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Future of bitcoin in India: Issues and challenges

2020, Journal of Statistics and Management Systems

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The present study is focused on assessing the role and impact of Bitcoin. As India is in the technical era, techno-Indians speak, invest, and possess Bitcoin. This paper is an outcome of an exploratory research on the role of Bitcoin and its future in the Indian economy. The study answers the question "Is Bitcoin boon or bane in India".

In 2017, the term 'Bitcoin' was searched 8 times more than the term 'Kardashian'. So what is the bitcoin? Or some as saying cryptocurrencies and how many people know about it and what do they know about it. Is Bitcoin the next economic bubble? This research is conducted to find what people know and what they think about the cryptocurrencies, this survey includes one's view about payment methods, about their preferred mode of investment, and their views and opinions about cryptocurrencies.

Interal Res journa Managt Sci Tech

In the history of humans, they have used commodity as currency in Barter system. Fiat currency(notes and coin) is the mostrecent development, they were first used around 1000 years ago, but today it is the most dominantform of money. This wasn't the end of monetary evolution. Cryptocurrency isthe latest development is the monetary system. Cryptocurrency is neither commodity money nor fiat money – it is a virtual currency.Compared to conventional payment systems, Bitcoin lacks governancestructure other than its underlying software. This has several implications forthe functioning of the system. First, Bitcoin imposes no obligation for a financialinstitution, payment processor, or other intermediary to verify a user's identityor cross-check with watch-lists or embargoed countries. Second, Bitcoin imposesno prohibition on sales of particular items;This article explains what cryptocurrency is, awareness about cryptocurrency in Indian marketand, the factors which will help people in adoption of cryptocurrency.

East Asian Journal of Multidisciplinary Research

Santhi Appannan

The purpose of this study is to identify the relationship between factors affecting the acceptance of Bitcoin in the Northern Region of Malaysia. Past studies with reference to the factors affecting the acceptance of Bitcoin were being done on a larger scale outside of Malaysia and none of the studies has narrowed it down to make the research more specific. Therefore, this study proposed to study the factors affecting the acceptance of Bitcoin in the Northern Region of Malaysia based on performance expectancy, risk, social influence, and facilitating conditions. Furthermore, the relationship between the factors affecting the acceptance of Bitcoin and the acceptance of Bitcoin in the Northern Region of Malaysia has been discovered in this research. The population selected for this exploration is the respondents in Perlis, Kedah, Pulau Pinang, and Perak. The primary data for this research has been collected from the respondents through an online survey questionnaire. The data collecte...

Now this day we see many new innovations in Monetary System. Now at one side Digital Currency comes with a new concept called Plastic Money with great and easy ways to pay and get money to anyone. And the other side we still struggling with Fiat Currencies problems which effecting economy of any country very badly. Conventional currency has been based on gold or silver. Around the world, people are using software programs that follow a mathematical formula to produce Bitcoins but bitcoin is equally facing risks, non acceptability of central banking system in developing Nations. This paper studies the present scenario of Bitcoin, its evolution and its impact on developing Nations like India

Acheampong Owusu

The purpose of this study is to explore the determinants of bitcoin adoption among individuals and also to assess whether the usage of the bitcoin technology for payment of transactions is preferable to other modes of payment. The study proposed a conceptual model analyzing the driving factors that influence a behavior towards the utilization of bitcoins in a developing economy, Ghana, through the lens of the unified theory of acceptance and use of technology model. A qualitative method which employed a purposive sampling technique in the selection of twelve respondents who understand and utilize the bitcoin technology was used. The findings showed that majority of the respondents were exceptionally positive about the idea of bitcoin and had the goal to keep utilizing it, on conditions with respect to the usefulness, the ease of use, the secureness, and the price value of bitcoin. The study offers practitioners with a framework based on theory that goes beyond not only investigating the adoption but also to assess the impact in utilizing bitcoin for organizational benefits.

tamrin Amboala

The evolution of blockchain technology that promotes decentralised peer to peer electronic cash system named as Bitcoin is becoming trending across the globe. Although the Bitcoin price is extremely volatile, the popularity of bitcoin is seemingly unambiguous. The Bitcoin trending has garnered a serious attention from the public, media and the academia. Hitherto, no research has yet systematically investigated the factors influencing the intention to invest in Bitcoin. Thus, the objective of this study is to identify the factors that influence the intention to invest in Bitcoin. An online survey was distributed via the email to the target respondents, those who have basic knowledge on Bitcoin investment. After three weeks of data collection period for pilot study, 45 samples were gathered to be tested further. Using the SPSS programme, the data were analysed through reliability test and factor analysis. The results of the study indicate that the items are valid and reliable. The exp...

Journal of Economics and Behavioral Studies

Since its introduction in 2008, the value and popularity of Bitcoin have risen exponentially. Despite being 10 years old, the concept of crypto currency is fairly new in South Africa. The increase in the value of Bitcoin, together with extensive media coverage, has led to the creation of a Bitcoin economic system with many South Africans jumping on the Bitcoin bandwagon. This study aims to identify the determinants affecting end-user adoption of Bitcoin in South Africa and to determine the main use of the crypto currency by South Africans. A research model was developed utilising constructs from the technology acceptance model and theory of planned behaviour. The model was then tested empirically by utilising two survey-based questionnaires, one for current users of Bitcoin and one for non-users. For users, perceived usefulness and access to facilitating conditions were the primary determinants influencing their decision to adopt the crypto currency while lack of trust and social i...

Pacific Business Review (International)

Dr.D.Ravinder Yadav

The assumption for more advantage altogether faster makes man to examine various ways. However, on occasion, it leaves a bitter experience. The instance of Ramalinga Swamy is an illustration of that. According to Sakshi media, G.Ramlinga Swamy, aged 36, of Khammam who allegedly killed himself by feeding poison at lodge in Suryapet due to loss of Rs.70 lakh over to crypto scammers. “He fell in their trap and start investing in cryptocurrency are initially given good returns. Eventually, fraudsters block the accounts and scoot” (Sakshi, 2021). This incident has awakened us to know the factors influencing people to invest their money in cryptocurrency. A sample of 300 investors who invested in cryptocurrency are selected on the basis of stratified random sampling from Warangal district of Telangana state. The study reveals that the majority of the investors are in the age group of 31-35 and whose occupation is business. Further, multiple correlation technique is used to figure out the most important factors influencing consumers to invest their money in cryptocurrency. This study also reveals the impact of cryptocurrency on Indian society and environment.

Niki De Kegel

In the existing literature, little research has been conducted on the adoption of Bitcoin and the factors driving or preventing this adoption. This paper adds to the literature by using country-level determinants to investigate the different drivers and barriers that influenced the adoption rate of Bitcoin during the period 2011-2015. Our findings demonstrate that the cryptocurrency Bitcoin is used to a lesser extent in corrupt countries. Furthermore, we find that both the inflation rate and the efficiency of the banking system in a country act as a barrier to the adoption of Bitcoin while the occurrence of many non-cash transactions tends to drive the adoption rate. When we divide the sample into four subsets based on the level of gross national income of a country, our findings reveal similar results for certain country determinants but for most of them, we find some surprising results. We now establish that the better the accessibility to the internet and the more open a country is to globalization, the higher the adoption rate of the cryptocurrency. Previously, these determinants did not seem to be important drivers since no significant impact on the adoption rate of Bitcoin could be found. In addition , we find unexpected results for the impact of financial inclusion and the degree of non-cash utilization on the adoption rate since the sign of the beta estimate of the variables changes over different subsets. It is therefore not clear what the true relationship is between these country determinants and Bitcoin's adoption rate.

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Cryptocurrency, the Future of India

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Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 392))

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While some discerning investors are increasingly becoming disenchanted with virtual currencies like Bitcoin as a result of recent price drops, others believe it is too early to declare it a dead end. In this study, we try to find an answer to the burning question: to invest or not to invest? The paper looks at a variety of facets of cryptocurrency platforms in an effort to address the research’s key questions, “Will cryptocurrency be the next money platform?” and “Will cryptocurrency be the next currency platform?” “Is it possible to use virtual currency platforms?” In this paper, we try to elaborate these issues. For this, we collect primary as well as secondary data for the analysis. The primary data was collected from Delhi. The respondents were asked question about cryptocurrency to understand the preliminary impression of cryptocurrency’s use, development, trustworthiness, and future expectations. Due to the vast amount of cryptocurrency that is flowing through multiple systems, the massive expansion and growth of using and implementing cryptocurrencies, and the possibilities that cryptocurrencies provide, the finding of our research suggests that cryptocurrency is quite likely to be the future currency platform.

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Singh, P., Rajni (2022). Cryptocurrency, the Future of India. In: Goar, V., Kuri, M., Kumar, R., Senjyu, T. (eds) Advances in Information Communication Technology and Computing. Lecture Notes in Networks and Systems, vol 392. Springer, Singapore. https://doi.org/10.1007/978-981-19-0619-0_36

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Bitcoin ETFs Are 'Some Of The Best Launches In History,' Says Analyst, But Ethereum ETF Unlikely Before 2025

Zinger key points.

• Spot Bitcoin ETF launches were successful, inflows stagnating, but normal, says James Seyffart.
• SEC may delay Ethereum ETF decision. Hong Kong ETFs promising long-term, but immediate impact limited by market size.

James Seyffart , a prominent ETF analyst at Bloomberg Intelligence, shared his insights on the current state of Bitcoin BTC/USD and Ethereum ETH/USD ETFs, providing context on the recent slowdown in inflows .

What Happened : In a recent podcast , Seyffart highlighted the success of spot Bitcoin ETFs , which have taken in $12-13 billion since their launch on Jan. 11. “Bunch of these ETFs are some of the best launches in history,” he noted, with BlackRock ‘s iShares Bitcoin Trust   IBIT arguably “the best launch in history.”

However, Seyffart also pointed out that inflows have started to stagnate. IBIT experienced its first day without an inflow after a record-breaking 72-day streak. He emphasized that this is normal for ETFs and that “crypto Twitter is kind of in this bubble where they think it should be inflows every single day, and that is not going to be the case.”

When asked about the potential impact of retirement accounts and institutional investors on Bitcoin ETFs, Seyffart explained that while some independent advisor shops and retail investors have bought in, larger platforms and advisor networks are still going through due diligence processes.

“Usually, you’d say at least three months, likely even six months for that to happen,” he added.

Regarding the possibility of an Ethereum ETF by the end of the year, Seyffart expressed skepticism, stating that 2025 is more likely. He believes that the SEC will likely “kick the can down the road” and that decisions may be influenced by the upcoming election in November.

Seyffart also touched on the upcoming Hong Kong ETFs , which will offer both Bitcoin and Ethereum spot ETFs with in-kind creation and redemption. While he believes this could be huge in the long term, he cautioned against overhyping their immediate impact due to the relatively small size of the Hong Kong market compared to the US.

Read Also: ‘Bitcoin Is In Correction Mode,’ But That’s ‘Necessary For The Chart To Look Good,’ Crypto Trader Reassures

Why It Matters : Seyffart’s comments come amid price consolidation for Bitcoin and growing concerns about the risks associated with spot Bitcoin ETFs.

Jim Bianco , founder of Bianco Research , pointed out that ETFs are primarily held by “paper-handed small-time traders” rather than institutional investors. He warned that if the price of Bitcoin drops below the average purchase price of around $58,000, it could trigger significant selling pressure from these “paper hands” investors.

Bloomberg ETF analyst Eric Balchunas pushed back against Bianco’s thesis, arguing that it’s too early to draw definitive conclusions about the long-term prospects of these ETFs.

What’s Next : These topics are expected to be thoroughly explored at Benzinga’s upcoming Future of Digital Assets  event on Nov. 19.

Read Next: Is The ‘Memecoin Supercycle’ Over? Mad Crypto Alpha With Ivan

This content was partially produced with the help of AI tools and was reviewed and published by Benzinga editors.

Image created using artificial intelligence with Midjourney.

© 2024 Benzinga.com. Benzinga does not provide investment advice. All rights reserved.

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Blockchain Sleuth Elliptic Explores AI and Anti-Money Laundering Using 200M Bitcoin Transactions

• The Elliptic2 dataset is orders of magnitude bigger than the one used when the team began using machine learning to detect money laundering with bitcoin back in 2019.
• The research made use of 122,000 groups of connected nodes and chains of transactions called "subgraphs" with known links to illicit activity.

Blockchain analytics firm Elliptic said it detected potential money laundering patterns on the Bitcoin blockchain after training an artificial intelligence (AI) model using a record 200 million transactions.

The work is an extension of a program carried out back in 2019 that used a dataset of only 200,000 transactions. The much larger "Elliptic2" dataset made use of 122,000 labeled "subgraphs," groups of connected nodes and chains of transactions known to have links to illicit activity.

AI becomes more insightful the larger the dataset available to train the machine-learning algorithms, and cryptocurrencies like bitcoin offer a plentiful supply of transparent transaction data on the blockchain. Elliptic used the transactions for learning the set of "shapes" that money laundering exhibits in cryptocurrency and accurately classifying new criminal activity, Elliptic said in a paper co-authored with researchers from the MIT-IBM Watson AI Lab.

"The money laundering techniques identified by the model have been identified because they are prevalent in bitcoin," Elliptic co-founder Tom Robinson said in an email. "Crypto laundering practices will evolve over time as they cease being effective, but an advantage of an AI/deep learning approach is that new money laundering patterns are identified automatically as they emerge."

Many of the suspicious subgraphs were found to contain what are known as "peeling chains," where a user sends or "peels" cryptocurrency to a destination address, while the remainder is sent to another address under the user's control. This happens repeatedly to form a peeling chain.

"In traditional finance this is known as 'smurfing,' where large amounts of cash are structured into multiple small transactions, to keep them under regulatory reporting limits and avoid detection," Elliptic said in the paper.

Another commonly occurring technique was the use of so-called "nested services," businesses that move funds through accounts at larger cryptocurrency exchanges, sometimes without the awareness or approval of the exchange. A nested service might receive a deposit from one of their customers into a cryptocurrency address, and then forward the funds to their deposit address at an exchange.

"Nested services are known to frequently have less stringent customer due diligence checks than the cryptocurrency exchanges they utilize, or sometimes have no such anti-money laundering checks at all, resulting in their misuse for cryptocurrency laundering - potentially causing them to feature in subgraphs deemed by the model as suspicious," said Elliptic.

Elliptic’s AI-driven research helps fight Bitcoin money laundering

Blockchain analytics firm Elliptic has made significant progress in using artificial intelligence (AI) to identify potential money laundering patterns on the Bitcoin blockchain.

In a research paper co-authored with the MIT-IBM Watson AI Lab, Elliptic described its use of a deep learning model trained on a dataset of nearly 200 million transactions to detect illicit activity involving groups of bitcoin nodes and transaction chains.

The research builds upon a previous study conducted in 2019, which utilized a much smaller dataset of 200,000 transactions. The new "Elliptic2" dataset contains 122,000 labeled "subgraphs," representing groups of connected nodes and transaction chains known to have links to illicit activity. By training the AI model on this extensive dataset, Elliptic aims to improve the accuracy and efficiency of detecting money laundering and other financial crimes on the blockchain.

The inherent transparency of blockchain data makes it well-suited for machine learning techniques, as transaction information and entity types can be readily analyzed. This stands in contrast to traditional finance, where transaction data is often siloed, making the application of AI more challenging.

The trained model successfully identified proceeds of crime deposited at a crypto exchange, as well as novel money laundering transaction patterns and previously-unknown illicit wallets. These findings are already being incorporated into Elliptic's products to enhance their capabilities.

"The money laundering techniques identified by the model have been identified because they are prevalent in bitcoin," Elliptic co-founder Tom Robinson explained in an email. "Crypto laundering practices will evolve over time as they cease being effective, but an advantage of an AI/deep learning approach is that new money laundering patterns are identified automatically as they emerge."

The research revealed common money laundering techniques, such as "peeling chains," where a user sends cryptocurrency to a destination address while sending the remainder to another address under their control, forming a chain of transactions. Another prevalent method involved the use of "nested services," businesses that move funds through accounts at larger crypto exchanges, sometimes even without the exchange's knowledge or approval.

To encourage further collaboration and advancement in this field, Elliptic has made the "Elliptic2" dataset publicly available. As the largest public dataset of its kind, it will enable the wider community to develop new AI techniques for detecting illicit cryptocurrency transactions and contribute to the ongoing fight against financial crime in the crypto space.

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We encourage authors to upload their code and data as part of their supplementary material in order to help reviewers assess the quality of the work. Check the policy as well as code submission guidelines and templates for further details.

Use of Large Language Models (LLMs): We welcome authors to use any tool that is suitable for preparing high-quality papers and research. However, we ask authors to keep in mind two important criteria. First, we expect papers to fully describe their methodology, and any tool that is important to that methodology, including the use of LLMs, should be described also. For example, authors should mention tools (including LLMs) that were used for data processing or filtering, visualization, facilitating or running experiments, and proving theorems. It may also be advisable to describe the use of LLMs in implementing the method (if this corresponds to an important, original, or non-standard component of the approach). Second, authors are responsible for the entire content of the paper, including all text and figures, so while authors are welcome to use any tool they wish for writing the paper, they must ensure that all text is correct and original.

Double-blind reviewing:   All submissions must be anonymized and may not contain any identifying information that may violate the double-blind reviewing policy.  This policy applies to any supplementary or linked material as well, including code.  If you are including links to any external material, it is your responsibility to guarantee anonymous browsing.  Please do not include acknowledgements at submission time. If you need to cite one of your own papers, you should do so with adequate anonymization to preserve double-blind reviewing.  For instance, write “In the previous work of Smith et al. [1]…” rather than “In our previous work [1]...”). If you need to cite one of your own papers that is in submission to NeurIPS and not available as a non-anonymous preprint, then include a copy of the cited anonymized submission in the supplementary material and write “Anonymous et al. [1] concurrently show...”). Any papers found to be violating this policy will be rejected.

OpenReview: We are using OpenReview to manage submissions. The reviews and author responses will not be public initially (but may be made public later, see below). As in previous years, submissions under review will be visible only to their assigned program committee. We will not be soliciting comments from the general public during the reviewing process. Anyone who plans to submit a paper as an author or a co-author will need to create (or update) their OpenReview profile by the full paper submission deadline. Your OpenReview profile can be edited by logging in and clicking on your name in https://openreview.net/ . This takes you to a URL "https://openreview.net/profile?id=~[Firstname]_[Lastname][n]" where the last part is your profile name, e.g., ~Wei_Zhang1. The OpenReview profiles must be up to date, with all publications by the authors, and their current affiliations. The easiest way to import publications is through DBLP but it is not required, see FAQ . Submissions without updated OpenReview profiles will be desk rejected. The information entered in the profile is critical for ensuring that conflicts of interest and reviewer matching are handled properly. Because of the rapid growth of NeurIPS, we request that all authors help with reviewing papers, if asked to do so. We need everyone’s help in maintaining the high scientific quality of NeurIPS.  

Please be aware that OpenReview has a moderation policy for newly created profiles: New profiles created without an institutional email will go through a moderation process that can take up to two weeks. New profiles created with an institutional email will be activated automatically.

Venue home page: https://openreview.net/group?id=NeurIPS.cc/2024/Conference

If you have any questions, please refer to the FAQ: https://openreview.net/faq

Ethics review: Reviewers and ACs may flag submissions for ethics review . Flagged submissions will be sent to an ethics review committee for comments. Comments from ethics reviewers will be considered by the primary reviewers and AC as part of their deliberation. They will also be visible to authors, who will have an opportunity to respond.  Ethics reviewers do not have the authority to reject papers, but in extreme cases papers may be rejected by the program chairs on ethical grounds, regardless of scientific quality or contribution.  

Preprints: The existence of non-anonymous preprints (on arXiv or other online repositories, personal websites, social media) will not result in rejection. If you choose to use the NeurIPS style for the preprint version, you must use the “preprint” option rather than the “final” option. Reviewers will be instructed not to actively look for such preprints, but encountering them will not constitute a conflict of interest. Authors may submit anonymized work to NeurIPS that is already available as a preprint (e.g., on arXiv) without citing it. Note that public versions of the submission should not say "Under review at NeurIPS" or similar.

Dual submissions: Submissions that are substantially similar to papers that the authors have previously published or submitted in parallel to other peer-reviewed venues with proceedings or journals may not be submitted to NeurIPS. Papers previously presented at workshops are permitted, so long as they did not appear in a conference proceedings (e.g., CVPRW proceedings), a journal or a book.  NeurIPS coordinates with other conferences to identify dual submissions.  The NeurIPS policy on dual submissions applies for the entire duration of the reviewing process.  Slicing contributions too thinly is discouraged.  The reviewing process will treat any other submission by an overlapping set of authors as prior work. If publishing one would render the other too incremental, both may be rejected.

Anti-collusion: NeurIPS does not tolerate any collusion whereby authors secretly cooperate with reviewers, ACs or SACs to obtain favorable reviews. 

Author responses:   Authors will have one week to view and respond to initial reviews. Author responses may not contain any identifying information that may violate the double-blind reviewing policy. Authors may not submit revisions of their paper or supplemental material, but may post their responses as a discussion in OpenReview. This is to reduce the burden on authors to have to revise their paper in a rush during the short rebuttal period.

After the initial response period, authors will be able to respond to any further reviewer/AC questions and comments by posting on the submission’s forum page. The program chairs reserve the right to solicit additional reviews after the initial author response period.  These reviews will become visible to the authors as they are added to OpenReview, and authors will have a chance to respond to them.

After the notification deadline, accepted and opted-in rejected papers will be made public and open for non-anonymous public commenting. Their anonymous reviews, meta-reviews, author responses and reviewer responses will also be made public. Authors of rejected papers will have two weeks after the notification deadline to opt in to make their deanonymized rejected papers public in OpenReview.  These papers are not counted as NeurIPS publications and will be shown as rejected in OpenReview.

Publication of accepted submissions:   Reviews, meta-reviews, and any discussion with the authors will be made public for accepted papers (but reviewer, area chair, and senior area chair identities will remain anonymous). Camera-ready papers will be due in advance of the conference. All camera-ready papers must include a funding disclosure . We strongly encourage accompanying code and data to be submitted with accepted papers when appropriate, as per the code submission policy . Authors will be allowed to make minor changes for a short period of time after the conference.

Contemporaneous Work: For the purpose of the reviewing process, papers that appeared online within two months of a submission will generally be considered "contemporaneous" in the sense that the submission will not be rejected on the basis of the comparison to contemporaneous work. Authors are still expected to cite and discuss contemporaneous work and perform empirical comparisons to the degree feasible. Any paper that influenced the submission is considered prior work and must be cited and discussed as such. Submissions that are very similar to contemporaneous work will undergo additional scrutiny to prevent cases of plagiarism and missing credit to prior work.

Plagiarism is prohibited by the NeurIPS Code of Conduct .

Other Tracks: Similarly to earlier years, we will host multiple tracks, such as datasets, competitions, tutorials as well as workshops, in addition to the main track for which this call for papers is intended. See the conference homepage for updates and calls for participation in these tracks. 

Experiments: As in past years, the program chairs will be measuring the quality and effectiveness of the review process via randomized controlled experiments. All experiments are independently reviewed and approved by an Institutional Review Board (IRB).

Financial Aid: Each paper may designate up to one (1) NeurIPS.cc account email address of a corresponding student author who confirms that they would need the support to attend the conference, and agrees to volunteer if they get selected. To be considered for Financial the student will also need to fill out the Financial Aid application when it becomes available.