The Supplemental Fraud Survey (SFS) obtained additional information about fraud-related victimizations so that policymakers; academic researchers; practitioners at the federal, state, and local levels; and special interest groups who are concerned with these crimes can make informed decisions concerning policies and programs. The SFS asked questions related to victims' experiences with fraud. These responses are linked to the National Crime Victimization Survey (NCVS) survey instrument responses for a more complete understanding of the individual victim's circumstances. The 2017 Supplemental Fraud Survey (SFS) was the first implementation of this supplement to the annual NCVS to obtain specific information about fraud-related victimization and disorder on a national level. Since the SFS is a supplement to the NCVS, it is conducted under the authority of Title 34, United States Code, section 10132. Only Census employees sworn to preserve confidentiality may see the completed questionnaires.

In 2022, approximately 29,000 men aged between 45 and 54 years in Italy were victims of online swindles and cyber fraud. Women of the same age that fell victim to online fraud and internet scams were 23,347 in 2022. In comparison, approximately 850 teenagers between 14 and 17 years were victim of cyber fraud and online swindlers. Approximately across all age demographics, men and male kids were more likely to fall victim to online fraud in the last examined period.

The Market Saturation and Utilization Core-Based Statistical Areas (CBSA) dataset provides monitoring of market saturation as a means to help prevent potential fraud, waste, and abuse (FWA). CBSAs are geographical delineations that are Census Bureau-defined urban clusters of at least 10,000 people. Market saturation, in the present context, refers to the density of providers of a particular service within a defined geographic area relative to the number of beneficiaries receiving that service in the area. The data can be used to reveal the degree to which use of a service is related to the number of providers servicing a geographic region. There are also a number of secondary research uses for these data, but one objective of making these data public is to assist health care providers in making informed decisions about their service locations and the beneficiary population they serve. The interactive dataset can be filtered and analyzed on the site or downloaded in Excel format.

The objective of the Adult Criminal Court Survey (ACCS) is to develop and maintain a database of statistical information on appearances, charges, and cases in adult criminal courts. The survey is intended to be a census of federal statute charges heard in provincial and superior criminal courts in Canada. It includes information on the age and sex of the accused, case decision patterns, sentencing information regarding the length of prison and probation, and amount of fine, as well as case-processing data such as case elapsed time. These data on federal statute charges heard in adult criminal courts in the reference period are collected by the Canadian Centre for Justice Statistics (CCJS) in collaboration with provincial and territorial government departments responsible for adult criminal courts. The data are collected to respond to the needs of the provincial/territorial and federal departments of justice and attorneys-general, researchers and policy analysts, academics and the media, as well as to inform the public how adults are dealt with by adult provincial/territorial criminal courts in Canada. The ACCS has been replaced by the Integrated Criminal Court Survey (ICCS).

Crime Rates of offences per thousand population, by broad crime grouping, by financial year for wards. Offences: These are confirmed reports of crimes being committed. All data relates to "notifiable offences" - which are designated categories of crimes that all police forces in England and Wales are required to report to the Home Office. Ward data should not be aggregated to give a borough or London total. This is because a small percentage (less than 5%) of crimes in this dataset have not been geocoded to a ward. Therefore total numbers and rates are indicative only, and are not an exact measure at ward level. The rate is calculated using ward GLA 2012-based (SHLAA) population projections, and population data calculated and constrained to 2012 Borough (SHLAA) projections. The London figure only includes the Met Police area, not the City of London. The London total includes all offences in the Met Police Area including those that could not be geocoded. Therefore the London total will not equal the sum of the wards. Some ward boundaries changed in 2014. From 2013/14 the data shown is for the new boundaries. This only affects Hackney, Kensington and Chelsea, and Tower Hamlets. From 2013/14, the numbers and rates for 2013 ward boundaries in Hackney, K&C and Tower Hamlets, have all been modelled based on the proportion of population living in each area at the 2011 Census. Action Fraud have taken over the recording of fraud offences on behalf of individual police forces. This process began in April 2011 and was rolled out to all police forces by March 2013. Due to this change caution should be applied when comparing data over this transitional period and with earlier years. Data by detailed crime group and month are available from the MPS website .

Our consumer data is meticulously gathered and aggregated from surveys, digital services, and public sources, ensuring the collection of fresh and reliable data points through powerful profiling algorithms. Our comprehensive data enrichment solution spans a variety of datasets, enabling you to address gaps in customer data, gain deeper insights into your customers, and enhance client experiences.

Data Categories and Attributes:

• Geography: City, State, ZIP, County, CBSA, Census Tract, etc.
• Demographics: Gender, Age Group, Marital Status, Language, etc.
• Financial: Income Range, Credit Rating Range, Credit Type, Net Worth Range, etc.
• Persona: Consumer Type, Communication Preferences, Family Type, etc.
• Interests: Content, Brands, Shopping, Hobbies, Lifestyle, etc.
• Household: Number of Children, Number of Adults, IP Address, etc.
• Behaviors: Brand Affinity, App Usage, Web Browsing, etc.
• Firmographics: Industry, Company, Occupation, Revenue, etc.
• Retail Purchase: Store, Category, Brand, SKU, Quantity, Price, etc.
• Auto: Car Make, Model, Type, Year, etc.
• Housing: Home Type, Home Value, Renter/Owner, Year Built, etc

Data Export Methodology

Our dynamic data collection ensures the most updated insights, delivered at intervals best suited to your needs (daily, weekly, or monthly).

Use Cases

Our enriched consumer data supports a 360-degree customer view, data enrichment, fraud detection, and advertising & marketing, providing valuable insights to enhance your business strategies and client interactions.

This table contains figures on the number of registered crimes per month and per year. These are broken down by type of crime and by place. Attempts are also included in the recorded crimes. For some crimes (e.g. murder/homicide), this results in a much higher number than just the number of completed crimes.

Since July 2018, it is no longer possible to record multiple offences, which are related to each other (concurrence), in one registration. An example of this is a street robbery in which a firearm (gun possession) is used. If several offences occur in one registration, only the most serious offence was counted before July 2018. As a result of this adjustment, a number of offences show an increase compared to 2018. This mainly concerns trespassing, special laws including money laundering, arms trafficking including possession of weapons, drug trafficking, violation of public order and other social integrity including insults. The increase was therefore mainly visible in the last 6 months of 2018. This adjustment has only a limited impact on the total number of crimes. For the whole of 2018, this causes an increase of approximately 1.0%. Since 30 April 2020, it is possible to report WhatsApp fraud via the Internet (also known as friend-in-emergency fraud). This was immediately used extensively. In the months of May to December 2020, approximately 20,000 reports of WhatsApp fraud were made.

The number of registered crimes fireworks 2023 is not final. In the first half of 2024, many incidents with retroactive effect will still be classified as a criminal offence and included in the census.

Data available from: 2012

Status of figures: The figures in this table are regularly updated. This may result in minor differences with previous publications. Updating the figures is necessary, for example, in order to be able to retroactively process the reclassification of municipalities or the adjustment of coding.

Changes as of 15 November 2024: Figures for October have been added.

When will there be new figures? The figures for November are added on 16 December.

Dataset Description: Insurance Claims Prediction

Introduction: In the insurance industry, accurately predicting the likelihood of claims is essential for risk assessment and policy pricing. However, insurance claims datasets frequently suffer from class imbalance, where the number of non-claims instances far exceeds that of actual claims. This class imbalance poses challenges for predictive modeling, often leading to biased models favoring the majority class, resulting in subpar performance for the minority class, which is typically of greater interest.

Dataset Overview: The dataset utilized in this project comprises historical data on insurance claims, encompassing a variety of information about the policyholders, their demographics, past claim history, and other pertinent features. The dataset is structured to facilitate predictive modeling tasks aimed at accurately identifying the likelihood of future insurance claims.

Key Features: 1. Policyholder Information: This includes demographic details such as age, gender, occupation, marital status, and geographical location. 2. Claim History: Information regarding past insurance claims, including claim amounts, types of claims (e.g., medical, automobile), frequency of claims, and claim durations. 3. Policy Details: Details about the insurance policies held by the policyholders, such as coverage type, policy duration, premium amount, and deductibles. 4. Risk Factors: Variables indicating potential risk factors associated with policyholders, such as credit score, driving record (for automobile insurance), health status (for medical insurance), and property characteristics (for home insurance). 5. External Factors: Factors external to the policyholders that may influence claim likelihood, such as economic indicators, weather conditions, and regulatory changes.

Objective: The primary objective of utilizing this dataset is to develop robust predictive models capable of accurately assessing the likelihood of insurance claims. By leveraging advanced machine learning techniques, such as classification algorithms and ensemble methods, the aim is to mitigate the effects of class imbalance and produce models that demonstrate high predictive performance across both majority and minority classes.

Application Areas: 1. Risk Assessment: Assessing the risk associated with insuring a particular policyholder based on their characteristics and historical claim behavior. 2. Policy Pricing: Determining appropriate premium amounts for insurance policies by estimating the expected claim frequency and severity. 3. Fraud Detection: Identifying fraudulent insurance claims by detecting anomalous patterns in claim submissions and policyholder behavior. 4. Customer Segmentation: Segmenting policyholders into distinct groups based on their risk profiles and insurance needs to tailor marketing strategies and policy offerings.

Conclusion: The insurance claims dataset serves as a valuable resource for developing predictive models aimed at enhancing risk management, policy pricing, and overall operational efficiency within the insurance industry. By addressing the challenges posed by class imbalance and leveraging the rich array of features available, organizations can gain valuable insights into insurance claim likelihood and make informed decisions to mitigate risk and optimize business outcomes.

Abstract

The victims of crime survey 1998 was commissioned by the South African Department of Safety and Security (DSS), and undertaken by Statistics South Africa (Stats SA). The first national survey of its kind in South Africa, this countrywide, household-based survey examines crime from the point of view of the victim. While surveys of crime victims cannot replace police statistics, they can provide a rich source of information which will assist in the planning of crime prevention. A victim survey can also examine the extent of reporting of crime, explore the perceptions that different people have about the police and police services, and act as a benchmark against which future surveys of the same nature can be compared.

Geographic coverage

The survey has national coverage

Analysis unit

Households and individuals

Universe

The survey covered all households in South Africa

Kind of data

Sample survey data

Sampling procedure

The sample consisted of 4 000 people aged 16 years or more. It was drawn in three stages. Firstly, a probability sample of 800 enumerator areas (EAs) was drawn from the sampling frame of 86 000 EAs, as demarcated for the 1996 population census. This sample was stratified explicitly by province, and implicitly by the 42 police districts of the country. Secondly, within each of the 800 EAs, five households were selected for interviewing, using systematic sampling. Thirdly, one respondent aged 16 years or more was selected to be interviewed in each of the five households in each sampled EA. This person was chosen using a table of random numbers. Once a respondent had been selected, fieldworkers were instructed to make sure that they interviewed only that specific person and nobody else. In case of non-contacts with that person, repeated callbacks (at least three) had to be made. There were no substitutions for refusals or non-contacts.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey questionnaire was based on a standard international questionnaire, but with certain modifications for use in South Africa. The international questionnaire covered eleven main crimes, including theft of a car or other motor vehicle, theft from a car or other vehicle, car vandalism, theft of a motor cycle or scooter, theft of a bicycle, burglary or housebreaking, attempted burglary, robbery with force, personal theft, sexual incidents and assault and two supplementary crimes (consumer fraud and corruption). In the South African questionnaire, the following crimes were added on the recommendation of the advisory committee to meet specific South African needs: theft of livestock, poultry and other animals, hijacking or attempted hijacking of vehicles, deliberate damage, burning or destruction of dwellings and deliberate killing or murder.

A control questionnaire was administered by the fieldwork supervisor in one of the five households selected for participation in each enumerator area. This served as a check on the accuracy of the random selection process of the individual in the household, and of the quality of information collected. The survey was favourably received, and 97% of the sample was realised.

Cleaning operations

The processes of computer programming, data capture and data analysis involved several steps: A tabulation plan was drawn up beforehand to assist with writing the computer programme for data capture. The data-input programme, containing both range and consistency checks, was written by a programmer working in Stats SA's Directorate of Household Surveys. Coding of the questionnaires and data capture were handled by temporary staff. Once the capturing was completed, additional editing programmes were written, and then the data-cleaning process was completed. Tables from the dataset, based on the tabulation plan, and the data set itself were then made available for analysis and report-writing.

This table contains figures on the number of registered crimes per month and per year. These are broken down by type of crime and by municipality/neighbourhood. Attempts are also included in the recorded crimes. For some crimes (e.g. murder/homicide), this results in a much higher number than just the number of completed crimes. The data per municipality are presented for all years according to the municipal classification of 1 January 2024.

Since July 2018, it is no longer possible to record multiple offences, which are related to each other (concurrence), in one registration. An example of this is a street robbery in which a firearm (gun possession) is used. If several offences occur in one registration, only the most serious offence was counted before July 2018. As a result of this adjustment, a number of offences show an increase compared to 2018. This mainly concerns trespassing, special laws including money laundering, arms trafficking including possession of weapons, drug trafficking, violation of public order and other social integrity including insults. The increase was therefore mainly visible in the last 6 months of 2018. This adjustment has only a limited impact on the total number of crimes. For the whole of 2018, this causes an increase of approximately 1.0%. Since 30 April 2020, it is possible to report WhatsApp fraud via the Internet (also known as friend-in-emergency fraud). This was immediately used extensively. In the months of May to December 2020, approximately 20,000 reports of WhatsApp fraud were made.

The number of registered crimes fireworks 2023 is not final. In the first half of 2024, many incidents with retroactive effect will still be classified as a criminal offence and included in the census.

Declarations concern registered crimes for which a ‘reporting record’ has been drawn up. Several reports can be made per crime. Internet reporting can only be done for a selected number of offences and only if there is no detection indication.

Data available from: 2012

Status of figures: The figures in this table are regularly updated. This may result in minor differences with previous publications. Updating the figures is necessary, for example, in order to be able to retroactively process the reclassification of municipalities or the adjustment of coding. Figures on declarations and internet declarations are updated after each quarter.

Changes as of 15 November 2024: Figures for October have been added.

When will there be new figures? The figures for November are added on 16 December.