19 datasets found
  1. A

    ‘Ethereum Fraud Detection Dataset’ analyzed by Analyst-2

    • analyst-2.ai
    Updated Nov 15, 2021
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2021). ‘Ethereum Fraud Detection Dataset’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/kaggle-ethereum-fraud-detection-dataset-d749/dcfcefc9/?iid=039-335&v=presentation
    Explore at:
    Dataset updated
    Nov 15, 2021
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Analysis of ‘Ethereum Fraud Detection Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/vagifa/ethereum-frauddetection-dataset on 13 November 2021.

    --- Dataset description provided by original source is as follows ---

    Context

    This dataset contains rows of known fraud and valid transactions made over Ethereum, a type of cryptocurrency. This dataset is imbalanced, so keep that in mind when modelling

    Content

    Here is a description of the rows of the dataset:

    • Index: the index number of a row
    • Address: the address of the ethereum account
    • FLAG: whether the transaction is fraud or not
    • Avg min between sent tnx: Average time between sent transactions for account in minutes
    • Avg_min_between_received_tnx: Average time between received transactions for account in minutes
    • Time_Diff_between_first_and_last(Mins): Time difference between the first and last transaction
    • Sent_tnx: Total number of sent normal transactions
    • Received_tnx: Total number of received normal transactions
    • Number_of_Created_Contracts: Total Number of created contract transactions
    • Unique_Received_From_Addresses: Total Unique addresses from which account received transactions
    • Unique_Sent_To_Addresses20: Total Unique addresses from which account sent transactions
    • Min_Value_Received: Minimum value in Ether ever received
    • Max_Value_Received: Maximum value in Ether ever received
    • Avg_Value_Received5Average value in Ether ever received
    • Min_Val_Sent: Minimum value of Ether ever sent
    • Max_Val_Sent: Maximum value of Ether ever sent
    • Avg_Val_Sent: Average value of Ether ever sent
    • Min_Value_Sent_To_Contract: Minimum value of Ether sent to a contract
    • Max_Value_Sent_To_Contract: Maximum value of Ether sent to a contract
    • Avg_Value_Sent_To_Contract: Average value of Ether sent to contracts
    • Total_Transactions(Including_Tnx_to_Create_Contract): Total number of transactions

    • Total_Ether_Sent:Total Ether sent for account address

    • Total_Ether_Received: Total Ether received for account address

    • Total_Ether_Sent_Contracts: Total Ether sent to Contract addresses

    • Total_Ether_Balance: Total Ether Balance following enacted transactions

    • Total_ERC20_Tnxs: Total number of ERC20 token transfer transactions

    • ERC20_Total_Ether_Received: Total ERC20 token received transactions in Ether

    • ERC20_Total_Ether_Sent: Total ERC20token sent transactions in Ether

    • ERC20_Total_Ether_Sent_Contract: Total ERC20 token transfer to other contracts in Ether

    • ERC20_Uniq_Sent_Addr: Number of ERC20 token transactions sent to Unique account addresses

    • ERC20_Uniq_Rec_Addr: Number of ERC20 token transactions received from Unique addresses

    • ERC20_Uniq_Rec_Contract_Addr: Number of ERC20token transactions received from Unique contract addresses

    • ERC20_Avg_Time_Between_Sent_Tnx: Average time between ERC20 token sent transactions in minutes

    • ERC20_Avg_Time_Between_Rec_Tnx: Average time between ERC20 token received transactions in minutes

    • ERC20_Avg_Time_Between_Contract_Tnx: Average time ERC20 token between sent token transactions

    • ERC20_Min_Val_Rec: Minimum value in Ether received from ERC20 token transactions for account

    • ERC20_Max_Val_Rec: Maximum value in Ether received from ERC20 token transactions for account

    • ERC20_Avg_Val_Rec: Average value in Ether received from ERC20 token transactions for account

    • ERC20_Min_Val_Sent: Minimum value in Ether sent from ERC20 token transactions for account

    • ERC20_Max_Val_Sent: Maximum value in Ether sent from ERC20 token transactions for account

    • ERC20_Avg_Val_Sent: Average value in Ether sent from ERC20 token transactions for account

    • ERC20_Uniq_Sent_Token_Name: Number of Unique ERC20 tokens transferred

    • ERC20_Uniq_Rec_Token_Name: Number of Unique ERC20 tokens received

    • ERC20_Most_Sent_Token_Type: Most sent token for account via ERC20 transaction

    • ERC20_Most_Rec_Token_Type: Most received token for account via ERC20 transactions

    --- Original source retains full ownership of the source dataset ---

  2. k

    Ethereum-Fraud-Detection-Dataset

    • kaggle.com
    Updated Apr 18, 2021
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    (2021). Ethereum-Fraud-Detection-Dataset [Dataset]. https://www.kaggle.com/vagifa/ethereum-frauddetection-dataset/code
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Apr 18, 2021
    License

    http://opendatacommons.org/licenses/dbcl/1.0/http://opendatacommons.org/licenses/dbcl/1.0/

    Description

    A dataset containing fraud and valid ethereum transactions

  3. Ethereum Fraud Detection Dataset

    • kaggle.com
    Updated Jan 3, 2021
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Vagif Aliyev (2021). Ethereum Fraud Detection Dataset [Dataset]. https://www.kaggle.com/vagifa/ethereum-frauddetection-dataset/tasks
    Explore at:
    Dataset updated
    Jan 3, 2021
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Vagif Aliyev
    License

    http://opendatacommons.org/licenses/dbcl/1.0/http://opendatacommons.org/licenses/dbcl/1.0/

    Description

    Context

    This dataset contains rows of known fraud and valid transactions made over Ethereum, a type of cryptocurrency. This dataset is imbalanced, so keep that in mind when modelling

    Content

    Here is a description of the rows of the dataset:

    • Index: the index number of a row
    • Address: the address of the ethereum account
    • FLAG: whether the transaction is fraud or not
    • Avg min between sent tnx: Average time between sent transactions for account in minutes
    • Avg_min_between_received_tnx: Average time between received transactions for account in minutes
    • Time_Diff_between_first_and_last(Mins): Time difference between the first and last transaction
    • Sent_tnx: Total number of sent normal transactions
    • Received_tnx: Total number of received normal transactions
    • Number_of_Created_Contracts: Total Number of created contract transactions
    • Unique_Received_From_Addresses: Total Unique addresses from which account received transactions
    • Unique_Sent_To_Addresses20: Total Unique addresses from which account sent transactions
    • Min_Value_Received: Minimum value in Ether ever received
    • Max_Value_Received: Maximum value in Ether ever received
    • Avg_Value_Received5Average value in Ether ever received
    • Min_Val_Sent: Minimum value of Ether ever sent
    • Max_Val_Sent: Maximum value of Ether ever sent
    • Avg_Val_Sent: Average value of Ether ever sent
    • Min_Value_Sent_To_Contract: Minimum value of Ether sent to a contract
    • Max_Value_Sent_To_Contract: Maximum value of Ether sent to a contract
    • Avg_Value_Sent_To_Contract: Average value of Ether sent to contracts
    • Total_Transactions(Including_Tnx_to_Create_Contract): Total number of transactions

    • Total_Ether_Sent:Total Ether sent for account address

    • Total_Ether_Received: Total Ether received for account address

    • Total_Ether_Sent_Contracts: Total Ether sent to Contract addresses

    • Total_Ether_Balance: Total Ether Balance following enacted transactions

    • Total_ERC20_Tnxs: Total number of ERC20 token transfer transactions

    • ERC20_Total_Ether_Received: Total ERC20 token received transactions in Ether

    • ERC20_Total_Ether_Sent: Total ERC20token sent transactions in Ether

    • ERC20_Total_Ether_Sent_Contract: Total ERC20 token transfer to other contracts in Ether

    • ERC20_Uniq_Sent_Addr: Number of ERC20 token transactions sent to Unique account addresses

    • ERC20_Uniq_Rec_Addr: Number of ERC20 token transactions received from Unique addresses

    • ERC20_Uniq_Rec_Contract_Addr: Number of ERC20token transactions received from Unique contract addresses

    • ERC20_Avg_Time_Between_Sent_Tnx: Average time between ERC20 token sent transactions in minutes

    • ERC20_Avg_Time_Between_Rec_Tnx: Average time between ERC20 token received transactions in minutes

    • ERC20_Avg_Time_Between_Contract_Tnx: Average time ERC20 token between sent token transactions

    • ERC20_Min_Val_Rec: Minimum value in Ether received from ERC20 token transactions for account

    • ERC20_Max_Val_Rec: Maximum value in Ether received from ERC20 token transactions for account

    • ERC20_Avg_Val_Rec: Average value in Ether received from ERC20 token transactions for account

    • ERC20_Min_Val_Sent: Minimum value in Ether sent from ERC20 token transactions for account

    • ERC20_Max_Val_Sent: Maximum value in Ether sent from ERC20 token transactions for account

    • ERC20_Avg_Val_Sent: Average value in Ether sent from ERC20 token transactions for account

    • ERC20_Uniq_Sent_Token_Name: Number of Unique ERC20 tokens transferred

    • ERC20_Uniq_Rec_Token_Name: Number of Unique ERC20 tokens received

    • ERC20_Most_Sent_Token_Type: Most sent token for account via ERC20 transaction

    • ERC20_Most_Rec_Token_Type: Most received token for account via ERC20 transactions

  4. H

    Data from: Detection of illicit accounts over the Ethereum blockchain

    • dataverse.harvard.edu
    • dataverse.nl
    csv, txt
    Updated Sep 21, 2021
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Harvard Dataverse (2021). Detection of illicit accounts over the Ethereum blockchain [Dataset]. http://doi.org/10.34894/GKAQYN
    Explore at:
    csv(1016388), txt(506)Available download formats
    Dataset updated
    Sep 21, 2021
    Dataset provided by
    Harvard Dataverse
    License

    https://dataverse.harvard.edu/api/datasets/:persistentId/versions/1.null/customlicense?persistentId=doi:10.34894/GKAQYNhttps://dataverse.harvard.edu/api/datasets/:persistentId/versions/1.null/customlicense?persistentId=doi:10.34894/GKAQYN

    Description

    The recent technological advent of cryptocurrencies and their respective benefits have been shrouded with a number of illegal activities operating over the network such as money laundering, bribery, phishing, fraud, among others. In this work we focus on the Ethereum network, which has seen over 400 million transactions since its inception. Using 2179 accounts flagged by the Ethereum community for their illegal activity coupled with 2502 normal accounts, we seek to detect illicit accounts based on their transaction history using the XGBoost classifier. Using 10 fold cross-validation, XGBoost achieved an average accuracy of 0.963 ( ± 0.006) with an average AUC of 0.994 ( ± 0.0007). The top three features with the largest impact on the final model output were established to be ‘Time diff between first and last (Mins)’, ‘Total Ether balance’ and ‘Min value received’. Based on the results we conclude that the proposed approach is highly effective in detecting illicit accounts over the Ethereum network. Our contribution is multi-faceted; firstly, we propose an effective method to detect illicit accounts over the Ethereum network; secondly, we provide insights about the most important features; and thirdly, we publish the compiled data set as a benchmark for future related works.

  5. Ethereum Phishing Transaction Network

    • kaggle.com
    zip
    Updated Mar 23, 2020
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    XBlock (2020). Ethereum Phishing Transaction Network [Dataset]. https://www.kaggle.com/datasets/xblock/ethereum-phishing-transaction-network
    Explore at:
    zip(410821864 bytes)Available download formats
    Dataset updated
    Mar 23, 2020
    Authors
    XBlock
    Description

    Cryptocurrency, as blockchain’s most famous implementation, suffers a huge economic loss due to phishing scams. In our work, accounts and transactions in Ethereum are treated as nodes and edges, thus detection of phishing accounts can be modeled as a node classification problem.

    In this work, we collected phishing nodes from Ethereum that reported in Etherscan labeled cloud. Starting from phishing nodes we crawl a huge Ethereum transaction network via second-order BFS. Dataset contains 2,973,489 nodes, 13,551,303 edges and 1,165 labeled nodes.

    MulDiGraph.pkl:This dataset is stored in pickle format, and it is the networkx object. Each node is an address with an attribute called isp indicating whether it is a phishing node. Each edge has two attributes, including amount and timestamp, which represent the balance of the transaction and the timestamp of the transaction, respectively. In this data set, the total number of nodes is 2,973,489, the number of transactions is 13,551,303, and the average degree is 4.5574.

    For more details about blockchain dataset, please click here.

  6. d

    Detection of illicit accounts over the Ethereum blockchain - Dataset -...

    • b2find.dkrz.de
    Updated Feb 18, 2020
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    (2020). Detection of illicit accounts over the Ethereum blockchain - Dataset - B2FIND [Dataset]. https://b2find.dkrz.de/dataset/0674163e-0c7b-5ec1-ab44-668efcdff333
    Explore at:
    Dataset updated
    Feb 18, 2020
    Description

    The recent technological advent of cryptocurrencies and their respective benefits have been shrouded with a number of illegal activities operating over the network such as money laundering, bribery, phishing, fraud, among others. In this work we focus on the Ethereum network, which has seen over 400 million transactions since its inception. Using 2179 accounts flagged by the Ethereum community for their illegal activity coupled with 2502 normal accounts, we seek to detect illicit accounts based on their transaction history using the XGBoost classifier. Using 10 fold cross-validation, XGBoost achieved an average accuracy of 0.963 ( ± 0.006) with an average AUC of 0.994 ( ± 0.0007). The top three features with the largest impact on the final model output were established to be ‘Time diff between first and last (Mins)’, ‘Total Ether balance’ and ‘Min value received’. Based on the results we conclude that the proposed approach is highly effective in detecting illicit accounts over the Ethereum network. Our contribution is multi-faceted; firstly, we propose an effective method to detect illicit accounts over the Ethereum network; secondly, we provide insights about the most important features; and thirdly, we publish the compiled data set as a benchmark for future related works.

  7. Ethereum Fraud Dataset

    • kaggle.com
    zip
    Updated Sep 14, 2022
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Guille Escobero (2022). Ethereum Fraud Dataset [Dataset]. https://www.kaggle.com/datasets/gescobero/ethereum-fraud-dataset
    Explore at:
    zip(1733838 bytes)Available download formats
    Dataset updated
    Sep 14, 2022
    Authors
    Guille Escobero
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    Dataset

    This dataset was created by Guille Escobero

    Released under CC0: Public Domain

    Contents

  8. Ethereum Fraud Transactions

    • kaggle.com
    zip
    Updated Dec 4, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    vasavi chithanuru (2023). Ethereum Fraud Transactions [Dataset]. https://www.kaggle.com/datasets/vasavichithanuru/ethereum-fraud-transactions
    Explore at:
    zip(418081 bytes)Available download formats
    Dataset updated
    Dec 4, 2023
    Authors
    vasavi chithanuru
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Description

    Dataset

    This dataset was created by vasavi chithanuru

    Released under MIT

    Contents

  9. f

    The comparison between (smart contract)-based delegate contract signing and...

    • figshare.com
    xls
    Updated Jun 6, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Wei Xiong; Yangcheng Hu (2023). The comparison between (smart contract)-based delegate contract signing and traditional delegate contract signing. [Dataset]. http://doi.org/10.1371/journal.pone.0273424.t001
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 6, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Wei Xiong; Yangcheng Hu
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The comparison between (smart contract)-based delegate contract signing and traditional delegate contract signing.

  10. D

    Smart Contracts Market Report | Global Forecast From 2023 To 2032

    • dataintelo.com
    csv, pdf, pptx
    Updated Sep 8, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Dataintelo (2023). Smart Contracts Market Report | Global Forecast From 2023 To 2032 [Dataset]. https://dataintelo.com/report/smart-contracts-market
    Explore at:
    csv, pdf, pptxAvailable download formats
    Dataset updated
    Sep 8, 2023
    Dataset authored and provided by
    Dataintelo
    License

    https://dataintelo.com/privacy-and-policyhttps://dataintelo.com/privacy-and-policy

    Time period covered
    2024 - 2032
    Area covered
    Global
    Description

    The Global Smart Contracts Market Size is projected to grow from USD XX billion in 2021 to USD XX billion by 2028, at an estimated CAGR of 34.8% during the forecast period (2021-2028). The market is driven by factors such as the increasing need for automation, integration with various technologies and systems, and high scalability potential among others.

    Smart contracts are self-executing digital agreements that can carry out the direct transaction of assets between untrusted agents. Smart Contracts allow for formal verification, which helps to ensure there is no misbehavior and protects you from fraud. Blockchain technology enables a secure way to create these contracts without any third-party interference directly between peers.

    On the basis of Type, the market is segmented into Bitcoin, Sidechains, NXT, and Ethereum.


    Bitcoin:

    Bitcoin is a decentralized peer-to-peer Digital Currency that enables instant payments to anyone, anywhere in the world. Bitcoin uses blockchain technology to record its transactions. Blockchain is a public ledger of all Bitcoin transactions.


    Sidechains:

    Sidechains are decentralized, peer-to-peer networks that provide connectivity between different blockchains.


    NXT:

    NXT is an open-source Cryptocurrency and payment network that uses proof of stake to reach a consensus for transactions. NXT was conceived by BCNext, whose goal was to create a platform on which decentralized applications could be built in the future.


    Ethereum:

    Ethereum is a decentralized platform that runs "smart contracts" or self-executing transactions, which do not rely on any central server for processing. These smart contracts run on the Ethereum Virtual Machine (EVM) and can be written in Solidity language, Serpent language, and LLL programming languages.

    On the basis of Application, the market is segmented into Banking, Government, Management, Supply Chain, Automobile, Real Estate, Insurance, and Healthcare.


    Banking:

    The use of smart contracts in banking can dramatically reduce the time and cost associated with financial transactions. The implementation of Smart Contracts by banks allows for improved processes such as reconciliation, settlement, identity management, regulatory compliance, and trading surveillance.


    Government:

    Smart Contracts can be used in Government to make the process more transparent and efficient.


    Management:

    In management, smart contracts are used to ensure the security and integrity of critical business processes. It is a digital alternative to traditional contract law. With this technology, contractual agreements can be executed automatically without human intervention while ensuring transparency for all concerned parties.


    Supply Chain:

    The use of Smart Contracts in the Supply Chain is to track and monitor requirements, inventory levels, production schedules, shipments, and more. This will also help with fulfillment management as a whole for both suppliers and customers involved in the process.


    Automobile:

    Smart contracts in the automotive industry are generally utilized to facilitate payments between two parties. Smart contracts also provide a safe and secure method for making payments without any additional transaction fees or intermediary involvement by banks, government authorities, etc., which can be both time-consuming and costly.


    Real Estate:

    The use of Smart Contracts in Real Estate is for creating a record of the terms and conditions that are to be followed while executing real estate transactions. They ensure security, accuracy, transparency, and speed with their recording procedure which includes Digital Signatures from all concerned parties at every stage.


    Insurance:

    The use of smart contracts in Insurance can provide a higher level of automation by facilitating policy underwriting, claims processing, and payments. This will reduce delays between all parties involved.


    Healthcare:

    In healthcare, smart contacts for payment and adherence to treatment protocols can potentially reduce costs. It is a secure method of transmitting patient data allowing doctors immediate access to their records while minimizing risks associated with storing information in multiple locations.

    On the basis of Region, the market is segmented into North Amer

  11. Dataset-Fraud-ETH-Cleaned

    • kaggle.com
    zip
    Updated Apr 19, 2021
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Pablo Garcia Carreira (2021). Dataset-Fraud-ETH-Cleaned [Dataset]. https://www.kaggle.com/datasets/sibeliuspgc/datasetfraudethcleaned
    Explore at:
    zip(595325 bytes)Available download formats
    Dataset updated
    Apr 19, 2021
    Authors
    Pablo Garcia Carreira
    Description

    Dataset

    This dataset was created by Pablo Garcia Carreira

    Contents

  12. Ethereum Block data

    • kaggle.com
    zip
    Updated Jun 25, 2021
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Mohd Abdul Azeem (2021). Ethereum Block data [Dataset]. https://www.kaggle.com/muhammedabdulazeem/ethereum-block-data
    Explore at:
    zip(1157439259 bytes)Available download formats
    Dataset updated
    Jun 25, 2021
    Authors
    Mohd Abdul Azeem
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    About the data.

    Dataset contains 8.5M+ Ethereum block data. Ethereum is an open-source, blockchain-based, decentralized software platform used for its own cryptocurrency, ether. It enables Smart-Contracts and Distributed Applications (ĐApps) to be built and run without any downtime, fraud, control, or interference from a third party. To learn more about ethereum, visit here.

    Data

    Dataset contains columns such as - Block Height - Block Hash - Block created timestamp - Miner details - Block size - Block Reward - Total Transactions in that block - Gas Limit and many more.

    To get the scraping script of this data, go to my github link provided below

    Github

  13. D

    Mortgage Lender Market Size , Share Research Report |2032

    • dataintelo.com
    csv, pdf, pptx
    Updated Mar 7, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Dataintelo (2024). Mortgage Lender Market Size , Share Research Report |2032 [Dataset]. https://dataintelo.com/report/mortgage-lender-market
    Explore at:
    pdf, csv, pptxAvailable download formats
    Dataset updated
    Mar 7, 2024
    Dataset authored and provided by
    Dataintelo
    License

    https://dataintelo.com/privacy-and-policyhttps://dataintelo.com/privacy-and-policy

    Time period covered
    2024 - 2032
    Area covered
    Global
    Description

    Mortgage Lender Market Outlook 2032



    The global Mortgage Lender Market size was USD 1,024.6 Billion in 2023 and is projected to reach USD 3,077.9 Billion by 2032, expanding at a CAGR of 13% during 2024–2032. The market is fueled by the increasing demand for housing due to population growth and the advancement of digital mortgage platforms enhancing loan processing efficiency.



    Rising demand for efficiency and speed in mortgage processing propels the adoption of digital and automated solutions in the market. Financial technology firms have revolutionized loan processing with AI-driven algorithms, reducing approval times from weeks to mere days.



    Blockchain technology enhances transparency and security, mitigating fraud risks. Moreover, the integration of big data analytics enables lenders to offer personalized mortgage solutions, improving customer satisfaction and loyalty.





    • In February 2023, Roofstock, a premier digital platform for single-family rental properties, completed the sale of an Alabama rental through its web3 arm, Roofstock onChain, utilizing non-fungible token (NFT) technology on the Ethereum Blockchain. This transaction showcases the application of Teller Protocol's flexible DeFi lending solutions in real estate, facilitated by USDC.Homes, a marketplace powered by Teller Protocol for streamlined real estate financing.





    Increasing awareness of environmental issues drives the market toward sustainable and green financing. Lenders are introducing eco-friendly mortgage products that offer lower interest rates for energy-efficient homes, encouraging sustainable development.



    Government incentives for green buildings further fuel this trend, making eco-conscious mortgages attractive to borrowers. This shift addresses climate change and opens new market segments for lenders focusing on sustainability.



    <span style="font-size:11pt&qu

  14. Monthly size of crypto theft 2020-2022

    • statista.com
    Updated Feb 3, 2022
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Statista (2022). Monthly size of crypto theft 2020-2022 [Dataset]. https://www.statista.com/statistics/1285057/crypto-theft-size/
    Explore at:
    Dataset updated
    Feb 3, 2022
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Feb 3, 2022
    Area covered
    Worldwide
    Description

    The value of crypto lost to security threats grew over nine times between 2020 and 2021, with one incident in August 2021 accounting for 610 million U.S. dollars stolen. During this particular incident - claimed to be one of the biggest cryptocurrency heists of all time - an individual person targeted the Ethereum-based DeFi application Poly Network after exploited a flaw in the Network's code. After Poly Network pleaded with the hacker, the anonymous hacker handed back about half of the money - 342 million U.S. dollars - claiming he did the hack "for fun".

  15. Worldwide blockchain market value share 2020, by sector

    • rtllu.org
    • statista.com
    Updated Feb 6, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Statista Research Department (2024). Worldwide blockchain market value share 2020, by sector [Dataset]. https://www.rtllu.org/?_=%2Ftopics%2F5122%2Fblockchain%2F%23KJWqMdlUlBntOaMGRBz0jpjhec9jFFI%3D
    Explore at:
    Dataset updated
    Feb 6, 2024
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Statista Research Department
    Description

    In 2020, the distribution of the global blockchain market revenue was heavily distributed towards the banking industry, which has a market share of almost 30 percent. While process manufacturing accounted for 11.4 percent of worldwide blockchain spending. Overall, the global spending on blockchain solutions is continued to grow in the upcoming years.

    Blockchain technology
    Simply put, blockchain is a distributed ledger technology, which creates assurance between trading partners, especially in trades that occur with cryptocurrency. For example, in the case of Bitcoin and Ethereum, blockchain is the technology that allows for the transfer of these cryptocurrencies, providing confidence in financial transactions. This additional confidence through the usage of blockchain comes from the reduced fraud, increased financial inclusion, and decreased costs. This leads to the simplification of cross-border payments and settlements, which has the potential to change the global banking industry as we know it.

    Blockchain and Bitcoin Blockchain and Bitcoin have a symbiotic relationship as blockchain technology was created to be a database structured into “blocks” of data that is linked, or in other words, “chained”, to other sets of data. The blockchain technology stores the Bitcoin transactions in a continuous linked structure, that continues to increase with time and each transaction. Hence, with the increased popularity of Bitcoin comes the increased importance of the growing Bitcoin blockchain, which is visible in the increased number of blockchain wallet users worldwide in the past few years alone.

  16. Blockchain Market in Supply Chain Industry Analysis North America, Europe,...

    • technavio.com
    Updated Dec 15, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Technavio (2023). Blockchain Market in Supply Chain Industry Analysis North America, Europe, APAC, South America, Middle East and Africa - US, Canada, China, UK, Germany - Size and Forecast 2024-2028 [Dataset]. https://www.technavio.com/report/blockchain-market-in-supply-chain-industry-market-industry-analysis
    Explore at:
    Dataset updated
    Dec 15, 2023
    Dataset provided by
    TechNavio
    Authors
    Technavio
    License

    https://www.technavio.com/content/privacy-noticehttps://www.technavio.com/content/privacy-notice

    Time period covered
    2021 - 2025
    Area covered
    Global
    Description

    Snapshot img

    Exploring the Lucrative Blockchain Market: Trends, Opportunities and Growth 2024-2028

    The global Blockchain Market in Supply Chain Industry is projected to reach USD 8.55 billion in 2027, with a CAGR of 53.59% between 2023 and 2028. The growth rate of the market depends on several factors, including the growing number of cargo thefts, increasing complexities due to time-bound deliveries and customization of the supply chain, and the booming e-commerce industry.

    Market Overview and Trends

        Analysis Period
        2018-2028
    
    
        Market Size (2018) Historic Year
        USD 0.21 billion
    
    
        Market Size (2028) - Forecasted Year
        USD 8.55 billion
    
    
        Historic Opportunity (2018-2022)
        USD 0.48 billion
    
    
        Historic CAGR
        34.33 %
    
    
        Forecasted Opportunity (2024-2028)
        USD 7.55 billion
    
    
        Market Opportunity Transformation Growth
        3.00 %
    
    
        Market Opportunity Capitalization
        USD 8.02 billion
    

    The blockchain market is a rapidly growing sector in the technology industry. Blockchain technology offers secure and transparent transactions without the need for intermediaries. Major industries such as finance, supply chain management, and healthcare are adopting blockchain solutions.

    Market Overview

    For More Highlights About this Report, Download Free Sample in a Minute

    Future Outlook of the Blockchain Market

    Cryptocurrencies like Bitcoin and Ethereum are built on blockchain technology and have fueled the growth of the blockchain market. By 2023, it is estimated that the blockchain-based identity management market will exceed USD1 billion, providing secure and self-sovereign digital identities for individuals. Over 60% of major banks are expected to use blockchain technology for cross-border payments by 2024. The adoption of blockchain technology in supply chain management is anticipated to reduce costs by up to 20%.

    Definition and Evolution of Blockchain Technology

    The market for blockchain in the supply chain industry refers to the use of blockchain technology to enhance and revolutionize various aspects of supply chain management. Blockchain is a decentralized and transparent digital ledger that records transactions and interactions in a secure and immutable way. In the supply chain industry, blockchain technology can be utilized to streamline and optimize processes such as tracking and tracing products, ensuring transparency and authenticity of products, improving supply chain visibility, reducing fraud, and enhancing trust among stakeholders.

    Market Growth and Forecasting

    The increasing complexities due to time-bound deliveries and customization of the supply chain are notably driving the market growth. Selecting or formulating the right supply chain model is vital and critical. This is because customers prefer shorter lead times, while logistics and supply chain companies seek to keep the operational cost as low as possible. Various products such as chemicals, clinical, pharmaceuticals, and perishable food and beverages require special care and attention, specific packaging, and a customized supply chain for sustaining the physical and chemical properties of the shipped product.

    In addition, the supply chain complexities vary with rural infrastructure based on topography, technology advances, and region/country-specific regulations and policies. For instance, the supply chain of pharmaceutical logistics in rural areas is different compared to that in urban areas. Rural areas lack logistical and technological infrastructure. Therefore, companies and end-user industries are availing blockchain technology to streamline their business operations and maintain high efficiency and agility in the supply chain. Hence, these factors are expected to drive market growth during the forecast period.

    Industries Benefiting from Blockchain Adoption

    The blockchain technology market has witnessed significant growth, driven by increased investments and the adoption of blockchain by various institutions. The transformative potential of blockchain extends to diverse industries, providing tangible benefits such as enhanced security, transparency, and efficiency. Notably, blockchain fosters innovation in financial transactions, allowing seamless transfer of money. Small and medium-sized enterprises (SMEs) leverage blockchain's decentralized database for improved operations. The adoption of blockchain extends to various use cases, including the creation of a secure and decentralized network.

    Market Trends and Analysis

    The emergence of blockchain-as-a-service is the key trend in the market. Blockchain-as-a-service is defined as a service in which companies set up the blockchain-connected nodes on the enterprise's behalf and also manage it at the back end. The growing evolution of the blockchain-as-a-service is encouraging companies through its subsidiary Amazon Web Services (AWS), and SAP to invest in the tec

  17. D

    Blockchain Supplychain Market Report | Global Forecast From 2023 To 2032

    • dataintelo.com
    csv, pdf, pptx
    Updated Apr 15, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Dataintelo (2023). Blockchain Supplychain Market Report | Global Forecast From 2023 To 2032 [Dataset]. https://dataintelo.com/report/global-blockchain-supplychain-market
    Explore at:
    pdf, pptx, csvAvailable download formats
    Dataset updated
    Apr 15, 2023
    Dataset authored and provided by
    Dataintelo
    License

    https://dataintelo.com/privacy-and-policyhttps://dataintelo.com/privacy-and-policy

    Time period covered
    2024 - 2032
    Area covered
    Global
    Description

    The Global Blockchain Supplychain Market size is expected to grow from USD 6.21 Billion in 2021 to USD XX Billion by 2028, at a CAGR of 28.7% during the forecast period. The growth of this market can be attributed to the increasing demand for distributed ledger technology across industries, rising fraudulent activities across supply chains, and the growing need for transparency and immutability in supply chains.

    The blockchain supply chain is the application of blockchain technology in the field of supply chain management. The blockchain supply chain provides a secure and transparent way to track and manage goods throughout the entire supply-chain process. It can be used to record any type of information related to the supply-chain process, such as shipment tracking, product history, quality control, and more. The blockchain-based supply chain offers transparency, security, and efficiency to businesses.

    On the basis of Type, the market is segmented into Public Blockchain, Private Blockchain, and Consortium Blockchain. The Public Blockchain segment is expected to hold the largest market share during the forecast period.


    Public Blockchain:

    A public blockchain is a distributed ledger technology that enables anyone to read or write to the ledger. Transactions on a public blockchain are transparent and can be verified by anyone. Public blockchains are typically permissionless, meaning anyone can participate in the network without having to ask for permission. Bitcoin and Ethereum are two examples of public blockchains. Public blockchains have several advantages over private blockchains. First, they are more secure because they have more nodes (computers) verifying transactions. Second, they are more decentralized, meaning there is no one party controlling the network. This makes them less vulnerable to censorship or attacks.


    Private Blockchain:

    A private blockchain is a permissioned network where only authorized nodes can participate in the network. These nodes are typically known to each other and trust is established among them. The transactions on a private blockchain are verified by a set of trusted nodes instead of using a Proof-of-Work (PoW) algorithm. Private blockchains are ideal for businesses that want to keep their data private and secure.


    Consortium Blockchain:

    A consortium blockchain is a blockchain where the nodes or participants are restricted to a certain group. Consortium blockchains are somewhere in between, with some restrictions on who can join, but not as tight as private blockchains. The consortium members could be companies, organizations, or individuals that have come together for a specific task or business goal.

    On the basis of Application, the market is segmented into Retail, Oil & Gas, Healthcare, IT & Telecom, and Others. The Retail segment is expected to hold the largest market share during the forecast period.


    Retail:

    Blockchain technology is being used in the retail sector for a number of reasons. These include tracking the movement of products, preventing counterfeiting, and improving customer experiences. In particular, retailers are interested in the use of blockchain to create transparent and secure supply chains. This allows customers to trust that they are getting what they paid for and that the products they purchase have not been tampered with. Additionally, by reducing waste and improving inventory management, blockchain can help retailers save money. Some companies are already implementing these applications and seeing positive results.


    Oil & Gas:

    Oil and gas are one of the most important industrial sectors in the world. Blockchain technology has a lot to offer in this sector as well. Some of the possible uses of blockchain supply chain in oil and gas are Tracking and tracing of oil products throughout the supply chain; Preventing fraud by tracking product origins; Improved security due to immutable ledger; Reduced administrative costs; Real-time monitoring of supplier performance; Enhanced communication between producers and consumers.


    Healthcare:

    The healthcare sector is one of the most important sectors that is looking to adopt blockchain technology. The reason for this is that the healthcare sector is rife with inefficiencies and problems that can be solved with blockchain technology. Some of the areas where blockchain supply chain can be used in the healthcare sector include Tracking Drugs: One of the biggest problems in the pharmaceutical industry is counterfeit drugs. The blockchain supply chain can help track drugs from manufact

  18. Ethereum Historical Data

    • kaggle.com
    zip
    Updated Oct 6, 2018
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    LiamLarsen (2018). Ethereum Historical Data [Dataset]. https://www.kaggle.com/datasets/kingburrito666/ethereum-historical-data
    Explore at:
    zip(207070 bytes)Available download formats
    Dataset updated
    Oct 6, 2018
    Authors
    LiamLarsen
    Description

    Context

    The Ethereum blockchain gives a revolutionary way of decentralized applications and provides its own cryptocurrency. Ethereum is a decentralized platform that runs smart contracts: applications that run exactly as programmed without any possibility of downtime, censorship, fraud or third party interference. These apps run on a custom built blockchain, an enormously powerful shared global infrastructure that can move value around and represent the ownership of property. This enables developers to create markets, store registries of debts or promises, move funds in accordance with instructions given long in the past (like a will or a futures contract) and many other things that have not been invented yet, all without a middle man or counterparty risk.

    Content

    What you may see in the CSVs are just numbers, but there is more to this. Numbers make machine learning easy. I've labeled each column, the first in all of them is the day; it may look weird but it makes sense if you look closely.

    Note:

    TIMESTAMP FORMAT

    How to convert timestamp in python:

    import datetime as dt
    # The (would-be) timestamp value is below
    timestamp = 1339521878.04 
    # Technechly you would iterate through and change them all if you were graphing
    timeValue = dt.datetime.fromtimestamp(timestamp)
    #Year, month, day, hour, minute, second
    print(timeValue.strftime('%Y-%m-%d %H:%M:%S'))
    

    Acknowledgements

    MR. Vitalik Buterin. co-founder of Ethereum and as a co-founder of Bitcoin Magazine.

    Hit a brother up

    0x767e8b211f70c5b8b4caa38c2efe05bf8eac0da7

    Will be updating every month with new Ethereum history!

  19. Ethereum Historical Dataset

    • kaggle.com
    zip
    Updated Apr 16, 2020
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    prasoon kottarathil (2020). Ethereum Historical Dataset [Dataset]. https://www.kaggle.com/prasoonkottarathil/ethereum-historical-dataset
    Explore at:
    zip(22396334 bytes)Available download formats
    Dataset updated
    Apr 16, 2020
    Authors
    prasoon kottarathil
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    If you reach this DATASET, please UPVOTE this dataset to show your appreciation

    Ethereum (ETH) is a smart contract platform that enables developers to build decentralized applications (DAPs) conceptualized in Vitalik Buterin 2013. ETH is the local currency of the Ethereum platform and also serves as a transaction fee for miners in the Ethereum network.

    Ethereum is the pioneer of blockchain-based smart contracts. Smart contract becomes a self-operating computer program when running on the blockchain, which automatically executes when certain conditions are met. On the blockchain, smart contracts allow the code to be programmed without the possibility of useless time, censorship, fraud or third party interference. It facilitates the conversion of money, content, property, shares or anything valuable. Ethereum Network went live on July 30, 2015 with 72 million Ethereum Premium.

    https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2533028%2F776eb982d1ce67447534edb8325b5540%2FOpera%20Snapshot_2020-04-16_121111_coinmarketcap.com.png?generation=1587019388673274&alt=media" alt="">

    • Previous Close 152.355
    • Open 152.355
    • All Time High $1,432.88 USD (Jan 13, 2018)
    • 52 Week Range 150.454 - 155.033
    • Start Date 2015-08-07
    • Algorithm PoW
    • Market Cap 17.13B
    • Circulating Supply 109,303,536 ETH
    • Max Supply NA
    • Volume 14,770,172,928
    • Volume (24hr) 14.73B

    I encourage you to use this Dataset to start your own projects. If you do, please cite the Dataset: author = {Prasoon Kottarathil}, title = {Ethereum Historical Dataset}, year = {2020}, publisher = {kaggle}, journal = {Kaggle Dataset}, how published = {\url{https://www.kaggle.com/prasoonkottarathil/ethereum-historical-dataset}}

  20. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2021). ‘Ethereum Fraud Detection Dataset’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/kaggle-ethereum-fraud-detection-dataset-d749/dcfcefc9/?iid=039-335&v=presentation

‘Ethereum Fraud Detection Dataset’ analyzed by Analyst-2

Explore at:
Dataset updated
Nov 15, 2021
Dataset authored and provided by
Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description

Analysis of ‘Ethereum Fraud Detection Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/vagifa/ethereum-frauddetection-dataset on 13 November 2021.

--- Dataset description provided by original source is as follows ---

Context

This dataset contains rows of known fraud and valid transactions made over Ethereum, a type of cryptocurrency. This dataset is imbalanced, so keep that in mind when modelling

Content

Here is a description of the rows of the dataset:

  • Index: the index number of a row
  • Address: the address of the ethereum account
  • FLAG: whether the transaction is fraud or not
  • Avg min between sent tnx: Average time between sent transactions for account in minutes
  • Avg_min_between_received_tnx: Average time between received transactions for account in minutes
  • Time_Diff_between_first_and_last(Mins): Time difference between the first and last transaction
  • Sent_tnx: Total number of sent normal transactions
  • Received_tnx: Total number of received normal transactions
  • Number_of_Created_Contracts: Total Number of created contract transactions
  • Unique_Received_From_Addresses: Total Unique addresses from which account received transactions
  • Unique_Sent_To_Addresses20: Total Unique addresses from which account sent transactions
  • Min_Value_Received: Minimum value in Ether ever received
  • Max_Value_Received: Maximum value in Ether ever received
  • Avg_Value_Received5Average value in Ether ever received
  • Min_Val_Sent: Minimum value of Ether ever sent
  • Max_Val_Sent: Maximum value of Ether ever sent
  • Avg_Val_Sent: Average value of Ether ever sent
  • Min_Value_Sent_To_Contract: Minimum value of Ether sent to a contract
  • Max_Value_Sent_To_Contract: Maximum value of Ether sent to a contract
  • Avg_Value_Sent_To_Contract: Average value of Ether sent to contracts
  • Total_Transactions(Including_Tnx_to_Create_Contract): Total number of transactions

  • Total_Ether_Sent:Total Ether sent for account address

  • Total_Ether_Received: Total Ether received for account address

  • Total_Ether_Sent_Contracts: Total Ether sent to Contract addresses

  • Total_Ether_Balance: Total Ether Balance following enacted transactions

  • Total_ERC20_Tnxs: Total number of ERC20 token transfer transactions

  • ERC20_Total_Ether_Received: Total ERC20 token received transactions in Ether

  • ERC20_Total_Ether_Sent: Total ERC20token sent transactions in Ether

  • ERC20_Total_Ether_Sent_Contract: Total ERC20 token transfer to other contracts in Ether

  • ERC20_Uniq_Sent_Addr: Number of ERC20 token transactions sent to Unique account addresses

  • ERC20_Uniq_Rec_Addr: Number of ERC20 token transactions received from Unique addresses

  • ERC20_Uniq_Rec_Contract_Addr: Number of ERC20token transactions received from Unique contract addresses

  • ERC20_Avg_Time_Between_Sent_Tnx: Average time between ERC20 token sent transactions in minutes

  • ERC20_Avg_Time_Between_Rec_Tnx: Average time between ERC20 token received transactions in minutes

  • ERC20_Avg_Time_Between_Contract_Tnx: Average time ERC20 token between sent token transactions

  • ERC20_Min_Val_Rec: Minimum value in Ether received from ERC20 token transactions for account

  • ERC20_Max_Val_Rec: Maximum value in Ether received from ERC20 token transactions for account

  • ERC20_Avg_Val_Rec: Average value in Ether received from ERC20 token transactions for account

  • ERC20_Min_Val_Sent: Minimum value in Ether sent from ERC20 token transactions for account

  • ERC20_Max_Val_Sent: Maximum value in Ether sent from ERC20 token transactions for account

  • ERC20_Avg_Val_Sent: Average value in Ether sent from ERC20 token transactions for account

  • ERC20_Uniq_Sent_Token_Name: Number of Unique ERC20 tokens transferred

  • ERC20_Uniq_Rec_Token_Name: Number of Unique ERC20 tokens received

  • ERC20_Most_Sent_Token_Type: Most sent token for account via ERC20 transaction

  • ERC20_Most_Rec_Token_Type: Most received token for account via ERC20 transactions

--- Original source retains full ownership of the source dataset ---

Search
Clear search
Close search
Google apps
Main menu