax officials are investigating art dealer and freeport magnate Yves Bouvier for allegedly concealing CHF 330 million in profits. The Swiss authorities believe that Bouvier used a fictitious residence in Singapore to evade taxes in his home country, and confiscated one of Bouvier's properties, reportedly worth CHF 4.5 million, as a pledge while they continue investigating his finances. The investigation, however, was nearly derailed in its early stages due to a single vulnerable tax official. An escort girl known only as Sarah has testified that in September 2017, Yves Bouvier sent her to a conference to seduce a key official with Switzerland's Federal Tax Administration. Sarah's honeypot adventure took place mere months after Swiss tax officials had begun looking into Bouvier's finances.

The field of journalism over the past decade or so has been witnessing continuous change. Today, journalism is influenced by big data and new computational tools. Data and visualisation have become the latest techniques for telling stories in media, thanks to intersections between journalism and computation. One of the many things that AI is doing for journalism is to make it easier and faster to analyse the data and also synthesise the data into stories. When we mention automatic story writing tools, they use Natural Language Understanding and Processing, to synthesise the stories.

New Delhi: Delhi government's VAT department will soon take the help of a machine learning tool to identify bogus firms and tax evasion to plug leaks, which is estimated to be around Rs 300 crore annually. AAP government has shared thousands of VAT returns registered in Delhi between 2012-17, which are being scrutinised by two research scholars -- Aprajit Mahajan and Shekhar Mittal -- who are associated with University of California, Berkeley. Government officials said the researchers are likely to submit their first set of findings by December on the basis of which the VAT department would plan its action against firms that might be involved in systematic evasion of taxes. "It is going to be a first-ever systematic study of tax evasion in an economy with weak compliance," Delhi Dialogue and Development Commission vice-chairman Jasmine Shah said, referring to the researchers' paper'Who is Bogus? According to officials, the VAT department currently carries out surprise inspections to nab defaulters or traders resorting to unfair means to avoid paying taxes by generating bills of fraudulent transactions with firms that exist only on paper.

HM Revenue & Customs' decision to utilise more artificial intelligence in its quest to gather evidence for tax evasion investigations has led to a decline in the number of raids at business premises across the UK. The number of raids fell overall by 30% within 12 months. HMRC officers raided 471 commercial properties in the 12 months to April 2018, compared with 669 in the previous year, according to figures obtained in a recent Freedom of Information request. In March, HMRC's Acting Digital Transformation Director, Brigid McBride, confirmed that the tax authority was keen to use AI to improve departmental efficiencies and ease the complexity of tax investigations. HMRC has set its department a goal of automating ten million processes by the end of 2018.

In our Expert Takes, opinion leaders from inside and outside the crypto industry express their views, share their experience and give professional advice. Expert Takes cover everything from Blockchain technology and ICO funding to taxation, regulation, and cryptocurrency adoption by different sectors of the economy. If you would like to contribute an Expert Take, please email your ideas and CV to george@cointelegraph.com. According to TI's Corruption Perceptions Index for 2017, Spain slid eight points to be one of the EU's lowest ranked countries due to a spate of high-profile corruption scandals over the last decade -- with public procurement being particularly vulnerable. Albeit, Spain has been actively combating corruption by amending its anti-corruption laws and by developing blockchain and artificial intelligence (AI) solutions.

We present an algorithm that can anticipate tax evasion by modeling the co-evolution of tax schemes with auditing policies. Malicious tax non-compliance, or evasion, accounts for billions of lost revenue each year. Unfortunately when tax administrators change the tax laws or auditing procedures to eliminate known fraudulent schemes another potentially more profitable scheme takes it place. Modeling both the tax schemes and auditing policies within a single framework can therefore provide major advantages. In particular we can explore the likely forms of tax schemes in response to changes in audit policies.