In 2024/25, there were ***** thefts by an employee offences reported in England and Wales, compared with ***** in the previous reporting year. The peak for this type of crime in the provided time period was 2003/04, when there were ****** employee theft offences.

This statistic shows the median loss through employee theft in the United States in 2016, based on the company size. In 2016, median losses through employee theft for companies with fewer than *** employees totaled to ******* U.S. dollars.

This statistic shows the median loss due to employee theft in the United States in 2016, based on scheme type. In 2016, median losses for companies which are victims of vendor fraud totaled to about *** million U.S. dollars.

The global employee identity theft protection software market size was valued at USD 592.7 million in 2022 and is projected to reach USD 1,553.9 million by 2030, exhibiting a CAGR of 10.1% during the forecast period. The increasing prevalence of identity theft and the growing number of data breaches are driving the growth of the market. The market is segmented by type into cloud-based and on-premises. The cloud-based segment holds a larger market share due to its scalability, flexibility, and cost-effectiveness. By application, the market is divided into large enterprises and SMEs. Large enterprises typically have more employees and sensitive data to protect, making them a key target market for employee identity theft protection software providers. The market is also segmented by region into North America, Europe, Asia Pacific, Latin America, and the Middle East and Africa. North America holds the largest market share due to the high awareness of identity theft and the presence of a large number of large enterprises in the region. Overview Employee identity theft protection software empowers organizations to safeguard the personal information of their employees, reducing the risk of fraud and data breaches. This report provides comprehensive insights into the global employee identity theft protection software market, covering market dynamics, growth drivers, challenges, and key players.

The Uniform Crime Reporting Program Data, Police Employee Data, 2013 file contains monthly data on felonious or accidental killings and assaults upon United States law enforcement officers acting in the line of duty. The Federal Bureau of Investigation (FBI) assembled the data and processed them from UCR Master Police Employee (LEOKA) data tapes. Each agency record in the file includes the following summary variables: state code, population group code, geographic division, Metropolitan Statistical Area code, and agency name. These variables afford considerable flexibility in creating subsets or aggregations of the data. Since 1930, the Federal Bureau of Investigation has compiled the Uniform Crime Reports (UCR) to serve as a periodic nationwide assessment of reported crimes not available elsewhere in the criminal justice system. Each year, this information is reported in four types of files: (1) Offenses Known and Clearances by Arrest, (2) Property Stolen and Recovered, (3) Supplementary Homicide Reports (SHR), and (4) Police Employee (LEOKA) Data. The Police Employee (LEOKA) Data provide information about law enforcement officers killed or assaulted (hence the acronym, LEOKA) in the line of duty. The variables created from the LEOKA forms provide in-depth information on the circumstances surrounding killings or assaults, including type of call answered, type of weapon used, and type of patrol the officers were on.

Investigator(s): Bureau of Justice Statistics These data collections present public expenditure and employment data pertaining to criminal justice activities in the United States. The data were collected by the U.S. Bureau of the Census for the Bureau of Justice Statistics. Information on employment, payroll, and expenditures is provided for police, courts, prosecutors' offices, and corrections agencies. Specific variables include identification of each government, number of full- and part-time employees, level of full- and part-time payroll, current expenditures, capital outlay, and intergovernmental expenditures. Years Produced: Annually Related Data Longitudinal File (ICPSR 7636, ICPSR 7618) Individual Units File and Estimates File (ICPSR 9446, ICPSR 8650)

Since 1930, the Federal Bureau of Investigation has compiled the Uniform Crime Reports (UCR) to serve as a periodic nationwide assessment of reported crimes not available elsewhere in the criminal justice system. Each year, this information is reported in four types of files: (1) Offenses Known and Clearances by Arrest, (2) Property Stolen and Recovered, (3) Supplementary Homicide Reports (SHR), and (4) Police Employee (LEOKA) Data. The Police Employee (LEOKA) Data provide information about law enforcement officers killed or assaulted (hence the acronym, LEOKA) in the line of duty. The variables created from the LEOKA forms provide in-depth information on the circumstances surrounding killings or assaults, including type of call answered, type of weapon used, and type of patrol the officers were on.

Context

The Uniform Crime Reporting (UCR) Program has been the starting place for law enforcement executives, students of criminal justice, researchers, members of the media, and the public at large seeking information on crime in the nation. The program was conceived in 1929 by the International Association of Chiefs of Police to meet the need for reliable uniform crime statistics for the nation. In 1930, the FBI was tasked with collecting, publishing, and archiving those statistics.

Today, four annual publications, Crime in the United States, National Incident-Based Reporting System, Law Enforcement Officers Killed and Assaulted, and Hate Crime Statistics are produced from data received from over 18,000 city, university/college, county, state, tribal, and federal law enforcement agencies voluntarily participating in the program. The crime data are submitted either through a state UCR Program or directly to the FBI’s UCR Program.

This dataset focuses on the crime rates and law enforcement employment data in the state of California.

Content

Crime and law enforcement employment rates are separated into individual files, focusing on offenses by enforcement agency, college/university campus, county, and city. Categories of crimes reported include violent crime, murder and nonnegligent manslaughter, rape, robbery, aggravated assault, property crime, burglary, larceny-theft, motor vehicle damage, and arson. In the case of rape, data is collected for both revised and legacy definitions. In some cases, a small number of enforcement agencies switched definition collection sometime within the same year.

Acknowledgements

This dataset originates from the FBI UCR project, and the complete dataset for all 2015 crime reports can be found here.

Inspiration

• What are the most common types of crimes in California? Are there certain crimes that are more common in a particular place category, such as a college/university campus, compared to the rest of the state?
• How does the number of law enforcement officers compare to the crime rates of a particular area? Is the ratio similar throughout the state, or do certain campuses, counties, or cities have a differing rate?
• How does the legacy vs. refined definition of rape differ, and how do the rape counts compare? If you pulled the same data from FBI datasets for previous years, can you see a difference in rape rates over time?

Context

This data set contains Insurance Claim data for Fraud Detection.

Content

It consist of 3 dataset 1. Employee Data - this the master data of the employee ( a.k.a agents or adjusters ) working on the insurance claims 2. Vendor Data - this is the master data of the vendor who assist insurance company in investigating the claims 3. Claims Data - this is the claim level transaction details submitted by customer to the insurance company for re-imbursement

Inspiration

We can use this data to solve following use case: - Claim Level Fraud Detection. - Employee Fraud Detection - Employee Vendor Collusion

Employee Fraud Detection Market Outlook

According to our latest research, the global employee fraud detection market size reached USD 4.1 billion in 2024. The industry is demonstrating robust momentum, registering a CAGR of 13.2% during the forecast period. By 2033, the global employee fraud detection market is anticipated to achieve a value of USD 12.1 billion. This impressive growth is primarily driven by the escalating sophistication of internal threats, the proliferation of digital business operations, and the mounting regulatory pressures across industries to implement advanced fraud prevention solutions.

The increasing digitization of business processes and the rapid adoption of cloud-based enterprise applications have significantly expanded the attack surface for internal fraud. As organizations transition to hybrid and remote work models, vulnerabilities to payroll fraud, data theft, and asset misappropriation are rising. This transition has compelled enterprises to invest in advanced employee fraud detection solutions powered by artificial intelligence, machine learning, and behavioral analytics. The market is further propelled by the integration of real-time monitoring tools and automated alert systems, which enable organizations to detect and respond to suspicious activities more efficiently. As a result, the demand for both software and service-based fraud detection solutions is experiencing a notable upsurge, especially among enterprises with complex operational structures.

Another key growth factor is the tightening regulatory landscape, particularly in highly regulated sectors such as BFSI, healthcare, and government. Regulatory bodies worldwide are mandating stringent compliance with anti-fraud and data protection standards, including GDPR, SOX, and HIPAA. Organizations are therefore investing in employee fraud detection platforms not only to mitigate financial and reputational risks but also to ensure compliance with these evolving standards. The increasing frequency of high-profile internal fraud cases has heightened board-level attention to insider threats, prompting the allocation of greater budgets toward enterprise risk management and fraud detection technologies. This regulatory push is fostering innovation and accelerating the adoption of comprehensive, end-to-end fraud detection solutions across all verticals.

The surge in cyber threats and the growing sophistication of fraudulent schemes have also played a pivotal role in shaping the employee fraud detection market. As fraudsters leverage advanced tactics such as social engineering, deepfakes, and synthetic identity fraud, traditional detection methods are proving inadequate. This has led to a shift toward next-generation fraud detection systems that utilize predictive analytics, anomaly detection, and machine learning algorithms. These technologies not only enhance the accuracy of fraud detection but also reduce false positives, thereby improving operational efficiency. Organizations are increasingly seeking integrated solutions that offer a unified view of employee activities across multiple channels, enabling them to proactively identify and address potential risks before they escalate.

Income Fraud Detection has become a critical aspect of the broader employee fraud detection landscape. As organizations face increasing pressure to ensure financial integrity and transparency, the ability to accurately detect and prevent income-related fraud is paramount. This type of fraud often involves the manipulation of financial records, payroll discrepancies, and unauthorized salary adjustments, which can have significant financial and reputational implications for businesses. Advanced income fraud detection systems leverage machine learning algorithms and data analytics to identify anomalies in financial transactions and payroll data, providing organizations with the tools needed to investigate and mitigate potential fraud risks effectively. By integrating these solutions into their existing fraud detection frameworks, companies can enhance their overall financial security and maintain stakeholder trust.

From a regional perspective, North America continues to dominate the employee fraud detection market, accounting for the largest revenue share in 2024. This leadership is attributed to the presence of major industry players, early adoption of advanced

This data collection contains information about the victimization of District of Columbia residents. The primary objective was to measure the extent of crime in the District of Columbia and the impact of crime on the quality of life in the District. Researchers also studied the degree to which congressional employees working in the Capitol Hill area were subject to victimization and the extent to which fear of crime affected their productivity. However, to protect the confidentiality of the respondents, the data on Capitol Hill employees are not present in these files. The Capitol Hill employees data are archived at the Research Triangle Institute and, as of December 1984, a public-use data file did not exist. The three data files archived at the ICPSR contain information about District of Columbia residents only. The first data file includes person-level data including residential mobility, crime prevention measures, and sociodemographic characteristics such as race, age, income, and location and duration of current residence. Each record in Part 2, In Scope Crimes File, represents a reported criminal victimization. The third data file, Out of Scope File, contains data on crimes that were either outside the analysis time period of May 1, 1982, to April 30, 1983, or not crimes of interest for this study.

Employee Fraud Detection Market Outlook

According to our latest research, the global Employee Fraud Detection market size reached USD 5.82 billion in 2024, reflecting the growing imperative for organizations to combat internal threats and financial losses. The market is registering a robust CAGR of 13.9% from 2025 to 2033, projected to attain a value of USD 18.23 billion by 2033. This significant growth is fueled by the increasing sophistication of fraud schemes, digital transformation initiatives, and heightened regulatory compliance requirements across major industry verticals.

The primary growth driver for the employee fraud detection market is the rising incidence of internal fraud cases across both public and private sectors. Organizations are increasingly recognizing the financial and reputational damage caused by payroll fraud, asset misappropriation, data theft, and expense reimbursement fraud. As businesses expand their digital footprints and adopt more complex IT infrastructures, vulnerabilities to internal threats are also expanding. The integration of advanced analytics, artificial intelligence, and machine learning within fraud detection solutions is enabling real-time monitoring and predictive capabilities, significantly enhancing the speed and accuracy of fraud detection. This technological evolution is compelling organizations to invest in robust employee fraud detection systems to safeguard their assets and mitigate operational risks.

Another significant factor propelling market growth is the stringent regulatory landscape governing financial integrity and data protection. Regulations such as the Sarbanes-Oxley Act, GDPR, and various anti-money laundering directives are mandating organizations to implement comprehensive internal controls and monitoring mechanisms. Non-compliance can lead to severe penalties and loss of stakeholder trust, making fraud detection technologies a critical component of corporate governance. In addition, the increasing trend of remote and hybrid work models post-pandemic has created new challenges for monitoring employee activities, further emphasizing the need for advanced fraud detection tools that can operate seamlessly across decentralized environments.

The growing awareness among enterprises regarding the tangible and intangible costs of employee fraud is also contributing to the market’s expansion. Organizations are now prioritizing proactive fraud prevention strategies over reactive loss recovery. This shift is evident in the rising adoption of integrated fraud management systems that combine data analytics, behavioral monitoring, and case management functionalities. Moreover, the surge in digital payment systems and cloud-based operations is prompting businesses to seek scalable and adaptable fraud detection solutions. As a result, the market is witnessing increased demand from sectors like BFSI, healthcare, retail, and manufacturing, where the stakes for internal fraud are particularly high.

From a regional perspective, North America leads the employee fraud detection market due to its early adoption of advanced technologies, stringent regulatory frameworks, and high incidence of internal fraud cases. The region is followed by Europe and the Asia Pacific, where rapid digitalization, growing enterprise sizes, and increasing regulatory scrutiny are driving market growth. The Asia Pacific region, in particular, is expected to exhibit the fastest CAGR during the forecast period, fueled by the expansion of multinational corporations, rising cybercrime rates, and growing awareness of internal fraud risks. Latin America and the Middle East & Africa are also emerging as promising markets, supported by improving digital infrastructure and evolving regulatory landscapes.

Component Analysis

The employee fraud detection market is segmented by component into software and services, each playing a pivotal role in the overall ecosystem. The software segment dominates the market, accounting for the largest revenue share in 2024, owing to the rapid adoption of advanced analytics platforms, AI-driven monitoring tools, and integrated case management systems. These software solutions are designed to automate the detection of anomalous employee behavior, streamline investigation workflows, and provide comprehensive reporting for compliance purposes. The growing complexity of internal fraud schemes is compelling organizations to deploy sophisticated software capable of real-time data analy

The booming employee identity theft protection tool market, projected to reach $6 billion by 2033, is driven by rising cyber threats and remote work. Explore market size, growth trends, key players (Keeper Security, ID Agent, NortonLifeLock, etc.), and regional analysis in this comprehensive report.

Protect your employees from identity theft! Explore the booming employee identity theft protection market, projected to reach $8B+ by 2033. Discover key trends, leading companies, and regional insights in this comprehensive market analysis.

Protect your employees from identity theft! Explore the booming $1221.2 million Employee Identity Theft Protection market. Learn about key trends, growth drivers, and leading companies in this crucial sector. Discover insights on cloud-based vs. on-premises solutions and regional market shares. Invest in employee security today!

The global Employee Identity Theft Protection Software market is projected to reach an estimated USD 5,500 million by 2025, demonstrating a robust Compound Annual Growth Rate (CAGR) of 18.0% from 2019 to 2033. This significant expansion is fueled by the escalating rates of identity fraud impacting employees, coupled with a growing awareness among organizations regarding their responsibility to safeguard their workforce. The increasing sophistication of cyber threats and data breaches necessitates robust identity protection solutions, driving demand for both cloud-based and on-premises deployments. Large enterprises, in particular, are leading the adoption curve due to the sheer volume of sensitive employee data they manage and the potential reputational and financial damage associated with a breach. Small and Medium-sized Enterprises (SMEs) are also increasingly recognizing the value proposition, with cloud-based solutions offering scalability and cost-effectiveness. Key market drivers include the growing regulatory landscape mandating data protection, the rising prevalence of remote work environments, and the proactive stance businesses are taking to enhance employee well-being and trust. The market is characterized by a dynamic competitive landscape with established players like Keeper Security, NortonLifeLock, and Aura Company alongside specialized identity protection providers such as ID Agent and IDShield. Innovations in areas like advanced threat detection, proactive monitoring, and comprehensive identity restoration services are shaping market trends. The adoption of AI and machine learning is enhancing the efficacy of these solutions, enabling faster identification and mitigation of fraudulent activities. However, the market also faces restraints such as the cost of implementation and ongoing maintenance for some advanced solutions, as well as potential employee resistance to adopting new technologies or sharing personal information. Geographically, North America and Europe are expected to dominate the market share due to their advanced economies, stringent data privacy regulations, and high adoption rates of digital technologies. The Asia Pacific region is anticipated to witness substantial growth, driven by increasing digitalization and a rising awareness of cybersecurity threats. Here is a report description on Employee Identity Theft Protection Software, incorporating your specified elements:

The FBI's Uniform Crime Reporting (UCR) system accepts voluntary crime data from nearly 17,000 domestic law enforcement agencies. This data, collected since the 1920s, is the cornerstone for national crime statistics and is used by law enforcement, resea

Around 15.2 percent of all employee-related shrinkage cases in U.S. retail resulted in a loss of between 1,000 and 1,999 U.S. dollars in 2019. That year, the average dollar loss per dishonest employee incident for retailers in the United States was 1,139.32 U.S. dollars.

Federal Bureau of Investigation, Department of Justice ? Extraction of crime related data from the FBI's Uniform Crime Reporting (UCR) Program

Fraud Detected in the Office of the Commissioner for Public Sector Employment