Details of fraud referrals relating to war pensions & compensation

The Corporate Financial Fraud project is a study of company and top-executive characteristics of firms that ultimately violated Securities and Exchange Commission (SEC) financial accounting and securities fraud provisions compared to a sample of public companies that did not. The fraud firm sample was identified through systematic review of SEC accounting enforcement releases from 2005-2010, which included administrative and civil actions, and referrals for criminal prosecution that were identified through mentions in enforcement release, indictments, and news searches. The non-fraud firms were randomly selected from among nearly 10,000 US public companies censused and active during at least one year between 2005-2010 in Standard and Poor's Compustat data. The Company and Top-Executive (CEO) databases combine information from numerous publicly available sources, many in raw form that were hand-coded (e.g., for fraud firms: Accounting and Auditing Enforcement Releases (AAER) enforcement releases, investigation summaries, SEC-filed complaints, litigation proceedings and case outcomes). Financial and structural information on companies for the year leading up to the financial fraud (or around year 2000 for non-fraud firms) was collected from Compustat financial statement data on Form 10-Ks, and supplemented by hand-collected data from original company 10-Ks, proxy statements, or other financial reports accessed via Electronic Data Gathering, Analysis, and Retrieval (EDGAR), SEC's data-gathering search tool. For CEOs, data on personal background characteristics were collected from Execucomp and BoardEx databases, supplemented by hand-collection from proxy-statement biographies.

The data contains information on food fraud and was used to predict food fraud type using a Bayesian Network model. Food fraud notifications for the period 2000-2014 were downloaded from the Rapid Alert System for Food and Feed (RASFF) database. Each record contains detailed information on the kind of notification and the products and countries involved. Based on the description in each notification we added a variable "food fraud type" (i.e. six different types of food fraud). A set of 749 notifications for the years 2000-2013 was used to train a Bayesian Network model to predict food fraud type. This model was validated using the 88 notifications for the year 2014.

Interpretation of the data and details on the performance of the BN model can be found in the research article titled “Prediction of food fraud type using data from Rapid Alert System for Food and Feed (RASFF) and Bayesian network modelling” https://doi.org/10.1016/j.foodcont.2015.09.026

Column names

year - year notification was made

product - categorization of the different products

notification - categorization of the notifications

notified - country that made the notification

origin - country where the product originated from

fraud - classification of fraud type

The focus of this project was insider fraud -- crimes committed by the owners and operators of insurance companies that were established for the purposes of defrauding businesses and employees. The quantitative data for this collection were taken from a database maintained by the National Association of Insurance Commissioners (NAIC), an organization that represents state insurance departments collectively and acts as a clearinghouse for information obtained from individual departments. Created in 1988, the Regulatory Information Retrieval System (RIRS) database contains information on actions taken by state insurance departments against individuals and firms, including cease and desist orders, license revocations, fines, and penalties imposed. Data available for this project include a total of 123 actions taken against firms labeled as Multiple Employer Welfare Arrangements or Multiple Employer Trusts (MEWA/MET) in the RIRS database. Variables available in this data collection include the date action was taken, state where action was taken, dollar amount of the penalty imposed in the action, and disposition for action taken.

The Organic INTEGRITY Database is a certified organic operations database that contains up-to-date and accurate information about operations that may and may not sell as organic, deterring fraud, increases supply chain transparency for buyers and sellers, and promotes market visibility for organic operations. Only certified operations can sell, label, or represent products as organic, unless exempt or excluded from certification. The INTEGRITY database improves access to certified organic operation information by giving industry and public users an easier way to search for data with greater precision than the formerly posted Annual Lists of Certified Operations. You can find a certified organic farm or business, or search for an operation with specific characteristics such as:

The status of an operation: Certified, Surrendered, Revoked, or Suspended The scopes for which an operation is certified: Crops, Livestock, Wild Crops, or Handling

The organic commodities and services that operations offer. A new, more structured classification system (sample provided) will help you find more of what you’re looking for and details about the flexible taxonomy can be found in the INTEGRITY Categories and Items list. Resources in this dataset:Resource Title: Organic INTEGRITY Database. File Name: Web Page, url: https://organic.ams.usda.gov/integrity/Default.aspx Find a specific certified organic farm or business, or search for an operation with specific characteristics. Listings come from USDA-Accredited Certifying Agents. Historical Annual Lists of Certified Organic Operations and monthly snapshots of the full data set are available for download on the Data History page. Only certified operations can sell, label or represent products as organic, unless exempt or excluded from certification.

Database of allegations of fraud and dispositions of those allegations that warrant further investigation.

🇬🇧 영국

The global credit risk database market is experiencing robust growth, driven by the increasing need for accurate and timely credit risk assessment across diverse sectors. The market's expansion is fueled by several key factors, including the rising adoption of digital technologies in lending and credit underwriting, the growing complexity of financial regulations demanding more sophisticated risk management strategies, and the increasing prevalence of fraud and credit defaults. This necessitates comprehensive credit risk databases offering detailed information on individuals and businesses, enabling financial institutions and other stakeholders to make informed decisions, minimize losses, and optimize their credit portfolios. The market is segmented by database type (consumer, commercial, etc.), deployment model (cloud-based, on-premise), and end-user (banks, insurance companies, etc.). Key players are actively investing in advanced analytics, machine learning, and data enrichment capabilities to enhance the accuracy and predictive power of their credit risk databases, further driving market expansion. Competition is intensifying, with companies focusing on strategic partnerships, acquisitions, and technological innovation to maintain a competitive edge. The forecast period (2025-2033) anticipates continued growth, fueled by burgeoning adoption of sophisticated credit scoring models and the expansion of fintech companies leveraging these databases for lending and other financial services. Regulatory changes impacting credit reporting and data privacy are likely to shape the market landscape, necessitating compliance and adaptation among database providers. While challenges such as data security concerns and the cost of data acquisition and maintenance persist, the overall market outlook remains positive, with substantial growth potential across various geographic regions, particularly in emerging economies experiencing rapid economic development and financial sector expansion. Estimating a reasonable market size requires making assumptions about the provided CAGR and the market's initial value. Let's assume a base year (2025) market size of $5 billion and a CAGR of 12% for illustration purposes. This would indicate significant growth over the forecast period.

The Tax and Money Laundering Database was made as part of the Horizon 2020 project COFFERS, it took almost two years to make, from data collection planning and methodology to actual data collection and then verification of data and sources. The legal database gathers legislation of all European Union Member States regarding tax evasion and money laundering, as well as other relevant legal variables such as legal origins of each jurisdictions’ legislation

Details of debt management, recoveries, write-offs and casework.

Background: Increasing progress is being made in the field of accounting fraud, and extensive theoretical research is needed to develop future research topics using trend analysis. Our research consists of a literature review that examines the most common fraud theories and attempts to interpret the characteristics of human behaviour that lead to fraud as well as current methods of detecting corporate fraud. Methods: We searched the Scopus database for articles on fraud theory. We analyse articles published between 2004 and 2022, using a keyword search for ‘Fraud Triangle’, ‘Fraud Diamond’, ‘Fraud Pentagon’, and ‘Fraud Hexagon’. Furthermore, we include all document types like articles, conference papers, reviews, book chapters, conference reviews, notes, and data papers. The investigation was limited to papers published in English from 2004 to 2022, not including the current year 2023, as documents are still being published. The last research was done at the end of January 2023. The results from the above criteria are to collect 302 papers. We used VOS program viewer in our bibliometric analysis. Results: According to our network analysis, the Fraud Diamond theory seems to be the most functional fraud theory. According to our findings of the published articles, the main human behavioural characteristics that can lead a manager to commit fraud are the components of the fraud diamond theory: capability - opportunity - pressure - rationalization. Thus, the fraud diamond theory analyses more than the fraud triangle, pentagon, and hexagon theories. So human behavioural characteristics have a positive effect and can lead to fraud in companies. Conclusions: Future research needs to analyse more Pentagon and Hexagon fraud theories, which are more recent and have not yet been analysed in detail. Also, future research needs to analyse more of the human behaviour characteristics related to the Pentagon and Hexagon fraud theories. The deposited collection contains a CVS file from the Scopus Database with published articles from 2004 to 2022.

Dataset

This dataset was created by Binayatosh Panigrahi

Released under Database: Open Database, Contents: © Original Authors

Contents

Details of debt management, recoveries, write-offs and casework.

Global GPU Database Market size is set to expand from $ 509.36 Million in 2023 to $ 2368.35 Million by 2032, with an anticipated CAGR of around 18.62% from 2024 to 2032.

Hungary Fraud Attempts: Volume data was reported at 32.000 Unit in Dec 2019. This records an increase from the previous number of 12.000 Unit for Sep 2019. Hungary Fraud Attempts: Volume data is updated quarterly, averaging 68.000 Unit from Mar 2010 (Median) to Dec 2019, with 40 observations. The data reached an all-time high of 243.000 Unit in Mar 2013 and a record low of 12.000 Unit in Sep 2019. Hungary Fraud Attempts: Volume data remains active status in CEIC and is reported by National Bank of Hungary. The data is categorized under Global Database’s Hungary – Table HU.KA013: Card and Electronic Payment Frauds.

This competition involves advertisement data provided by BuzzCity Pte. Ltd. BuzzCity is a global mobile advertising network that has millions of consumers around the world on mobile phones and devices. In Q1 2012, over 45 billion ad banners were delivered across the BuzzCity network consisting of more than 10,000 publisher sites which reach an average of over 300 million unique users per month. The number of smartphones active on the network has also grown significantly. Smartphones now account for more than 32% phones that are served advertisements across the BuzzCity network. The "raw" data used in this competition has two types: publisher database and click database, both provided in CSV format. The publisher database records the publisher's (aka partner's) profile and comprises several fields:

publisherid - Unique identifier of a publisher. Bankaccount - Bank account associated with a publisher (may be empty) address - Mailing address of a publisher (obfuscated; may be empty) status - Label of a publisher, which can be the following: "OK" - Publishers whom BuzzCity deems as having healthy traffic (or those who slipped their detection mechanisms) "Observation" - Publishers who may have just started their traffic or their traffic statistics deviates from system wide average. BuzzCity does not have any conclusive stand with these publishers yet "Fraud" - Publishers who are deemed as fraudulent with clear proof. Buzzcity suspends their accounts and their earnings will not be paid

On the other hand, the click database records the click traffics and has several fields:

id - Unique identifier of a particular click numericip - Public IP address of a clicker/visitor deviceua - Phone model used by a clicker/visitor publisherid - Unique identifier of a publisher adscampaignid - Unique identifier of a given advertisement campaign usercountry - Country from which the surfer is clicktime - Timestamp of a given click (in YYYY-MM-DD format) publisherchannel - Publisher's channel type, which can be the following: ad - Adult sites co - Community es - Entertainment and lifestyle gd - Glamour and dating in - Information mc - Mobile content pp - Premium portal se - Search, portal, services referredurl - URL where the ad banners were clicked (obfuscated; may be empty). More details about the HTTP Referer protocol can be found in this article. Related Publication: R. J. Oentaryo, E.-P. Lim, M. Finegold, D. Lo, F.-D. Zhu, C. Phua, E.-Y. Cheu, G.-E. Yap, K. Sim, M. N. Nguyen, K. Perera, B. Neupane, M. Faisal, Z.-Y. Aung, W. L. Woon, W. Chen, D. Patel, and D. Berrar. (2014). Detecting click fraud in online advertising: A data mining approach, Journal of Machine Learning Research, 15, 99-140.

The Canadian Anti-Fraud Centre's fraud and identity crime reports are contained within their Fraud Reporting System database. The data is acquired from total public reports, online reports are created by the public entering information to populate their individual reports. The accuracy of a fraud report is largely dependent on the individual submitting the information. Individuals submitting reports can choose to include as much or as little information as they deem necessary. Nonetheless, the Canadian Anti-Fraud Centre intake analysts review all submitted reports to determine accuracy of submitted information.

The data is sourced from the CSMAR database, covering violation records of companies listed on the Shanghai and Shenzhen stock exchanges from 2015 to 2020, focusing on five types of financial fraud: fictitious profits, inflated assets, false records, material omissions, and inaccurate disclosures. After excluding financial firms, the fraud sample set includes 2,652 violation records from 1,226 companies. Additionally, 2,652 high-quality companies without fraud were selected from the CNRDS ESG rating database to form the non-fraud sample set. The dataset consists of two parts: 1) Structured data: The file "financial fraud dataset (structured data).xlsx" contains 5,304 records covering 43 fields, such as basic company information, financial indicators, structural indicators, and linguistic features of annual report texts. Field names are listed in Table 1. 2) Annual report text data: The folder named "Annual report text data" includes 2,652 fraud samples (file names formatted as Symbol-Year.txt) and 2,652 non-fraud samples (same format). The files contain the MD&A sections of listed companies' annual reports.

Graph Database Market size was valued at USD 2.86 Billion in 2024 and is projected to reach USD 14.58 Billion by 2032, growing at a CAGR of 22.6% from 2026 to 2032.

Global Graph Database Market Drivers

The growth and development of the Graph Database Market is attributed to certain main market drivers. These factors have a big impact on how Graph Database are demanded and adopted in different sectors. Several of the major market forces are as follows:

Growth of Connected Data: Graph databases are excellent at expressing and querying relationships as businesses work with datasets that are more complex and interconnected. Graph databases are becoming more and more in demand as connected data gains significance across multiple industries.

Knowledge Graph Emergence: In fields like artificial intelligence, machine learning, and data analytics, knowledge graphs—which arrange information in a graph structure—are becoming more and more popular. Knowledge graphs can only be created and queried via graph databases, which is what is causing their widespread use.

Analytics and Machine Learning Advancements: Graph databases handle relationships and patterns in data effectively, enabling applications related to advanced analytics and machine learning. Graph databases are becoming more and more in demand when combined with analytics and machine learning as businesses want to extract more insights from their data.

Real-Time Data Processing: Graph databases can process data in real-time, which makes them appropriate for applications that need quick answers and insights. In situations like fraud detection, recommendation systems, and network analysis, this is especially helpful.

Increasing Need for Security and Fraud Detection: Graph databases are useful for fraud security and detection applications because they can identify patterns and abnormalities in linked data. The growing need for graph databases in security solutions is a result of the ongoing evolution of cybersecurity threats.

DomainIQ is a comprehensive global Domain Name dataset for organizations that want to build cyber security, data cleaning and email marketing applications. The dataset consists of the DNS records for over 267 million domains, updated daily, representing more than 90% of all public domains in the world.

The data is enriched by over thirty unique data points, including identifying the mailbox provider for each domain and using AI based predictive analytics to identify elevated risk domains from both a cyber security and email sending reputation perspective.

DomainIQ from Datazag offers layered intelligence through a highly flexible API and as a dataset, available for both cloud and on-premises applications. Standard formats include CSV, JSON, Parquet, and DuckDB.

Custom options are available for any other file or database format. With daily updates and constant research from Datazag, organizations can develop their own market leading cyber security, data cleaning and email marketing applications supported by comprehensive and accurate data from Datazag. Data updates available on a daily, weekly and monthly basis. API data is updated on a daily basis.