The rate of incidents of identity theft in Canada decreased by 4.2 incidents (-24.01 percent) in 2023 in comparison to the previous year. Nevertheless, the last two years recorded a significant higher rate of incidents than the preceding years.

The rate of identity fraud in Canada decreased to 52.96 incidents since the previous year. Nevertheless, the last two years recorded a significant higher rate than the preceding years.

In the first quarter of 2024, Canadians lost around 123 million Canadian dollars due to fraud. According to the reports from fraud victims, the amount of monetary loss caused by various scams has increased from 531 million Canadian dollars in 2022 to 554 million in 2023.

This graph shows the number of victims of identity fraud in Canada from 2012 to 2014. In 2012 there were 17,094 victims of identity fraud nationwide.

Identity fraud

Identity fraud is the use of some else’s identity (alive, dead, or fake) or the changing of information of one’s one identity for fraudulent purposes. It is differentiated from identity theft which is merely the stealing of someone else’s personal information (usually as a preparatory step for identity fraud). Examples of the objectives of identity frauds are teens attempting to purchase alcohol and tobacco with a fake ID, applying for loans and credit cards, receiving government benefits, money laundering and hiding criminal activity.

Identity fraud has been increasing in Canada since records started being kept by StatCan in 2010 but it is thought that it is still underreported. This is due partly to the prevalence of synthetic identity fraud (the manufacturing of a fake identity with real identification documents) which can be difficult to differentiate from genuine credit defaults. The rate of identity fraud in Canada increased 64 percent in just five years from 2010 to 2014 with the total number of identity fraud incidents at 10,606 for 2014. Canadian victims of identity fraud suffered roughly 10.5 million Canadian dollars in damages in 2014.

Police-reported cybercrime, by cyber-related violation (homicide, invitation to sexual touching, sexual exploitation, luring a child via a computer, voyeurism, non-consensual distribution of intimate images, extortion, criminal harassment, indecent/harassing communications, uttering threats, fraud, identity theft, identity fraud, mischief, fail to comply with order, indecent acts, child pornography, making or distribution of child pornography, public morals, breach of probation), Canada (selected police services), 2014 to 2023.

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.

This dataset includes all Theft from Motor Vehicle occurrences by reported date and related offences since 2014. The Theft from Motor Vehicle offences include Theft from Motor Vehicle Under and Theft from Motor Vehicle Over.Theft from Motor Vehicle DashboardDownload DocumentationThis data is provided at the offence and/or victim level, therefore one occurrence number may have several rows of data associated to the various offences used to categorize the occurrence.The downloadable datasets display the REPORT_DATE and OCC_DATE fields in UTC timezone.This data does not include occurrences that have been deemed unfounded. The definition of unfounded according to Statistics Canada is: “It has been determined through police investigation that the offence reported did not occur, nor was it attempted” (Statistics Canada, 2020).**The dataset is intended to provide communities with information regarding public safety and awareness. The data supplied to the Toronto Police Service by the reporting parties is preliminary and may not have been fully verified at the time of publishing the dataset. The location of crime occurrences have been deliberately offset to the nearest road intersection node to protect the privacy of parties involved in the occurrence. All location data must be considered as an approximate location of the occurrence and users are advised not to interpret any of these locations as related to a specific address or individual.NOTE: Due to the offset of occurrence location, the numbers by Division and Neighbourhood may not reflect the exact count of occurrences reported within these geographies. Therefore, the Toronto Police Service does not guarantee the accuracy, completeness, timeliness of the data and it should not be compared to any other source of crime data.By accessing these datasets, the user agrees to full acknowledgement of the Open Government Licence - Ontario.In accordance with the Municipal Freedom of Information and Protection of Privacy Act, the Toronto Police Service has taken the necessary measures to protect the privacy of individuals involved in the reported occurrences. No personal information related to any of the parties involved in the occurrence will be released as open data. ** Statistics Canada. 2020. Uniform Crime Reporting Manual. Surveys and Statistical Programs. Canadian Centre for Justice Statistics.

Percentage of Canadians who have experienced an Internet security and/or privacy incident during the past 12 months, by type of incident.

This dataset includes all auto theft occurrences by reported date and related offences since 2014.Auto Theft DashboardDownload DocumentationThis data is provided at the offence and/or vehicle level, therefore one occurrence number may have several rows of data associated to the various MCIs used to categorize the occurrence.The downloadable datasets display the REPORT_DATE and OCC_DATE fields in UTC timezone.This data does not include occurrences that have been deemed unfounded. The definition of unfounded according to Statistics Canada is: “It has been determined through police investigation that the offence reported did not occur, nor was it attempted” (Statistics Canada, 2020).**The dataset is intended to provide communities with information regarding public safety and awareness. The data supplied to the Toronto Police Service by the reporting parties is preliminary and may not have been fully verified at the time of publishing the dataset. The location of crime occurrences have been deliberately offset to the nearest road intersection node to protect the privacy of parties involved in the occurrence. All location data must be considered as an approximate location of the occurrence and users are advised not to interpret any of these locations as related to a specific address or individual.NOTE: Due to the offset of occurrence location, the numbers by Division and Neighbourhood may not reflect the exact count of occurrences reported within these geographies. Therefore, the Toronto Police Service does not guarantee the accuracy, completeness, timeliness of the data and it should not be compared to any other source of crime data.By accessing these datasets, the user agrees to full acknowledgement of the Open Government Licence - Ontario.In accordance with the Municipal Freedom of Information and Protection of Privacy Act, the Toronto Police Service has taken the necessary measures to protect the privacy of individuals involved in the reported occurrences. No personal information related to any of the parties involved in the occurrence will be released as open data. ** Statistics Canada. 2020. Uniform Crime Reporting Manual. Surveys and Statistical Programs. Canadian Centre for Justice Statistics.

Note: as of 15 Feb 2024, this dataset has been archived and will no longer be updated. A new dataset Losses of public money or property as per the Public Accounts of Canada has been created, combining the data for all losses of public money or public property into one bilingual CSV file. This new combined losses dataset contains all existing data from the previously separated losses files, and will continue to be updated on an annual basis in conjunction with the publication of the Public Accounts of Canada. --- At the end of each fiscal year, government-wide financial information is published in the Public Accounts. This dataset, based on the Volume 3 of the Public Accounts, provides, by ministry, details for Losses of revenues due to fraud or willful misrepresentation that were discovered or detected during the reporting fiscal year. Information on losses of public money and property is required under the Treasury Board directive on losses of money or property. This dataset is from the Public Accounts of Canada and is not the official record of information. The official version of record can be found on the Receiver General website for the most recent fiscal year and the Library and Archives website for historical years.

Cyber security guidance

In 2023, the police in Canada received more than four thousand reports about online identity fraud. Between 2014 and 2023, this number has increased significantly, peaking in 2021. In the first measured year, the number of such reports received by the Canadian police was 838, whereas in 2021, it reached 5,265 cases.

This data set is no longer compiled by the Ministry of the Solicitor General. Property crimes are typically non-violent in nature and include: * breaking and entering * motor vehicle theft * theft over $5,000 (non-motor vehicle) * theft under $5,000 (non-motor vehicle) * mischief The data can be accessed from Statistics Canada.

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Healthcare Fraud Detection Market Size 2025-2029

The healthcare fraud detection market size is forecast to increase by USD 1.09 billion at a CAGR of 11.8% between 2024 and 2029.

The market is experiencing significant growth due to the increasing number of patients seeking health insurance and the emergence of social media's influence on the healthcare industry. The rise in healthcare fraud cases, driven by the influx of insurance claims, necessitates robust fraud detection solutions. Social media's impact on healthcare extends to fraudulent activities, with fake claims and identity theft posing challenges. However, the deployment of healthcare fraud detection systems remains a time-consuming process, and the need for frequent upgrades to keep up with evolving fraud schemes adds complexity.
Additionally, collaborating with regulatory bodies and industry associations can help stay informed of the latest fraud trends and best practices. Overall, the market presents opportunities for innovation and growth, as the demand for effective solutions to combat fraudulent activities continues to rise. Companies must navigate these challenges by investing in advanced technologies, such as machine learning and artificial intelligence, to streamline deployment and enhance fraud detection capabilities.

What will be the Size of the Healthcare Fraud Detection Market during the forecast period?

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The market encompasses various solutions and services designed to mitigate fraudulent activities in Medicaid services and health insurance. Data analytics plays a pivotal role in this domain, with statistical methods and data science techniques used to identify fraudulent healthcare activities. Prescriptive analytics and machine learning algorithms enable the prediction of potential fraudulent claims and billing schemes. Medical services, including pharmacy billing fraud and prescription fraud, are prime targets for offenders. Identity theft and social media are also significant contributors to healthcare fraud costs. Payment integrity is crucial for insurers to minimize financial losses, making fraud detection a priority.

On-premise and cloud-based solutions offer analytics capabilities to combat fraud. Descriptive analytics provides insights into historical data, while predictive analytics and prescriptive analytics offer proactive fraud detection. Despite the advancements in fraud detection, data limitations pose challenges. The use of artificial intelligence and machine learning in fraud detection is increasing, providing more accurate and efficient solutions. Insurance claims review is a critical component of fraud detection, with fraudulent claims costing billions annually. Fraudsters continue to evolve their tactics, necessitating the need for advanced fraud detection solutions.

How is this Healthcare Fraud Detection Industry segmented?

The healthcare fraud detection industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Descriptive analytics
  Predictive analytics
  Prescriptive analytics


End-user

  Private insurance payers
  Third-party administrators (TPAs)
  Government agencies
  Hospitals and healthcare providers


Delivery Mode

  Cloud-based
  On-premises


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Japan


  South America

    Brazil


  Rest of World (ROW)

By Type Insights

The Descriptive analytics segment is estimated to witness significant growth during the forecast period. In the dynamic landscape of healthcare, Anomalies Detection and Healthcare Fraud Analytics play a pivotal role in safeguarding Financial Resources from Fraudulent Healthcare Activities. Descriptive analytics, a foundational type of analytics, forms the backbone of this industry. With its ability to aggregate and examine vast healthcare data, descriptive analytics identifies trends and operational performance insights. It is widely used in various departments, from Healthcare IT adoption to Urgent care, and supports Insurance Claims Review processes. Cloud-Based Solutions and On-Premises Solutions are two delivery models that cater to diverse organizational needs. Machine Learning and Statistical Methods are integral to advanced analytics, including Prescriptive analytics and Predictive analytics, which uncover intricate patterns and prevent Fraudulent Claims.

Social Media and Data Analytics offer valuable insights into potential Fraudulent Activities, while Real-Time Analytics ensure Payment Integrity in Healthca

This dataset includes all theft over occurrences by reported date and related offences since 2014.Theft Over DashboardDownload DocumentationThis data is provided at the offence and/or victim level, therefore one occurrence number may have several rows of data associated to the various MCIs used to categorize the occurrence.The downloadable datasets display the REPORT_DATE and OCC_DATE fields in UTC timezone.This data does not include occurrences that have been deemed unfounded. The definition of unfounded according to Statistics Canada is: “It has been determined through police investigation that the offence reported did not occur, nor was it attempted” (Statistics Canada, 2020).**The dataset is intended to provide communities with information regarding public safety and awareness. The data supplied to the Toronto Police Service by the reporting parties is preliminary and may not have been fully verified at the time of publishing the dataset. The location of crime occurrences have been deliberately offset to the nearest road intersection node to protect the privacy of parties involved in the occurrence. All location data must be considered as an approximate location of the occurrence and users are advised not to interpret any of these locations as related to a specific address or individual.NOTE: Due to the offset of occurrence location, the numbers by Division and Neighbourhood may not reflect the exact count of occurrences reported within these geographies. Therefore, the Toronto Police Service does not guarantee the accuracy, completeness, timeliness of the data and it should not be compared to any other source of crime data.By accessing these datasets, the user agrees to full acknowledgement of the Open Government Licence - Ontario.In accordance with the Municipal Freedom of Information and Protection of Privacy Act, the Toronto Police Service has taken the necessary measures to protect the privacy of individuals involved in the reported occurrences. No personal information related to any of the parties involved in the occurrence will be released as open data. ** Statistics Canada. 2020. Uniform Crime Reporting Manual. Surveys and Statistical Programs. Canadian Centre for Justice Statistics.

Police-reported cybercrime, by cyber-related violation (homicide, invitation to sexual touching, sexual exploitation, luring a child via a computer, voyeurism, non-consensual distribution of intimate images, extortion, criminal harassment, indecent/harassing communications, uttering threats, fraud, identity theft, identity fraud, mischief, fail to comply with order, indecent acts, child pornography, making or distribution of child pornography, public morals, breach of probation), Canada (selected police services), 2014 to 2023.

This dataset includes all Major Crime Indicators (MCI) occurrences by reported date and related offences since 2014.Major Crime Indicators DashboardDownload DocumentationThe Major Crime Indicators categories include Assault, Break and Enter, Auto Theft, Robbery and Theft Over (Excludes Sexual Violations). This data is provided at the offence and/or victim level, therefore one occurrence number may have several rows of data associated to the various MCIs used to categorize the occurrence.The downloadable datasets display the REPORT_DATE and OCC_DATE fields in UTC timezone.This data does not include occurrences that have been deemed unfounded. The definition of unfounded according to Statistics Canada is: “It has been determined through police investigation that the offence reported did not occur, nor was it attempted” (Statistics Canada, 2020).**The dataset is intended to provide communities with information regarding public safety and awareness. The data supplied to the Toronto Police Service by the reporting parties is preliminary and may not have been fully verified at the time of publishing the dataset. The location of crime occurrences have been deliberately offset to the nearest road intersection node to protect the privacy of parties involved in the occurrence. All location data must be considered as an approximate location of the occurrence and users are advised not to interpret any of these locations as related to a specific address or individual.NOTE: Due to the offset of occurrence location, the numbers by Division and Neighbourhood may not reflect the exact count of occurrences reported within these geographies. Therefore, the Toronto Police Service does not guarantee the accuracy, completeness, timeliness of the data and it should not be compared to any other source of crime data.By accessing these datasets, the user agrees to full acknowledgement of the Open Government Licence - Ontario.In accordance with the Municipal Freedom of Information and Protection of Privacy Act, the Toronto Police Service has taken the necessary measures to protect the privacy of individuals involved in the reported occurrences. No personal information related to any of the parties involved in the occurrence will be released as open data. ** Statistics Canada. 2020. Uniform Crime Reporting Manual. Surveys and Statistical Programs. Canadian Centre for Justice Statistics.

A survey among Canadian adults from November to December 2022 found that 47 percent of respondents said they were extremely concerned about people using information available about them online to attempt to steal their identity.

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Identity Verification Market Size 2025-2029

The identity verification market size is forecast to increase by USD 19.59 billion, at a CAGR of 20.2% between 2024 and 2029.

The market is experiencing significant growth due to the increasing adoption of electronic ID (eID) cards and smart infrastructure initiatives. Digitalization is transforming various sectors, leading to a rise in demand for robust identity verification solutions. Simultaneously, the bring your own device (BYOD) trend in enterprises is expanding the market scope, as organizations seek to ensure secure access to sensitive information on personal devices. However, this market is not without challenges.
Companies must navigate these challenges to capitalize on the opportunities presented by the digital transformation and the expanding adoption of eID cards and BYOD policies. Effective identity verification strategies will be crucial for businesses seeking to maintain customer trust and protect sensitive information in this rapidly evolving landscape. Privacy and security concerns surrounding data transferred through the Internet of Things (IoT) pose a significant obstacle. As more devices and applications become interconnected, ensuring the security and privacy of user data becomes increasingly complex.

What will be the Size of the Identity Verification Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, driven by advancements in technology and the increasing need for robust security solutions. Digital certificates, fingerprint scanning, data mining, authentication protocols, cloud security, presentation attack detection, image processing, biometric sensors, behavioral biometrics, and other technologies are integrated into comprehensive identity verification systems. These solutions employ pattern recognition and machine learning algorithms to enhance security and improve user experience (UX).

Biometric authentication methods, such as facial, iris, and voice recognition, offer enhanced security and regulatory compliance. Data privacy and anonymization remain key concerns, necessitating advanced encryption and access control measures. Risk-based authentication and edge computing further bolster security, while big data analytics and IoT security ensure comprehensive threat detection and response.

How is this Identity Verification Industry segmented?

The identity verification industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Component

  Solutions
  Services


Type

  Large enterprises
  SMEs


Technology

  Biometric verification
  Document verification
  Digital identity solutions and IDaaS
  Database verification
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    Australia
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Component Insights

The Solutions segment is estimated to witness significant growth during the forecast period. Identity verification is a crucial process for businesses to authenticate the identity of users or customers, ensuring that only authorized individuals access their services or systems. This process employs various advanced technologies to detect and prevent identity fraud, including pattern recognition, machine learning, biometric authentication, facial recognition, multi-factor authentication, and voice recognition. Regulatory compliance is a significant factor driving the adoption of these solutions, as businesses seek to meet stringent identity verification requirements. Data privacy and data anonymization are also essential considerations in identity verification, as sensitive information must be protected. To address these concerns, technologies such as data encryption, big data analytics, and cloud security are employed.

Additionally, edge computing and risk-based authentication help to improve the user experience (UX) by reducing response times and providing more personalized services. Biometric enrollment and access control systems, such as fingerprint scanning and behavioral biometrics, are used for offline identity verification. Online identity verification employs technologies like digital certificates, presentation attack detection, image processing, and biometric sensors. Deep learning and neural networks are used for spoof detection and template protection to prevent identity spoofing.

IoT security and liveness detection are also crucial components of identity verification, ensuring that only real people are accessing devices and systems. Ove

This dataset includes all Theft from Motor Vehicle occurrences by reported date. The Theft from Motor Vehicle offences include Theft from Motor Vehicle Under and Theft from Motor Vehicle Over. This data is provided at the offence and/or victim level, therefore one occurrence number may have several rows of data associated to the various offences used to categorize the occurrence. This data does not include occurrences that have been deemed unfounded. The definition of unfounded according to Statistics Canada is: “It has been determined through police investigation that the offence reported did not occur, nor was it attempted” (Statistics Canada, 2020). This data includes all MCI occurrences reported to the Toronto Police Service, including those where the location has not been able to be verified. As a result, coordinate fields may appear blank. Likewise, this includes occurrences where the coordinate location is outside the City of Toronto. Note: Fields have been included for both the old 140 City of Toronto Neighbourhoods structure as well as the new 158 City of Toronto Neighbourhoods structure