Crime data assembled by census block group for the MSA from the Applied Geographic Solutions' (AGS) 1999 and 2005 'CrimeRisk' databases distributed by the Tetrad Computer Applications Inc. CrimeRisk is the result of an extensive analysis of FBI crime statistics. Based on detailed modeling of the relationships between crime and demographics, CrimeRisk provides an accurate view of the relative risk of specific crime types at the block group level. Data from 1990 - 1996,1999, and 2004-2005 were used to compute the attributes, please refer to the 'Supplemental Information' section of the metadata for more details. Attributes are available for two categories of crimes, personal crimes and property crimes, along with total and personal crime indices. Attributes for personal crimes include murder, rape, robbery, and assault. Attributes for property crimes include burglary, larceny, and mother vehicle theft. 12 block groups have no attribute information. CrimeRisk is a block group and higher level geographic database consisting of a series of standardized indexes for a range of serious crimes against both persons and property. It is derived from an extensive analysis of several years of crime reports from the vast majority of law enforcement jurisdictions nationwide. The crimes included in the database are the "Part I" crimes and include murder, rape, robbery, assault, burglary, theft, and motor vehicle theft. These categories are the primary reporting categories used by the FBI in its Uniform Crime Report (UCR), with the exception of Arson, for which data is very inconsistently reported at the jurisdictional level. Part II crimes are not reported in the detail databases and are generally available only for selected areas or at high levels of geography. In accordance with the reporting procedures using in the UCR reports, aggregate indexes have been prepared for personal and property crimes separately, as well as a total index. While this provides a useful measure of the relative "overall" crime rate in an area, it must be recognized that these are unweighted indexes, in that a murder is weighted no more heavily than a purse snatching in the computation. For this reason, caution is advised when using any of the aggregate index values. The block group boundaries used in the dataset come from TeleAtlas's (formerly GDT) Dynamap data, and are consistent with all other block group boundaries in the BES geodatabase. This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase. The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive. The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders. Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful. This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase. The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive. The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders. Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful.

GapMaps offers advanced and reliable Crime Risk Places Data sourced from Applied Geographic Solutions (AGS), a trusted provider of premium demographic insights with over 20 years of experience. Leveraged by thousands of businesses, AGS use advanced statistical methodologies and a rolling seven-year database of FBI and local agency statistics to provide a highly accurate view of the relative risk of specific crime types for any geographic area empowering organizations to make informed decisions in areas such as insurance, urban planning, and real estate.

The AGS Crime Risk dataset includes: - Standardised indexes for a range of serious crimes against both persons and property such as murder, rape, robbery, assault, burglary, theft, and motor vehicle theft - Aggregate measures of crime risk, including crimes against persons, crimes against property, and overall crime risk, offering a comprehensive overview of an area’s safety. - 5-Year Projections: Added in 2020, these projections enhance the dataset by forecasting future crime risks, providing valuable insights for long-term planning. - High-Resolution Data: Crime risk indexes are available at the block group level, allowing insurers to identify variations in crime risk across specific land uses such as motor vehicle theft from parking structures.

Use cases: 1. Insurance underwriting and risk mitigation. 2. Evaluating the security measures needed to protect employees and customers at retail facilities. 3. The study of the effects of neighborhood crime on wellness and health care outcomes.

Methodology: Crime is tracked for multiple years using both FBI aggregate crime reports and for many parts of the country at the individual incident level. A complex set of statistical models are used to estimate and forecast risk of each individual crime type by using land use data in conjunction with demographic and business characteristics.

For more information visit www.appliedgeographic.com

Report generated using Esri Community Analyst tool. The Crime Index is an indication of the relative risk of a crime occurring and is measured against the overall risk at a national level. Values above 100 indicate the area has an above average risk of occurring compared to the US. Values below 100 indicate the area has a below average risk of occurring compared to the US. The Crime Indexes provides an assessment of the relative risk of seven major crime types: murder, rape, robbery, assault, burglary, larceny, and motor vehicle theft. It is modeled using data from the FBI Uniform Crime Report and demographic data from the U.S. Census and Applied Geographic Solutions (AGS).

In 2024, South Africa ranked first in the crime index among African countries, with a score of **** index points. Nigeria was the second most dangerous country on the continent, obtaining **** points. The index evaluates the overall crime levels in a specific country. Several African countries scored between ** and ** points, indicating high crime levels. Escalating concerns: South Africans worry about crime and violence In 2024, South Africa had one of the highest proportions of respondents expressing concerns about crime and violence compared to other countries participating in an online study. Throughout the period examined, the percentage of participants worried about violence peaked at ** percent in March 2023. The escalating levels of violent crime currently witnessed in the country has caused this significant rise in concerned respondents. South Africa's organized crime landscape In 2023, South Africa ranked the ************* in organized crime compared to its African counterparts. The continent's most prevalent organized criminal activity was **************************************. Moreover, from a regional perspective, Southern African countries had the lowest organized crime rate.

In 2023, Africa scored **** points in the global organized crime index. This was above the world average of **** points. The indicator measures levels of resilience, whereby a score of *** indicates low levels of resilience and a score of ** indicates the strong presence and effectiveness of frameworks formulated to address current organized crime risks and adapt to emerging threats.

Tags

   Social System, Social Institutions, Justice, Crime, BES, Murder, Rape, Robbery, Assault, Burglary, Larceny, Motor Vehicle Theft




   Summary


   Analysis of crime data for the Baltimore MSA.


   Description


   Crime data assembled by census block group for the MSA from the Applied Geographic Solutions' (AGS) 1999 and 2005 'CrimeRisk' databases distributed by the Tetrad Computer Applications Inc.  CrimeRisk is the result of an extensive analysis of FBI crime statistics. Based on detailed modeling of the relationships between crime and demographics, CrimeRisk provides an accurate view of the relative risk of specific crime types at the block group level. 


   Data from 1990 - 1996,1999, and 2004-2005 were used to compute the attributes, please refer to the 'Supplemental Information' section of the metadata for more details. Attributes are available for two categories of crimes, personal crimes and property crimes, along with total and personal crime indices. Attributes for personal crimes include murder, rape, robbery, and assault. Attributes for property crimes include burglary, larceny, and mother vehicle theft. 12 block groups have no attribute information.


   CrimeRisk is a block group and higher level geographic database consisting of a series of standardized indexes for a range of serious crimes against both persons and property. It is derived from an extensive analysis of several years of crime reports from the vast majority of law enforcement jurisdictions nationwide. The crimes included in the database are the "Part I" crimes and include murder, rape, robbery, assault, burglary, theft, and motor vehicle theft. These categories are the primary reporting categories used by the FBI in its Uniform Crime Report (UCR), with the exception of Arson, for which data is very inconsistently reported at the jurisdictional level. Part II crimes are not reported in the detail databases and are generally available only for selected areas or at high levels of geography.


   In accordance with the reporting procedures using in the UCR reports, aggregate indexes have been prepared for personal and property crimes separately, as well as a total index. While this provides a useful measure of the relative "overall" crime rate in an area, it must be recognized that these are unweighted indexes, in that a murder is weighted no more heavily than a purse snatching in the computation. For this reason, caution is advised when using any of the aggregate index values.


   The block group boundaries used in the dataset come from TeleAtlas's (formerly GDT) Dynamap data, and are consistent with all other block group boundaries in the BES geodatabase.


   Credits


   UVM Spatial Analysis Lab


   Use limitations


   BES use only


   Extent



   West -77.314305  East -76.049572 

   North 39.736284  South 38.700454

Tags

   Social System, Social Institutions, Justice, Crime, BES, Murder, Rape, Robbery, Assault, Burglary, Larceny, Motor Vehicle Theft




   Summary


   Analysis of crime data for the Baltimore MSA.


   Description


   Crime data assembled by census block group for the MSA from the Applied Geographic Solutions' (AGS) 1999 and 2005 'CrimeRisk' databases distributed by the Tetrad Computer Applications Inc.  CrimeRisk is the result of an extensive analysis of FBI crime statistics. Based on detailed modeling of the relationships between crime and demographics, CrimeRisk provides an accurate view of the relative risk of specific crime types at the block group level. 


   Data from 1990 - 1996,1999, and 2004-2005 were used to compute the attributes, please refer to the 'Supplemental Information' section of the metadata for more details. Attributes are available for two categories of crimes, personal crimes and property crimes, along with total and personal crime indices. Attributes for personal crimes include murder, rape, robbery, and assault. Attributes for property crimes include burglary, larceny, and mother vehicle theft. 12 block groups have no attribute information.


   CrimeRisk is a block group and higher level geographic database consisting of a series of standardized indexes for a range of serious crimes against both persons and property. It is derived from an extensive analysis of several years of crime reports from the vast majority of law enforcement jurisdictions nationwide. The crimes included in the database are the "Part I" crimes and include murder, rape, robbery, assault, burglary, theft, and motor vehicle theft. These categories are the primary reporting categories used by the FBI in its Uniform Crime Report (UCR), with the exception of Arson, for which data is very inconsistently reported at the jurisdictional level. Part II crimes are not reported in the detail databases and are generally available only for selected areas or at high levels of geography.


   In accordance with the reporting procedures using in the UCR reports, aggregate indexes have been prepared for personal and property crimes separately, as well as a total index. While this provides a useful measure of the relative "overall" crime rate in an area, it must be recognized that these are unweighted indexes, in that a murder is weighted no more heavily than a purse snatching in the computation. For this reason, caution is advised when using any of the aggregate index values.


   The block group boundaries used in the dataset come from TeleAtlas's (formerly GDT) Dynamap data, and are consistent with all other block group boundaries in the BES geodatabase.


   Credits


   UVM Spatial Analysis Lab


   Use limitations


   BES use only


   Extent



   West -77.314305  East -76.049572 

   North 39.736284  South 38.700454

Note. SCJ  =  structured clinical judgment; N/A  =  not applicable.aThe PCL-R was designed as a personality measure rather than a risk assessment tool, but is frequently used as means to assess risk of violent, sexual and general offending.

Risk Scoring for Mail Deliveries Market Outlook

According to the latest research, the global risk scoring for mail deliveries market size reached USD 1.42 billion in 2024, with a robust compound annual growth rate (CAGR) of 12.6% observed over the past year. This growth is primarily driven by the increasing sophistication of mail fraud, heightened regulatory requirements, and the exponential surge in e-commerce volumes. By 2033, the market is forecasted to grow significantly, reaching a value of USD 4.12 billion as organizations across sectors invest in advanced technologies to secure and streamline their mail delivery processes.

The demand for risk scoring solutions in mail deliveries is underpinned by the urgent need to combat rising threats such as mail theft, package tampering, and fraudulent shipments. As e-commerce and logistics sectors expand at an unprecedented rate, the risk landscape becomes increasingly complex, necessitating the adoption of sophisticated risk scoring systems. These systems leverage machine learning, artificial intelligence, and big data analytics to assess the probability of loss, theft, or fraud at each stage of the delivery chain. The integration of these advanced technologies ensures real-time risk assessment, enabling companies to proactively mitigate threats and ensure the safe and timely delivery of parcels. The market is further propelled by the growing awareness among businesses regarding the financial and reputational damages that can arise from mail-related incidents.

Another significant growth factor is the tightening of regulatory frameworks governing data security, privacy, and mail handling across multiple jurisdictions. Regulatory bodies are mandating more stringent controls and reporting mechanisms, especially for sectors handling sensitive or high-value items. Compliance with these regulations necessitates the implementation of robust risk scoring mechanisms, which can provide auditable records and enhance transparency throughout the mail delivery process. This regulatory push is particularly pronounced in regions such as North America and Europe, where data protection laws and anti-fraud directives are rigorously enforced. As a result, organizations are compelled to invest in scalable and compliant risk scoring solutions, further fueling market expansion.

The rapid digital transformation of the logistics and postal industries is also a key driver behind the surge in demand for risk scoring for mail deliveries. With the proliferation of smart devices, IoT-enabled tracking, and cloud-based delivery management platforms, there is a wealth of data available for analysis. Risk scoring algorithms can now process vast datasets in real-time, factoring in variables such as delivery routes, package value, recipient history, and local crime statistics. This data-driven approach not only enhances the accuracy of risk predictions but also enables dynamic adjustments to delivery protocols based on evolving threat landscapes. The shift towards digitalization is accelerating the adoption of risk scoring technologies, positioning them as an integral component of modern mail delivery ecosystems.

Regionally, North America continues to dominate the risk scoring for mail deliveries market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The strong presence of leading logistics and e-commerce companies, coupled with advanced technological infrastructure and stringent regulatory environments, has positioned these regions at the forefront of adoption. Meanwhile, emerging markets in Asia Pacific are witnessing rapid growth, driven by booming e-commerce sectors and increasing investments in smart logistics solutions. Latin America and the Middle East & Africa, while currently representing smaller market shares, are expected to experience accelerated growth rates over the forecast period as awareness of risk management and digital transformation initiatives gain momentum.

Component Analysis

T

Investigator(s): U.S. Sentencing Commission These data, collected to assist in the development of sentencing guidelines, describe offense and sentencing characteristics for organizations sentenced in federal district courts. The United States Sentencing Commission's primary function is to inform federal courts of sentencing policies and practices that include guidelines prescribing the appropriate form and severity of punishment for offenders convicted of federal crimes. Court-related variables include primary offense type, pecuniary offense loss and gain, dates of disposition and sentencing, method of determination of guilt, number of counts pled and charged, and dates and types of sentencing and restitution. Defendant organization variables include ownership structure, number of owners and employees, highest level of corporate knowledge of the criminal offense, highest level of corporate indictment and conviction for participation in the criminal offense, annual revenue, equity and financial status of the defendant organization, whether it was a criminal organization, duration of criminal activity, and risk to national security. Part 1, Organizational Defendants Data, 1988, describes offense and sentencing characteristics for organizations sentenced in federal district courts in 1988. Part 2, Organizational Defendants Data, 1989-1990, is a compilation of offense and sentencing characteristics for the population of organizations sentenced in federal district courts during the period January 1, 1989, to June 30, 1990. Part 3, Statute Data, 1989-1990, is a secondary component of the Commission's study that includes only the statutes of conviction and number of counts per conviction, during the period January 1, 1989, to June 30, 1990. Part 4, Organizational Defendants Data, 1987-1993, includes all organizational defendants sentenced pursuant to the Chapter Two, Part R (1987) antitrust guidelines and the Chapter Eight (1991) sentencing guidelines for organizational defendants that were sentenced between November 1, 1987, through September 30, 1993, and were received by the Commission. Part 6, Organizational Defendants Data, 1994, gives information on organizational defendants sentenced during fiscal year October 1, 1993, through September 30, 1994, and includes culpability scores and Chapter Eight (1991) culpability scoring procedures. Part 8, Organizational Defendants Data, 1995, covers fiscal year October 1, 1994, through September 30, 1995, and also includes culpability scores and Chapter Eight (1991) culpability scoring procedures. This file includes 9 defendants sentenced pursuant to Section 2R1.1 (1987) and 111 defendants sentenced pursuant to the Chapter Eight guidelines. Years Produced: Updated annually

The Pittsburgh Youth Study (PYS) is part of the larger "Program of Research on the Causes and Correlates of Delinquency" initiated by the Office of Juvenile Justice and Delinquency Prevention in 1986. PYS aims to document the development of antisocial and delinquent behavior from childhood to early adulthood, the risk factors that impinge on that development, and help seeking and service provision of boys' behavior problems. The study also focuses on boys' development of alcohol and drug use, and internalizing problems. PYS consists of three samples of boys who were in the first, fourth, and seventh grades in Pittsburgh, Pennsylvania public schools during the 1987-1988 academic year (called the youngest, middle, and oldest sample, respectively). Using a screening risk score that measured each boy's antisocial behavior, boys identified at the top 30 percent within each grade sample on the screening risk measure (n=~250), as well as an equal number of boys randomly selected from the remainder (n=~250), were selected for follow-up. Consequently, the final sample for the study consisted of 1,517 total students selected for follow-up. 506 of these students were in the oldest sample, 508 were in the middle sample, and 503 were in the youngest sample. Assessments were conducted semiannually and then annually using multiple informants (i.e., boys, parents, teachers) between 1987 and 2010. The youngest sample was assessed from ages 6-19 and again at ages 25 and 28. The middle sample was assessed from ages 9-13 and again at age 23. The oldest sample was assessed from ages 13-25, with an additional assessment at age 35. Information has been collected on a broad range of risk and protective factors across multiple domains (e.g., individual, family, peer, school, neighborhood). Measures of conduct problems, substance use/abuse, criminal behavior, mental health problems have been collected. This collection contains data and syntax files for delinquency constructs. The datasets include constructs on the frequency and level of criminal and delinquent activities, including theft, violence, weapons used, delinquency, drug-selling, white collar crime, as well as police contacts and past incarceration. Additionally, the collection includes data on delinquency risk (high vs. low) and the associated weight. The delinquency constructs were created by using the PYS raw data. The raw data are available at ICPSR in the following studies: Pittsburgh Youth Study Youngest Sample (1987 - 2001) [Pittsburgh, Pennsylvania], Pittsburgh Youth Study Middle Sample (1987 - 1991) [Pittsburgh, Pennsylvania] , and Pittsburgh Youth Study Oldest Sample (1987 - 2000) [Pittsburgh, Pennsylvania].

Thorp Arch has a crime rate of 130.0 per 1,000, higher than the UK average, with a safety score of 63/100.

ALLBUScompact is offered as an alternative to the structurally more complex full version of ALLBUS. It addresses the needs of newcomers to data analysis by providing a simplified demography module containing an easily manageable group of the most important demographic indicators. All topical question modules not containing sensitive data are retained as in the ALLBUS full version (scientific use file).

Topics:

1.) Use of media: Frequency and average total time of watching tv, frequency of watching news programs on public and commercial tv, frequency of reading a daily newspaper per week, frequency of reading books / e-books; internet use: frequency and type of device, frequency of using social media for political information, trustworthiness of different news sources with regard to crime and public safety.

2.) Social Inequality: Self-assessment of social class, fair share in standard of living, assessment of access to education, attitudes towards social inequality and the welfare state.

3.) Ethnocentrism and minorities: Attitude towards the influx of various groups of immigrants, attitudes towards the foreigners living in Germany, contacts with foreigners, antisemitic stereotypes and prejudices, attitudes towards Islam (Islamophobia), perceived risks and chances with respect to refugees.

4.) Family and gender roles: Attitudes towards working fathers and mothers, division of labor regarding house and family work., importance of educational goals.

5.) Values: Work orientations, attitudes towards legalizing abortion, materialism / postmaterialism (importance of law and order, fighting rising prices, free expression of opinions and influence on governmental decisions).

6.) Political attitudes: Pride in being a German, confidence in public institutions and organizations (public health service, federal constitutional court, federal parliament (Bundestag), city or municipal administration, churches, judiciary, television, newspapers, universities, federal government, the police, political parties, European Commission, European Parliament); identification with own community, the Federal Republic of Germany and the EU, preference for lower taxes or more social spending, stance on extension or reduction in social services, perceived strength of conflicts between social groups, political interest, self-placement on left-right continuum, satisfaction with democracy in Germany, voting intention (Sonntagsfrage).

7.) Deviant behavior and sanctions: Assessment of adequacy of court decisions, development of crime rate, moral assessment of deviant acts, crime-specific desire for sanctions (punitivity), desire to prohibit specific behaviors, attitude towards the death penalty, self-reported deviant behavior (past and future), perceived risk of being caught committing various crimes, victimisation (theft, any crime), respect of the law (norm), deterring crime through punishment, purpose of punishment, self-control (Grasmick), fear of crime, feeling of safety in living environment.

8.) Health: Self-assessment of overall health, physical and mental health during the last four weeks, acceptance of state powers to control epidemics.

9.) Religion: Self-assessment of religiousness, denomination, frequency of church attendance / attending a house of God.

10.) Other topics: Assessment of the present and future economic situation in Germany, assessment of present and future personal economic situation, social pessimism and orientation towards the future (anomia), interpersonal trust, reciprocity, authoritarianism, overall life satisfaction.

11.) ALLBUS-Demography: Details about the respondent: age, gender, marital status, citizenship (nationality), school education, vocational training, employment status, affiliation to public service, working hours per week (primary and secondary job), supervisory functions, fear of unemployment, length of unemployment, status of non-employment, date of termination of full-time employment, current or former membership in a trade union, membership in a political party, respondent´s income. Place of residence (size of municipality), duration of residence (in Germany and at current place of residence), mobility.

Details about respondent´s current spouse: age, school education, vocational training, employment status, affiliation to public service, status of non-employment.

Details about respondent´s steady partner: age, school education, vocational training, employment status, affiliati...

Note. k =  number of samples; SCJ  =  structured clinical judgment; SD  =  standard deviation. Designer status operationally defined as being an author of the English-language original version of the instrument under investigation.aAt start of follow-up; bViolent and non-violent.

Customer Risk Rating Market Outlook

According to our latest research, the global Customer Risk Rating market size reached USD 2.38 billion in 2024, and is expected to grow at a CAGR of 13.1% over the forecast period of 2025 to 2033, resulting in a forecasted market value of USD 7.02 billion by 2033. This robust expansion is primarily fueled by the increasing regulatory scrutiny, the accelerating adoption of advanced analytics and artificial intelligence in risk management, and the growing need for real-time risk assessment solutions across various industries.

The growth of the Customer Risk Rating market is significantly driven by the escalating complexity of financial crimes and the corresponding tightening of regulatory frameworks worldwide. Financial institutions, especially within the banking and insurance sectors, are under immense pressure to comply with Anti-Money Laundering (AML) and Know Your Customer (KYC) regulations. As a result, organizations are increasingly investing in sophisticated risk rating solutions that can provide comprehensive, real-time insights into customer behaviors, enabling proactive identification and mitigation of potential risks. The integration of AI and machine learning into these systems has further enhanced their predictive capabilities, allowing institutions to automate the risk assessment process and reduce operational costs while improving accuracy and compliance.

Another pivotal factor contributing to market growth is the rapid digital transformation across industries, which has expanded the attack surface for fraud and cyber threats. The proliferation of digital channels, mobile banking, and online retail has created new avenues for financial crimes and identity theft, necessitating more robust and dynamic risk rating mechanisms. Enterprises are now prioritizing the deployment of customer risk rating solutions that can adapt to evolving threats and provide continuous monitoring. This shift is especially evident in sectors such as fintech, healthcare, and retail, where customer interactions are increasingly digital, and risk exposure is heightened. The adoption of cloud-based risk rating platforms is also accelerating, as organizations seek scalable, flexible, and cost-effective solutions that can be rapidly deployed and integrated with existing IT infrastructure.

Furthermore, the demand for customer-centric risk management strategies is reshaping the landscape of the Customer Risk Rating market. Organizations are recognizing the value of leveraging customer risk data not only for compliance and fraud prevention but also for enhancing customer experience and loyalty. By gaining a deeper understanding of customer risk profiles, businesses can tailor their offerings, streamline onboarding processes, and deliver personalized services while maintaining robust risk controls. This trend is particularly pronounced among large enterprises and multinational corporations, which require comprehensive risk management frameworks to support complex, global operations. As a result, solution providers are innovating with advanced analytics, real-time data integration, and user-friendly interfaces to meet the evolving needs of diverse end-users.

Regionally, North America continues to dominate the Customer Risk Rating market, accounting for the largest share in 2024, followed closely by Europe and the Asia Pacific. The presence of stringent regulatory requirements, a mature financial services ecosystem, and early adoption of advanced risk management technologies are key factors supporting market growth in these regions. Meanwhile, the Asia Pacific region is witnessing the fastest growth, driven by the rapid expansion of digital banking, fintech adoption, and increasing regulatory emphasis on AML and KYC compliance. Latin America and the Middle East & Africa are also experiencing steady growth, supported by rising investments in digital infrastructure and the ongoing modernization of financial services.

Component Analysis

The Component segment of the Customer Risk Rating market is bifurcated into Software and Services, each playing a pivotal role in shaping the overall market dynamics. The software segment dominates the market, owing to the increasing demand for automated, scalable, and integrated solutions capable of processing vast amounts of customer data in real time. Risk rating software is being widely adopted by financial institutions a

This dataset provides detailed information on the 2019 Index of Multiple Deprivation (IMD) for Birmingham, UK. The data is available at the postcode level and includes the Lower Layer Super Output Area (LSOA) information.Data is provided at the LSOA 2011 Census geography.The decile score ranges from 1-10 with decile 1 representing the most deprived 10% of areas while decile 10 representing the least deprived 10% of areas.The IMD rank and decile score is allocated to the LSOA and all postcodes within it at the time of creation (2019).Note that some postcodes cross over LSOA boundaries. The Office for National Statistics sets boundaries for LSOAs and allocates every postcode to one LSOA only: this is the one which contains the majority of residents in that postcode area (as at 2011 Census).

The English Indices of Deprivation 2019 provide a detailed analysis of relative deprivation across small areas in England. The Crime Deprivation dataset is a key component of this index, measuring the risk of personal and material victimization at the local level. This dataset includes indicators such as recorded crimes for violence, burglary, theft, and criminal damage. It helps identify areas with high levels of crime, guiding policy interventions and resource allocation to improve safety and reduce crime rates.

KYC Utilities Market Outlook

According to our latest research, the global KYC (Know Your Customer) Utilities Market size reached USD 1.82 billion in 2024, with a robust year-on-year expansion driven by digital transformation in financial services. The market is projected to grow at a CAGR of 12.7% from 2025 to 2033, reaching an estimated USD 5.37 billion by 2033. This remarkable growth trajectory is fueled by the escalating demand for efficient, standardized, and cost-effective KYC compliance solutions across banking, financial services, insurance, and fintech sectors. As per our latest research, the KYC utilities market is experiencing a paradigm shift, with organizations worldwide striving to streamline customer onboarding, reduce compliance costs, and mitigate financial crime risks through shared utility platforms.

A primary growth factor for the KYC utilities market is the intensifying regulatory landscape governing anti-money laundering (AML) and counter-terrorism financing (CTF) requirements. Financial institutions are under increasing pressure to comply with stringent global and local regulations, which necessitate robust customer due diligence and ongoing monitoring. The complexity and frequency of regulatory changes have made traditional, siloed KYC processes costly and inefficient, prompting a shift toward centralized utility models. KYC utilities enable multiple financial institutions to access standardized customer information, thereby reducing redundancy and accelerating compliance processes. This collective approach not only enhances regulatory adherence but also fosters greater transparency and trust within the financial ecosystem.

Another significant driver propelling the KYC utilities market is the rapid digitization and adoption of advanced technologies such as artificial intelligence (AI), machine learning (ML), and blockchain. These technologies are transforming KYC processes by automating identity verification, risk assessment, and data validation, leading to faster onboarding and improved accuracy. The integration of AI and ML in KYC utilities allows for real-time monitoring of customer behavior, early detection of suspicious activities, and dynamic risk scoring. Blockchain, on the other hand, offers immutable and secure data sharing, further enhancing the reliability and efficiency of KYC utilities. The convergence of these technologies is enabling financial institutions to deliver seamless digital customer experiences while maintaining rigorous compliance standards.

The market is also benefiting from the rising incidence of financial crimes, including identity theft, fraud, and money laundering, which have compelled organizations to invest in robust KYC frameworks. As financial services become increasingly globalized and digital, the risk of cross-border financial crimes has surged. KYC utilities provide a collaborative defense mechanism, enabling institutions to share and access up-to-date customer information, thereby strengthening the overall security posture. Additionally, the growing trend of open banking and cross-industry partnerships is driving demand for interoperable KYC utilities that can serve diverse stakeholders, from traditional banks to emerging fintech players.

Regionally, North America continues to dominate the KYC utilities market, supported by early technology adoption, a mature regulatory environment, and the presence of leading financial institutions. However, Asia Pacific is emerging as the fastest-growing region, driven by rapid digitalization, expanding financial inclusion initiatives, and evolving regulatory frameworks. Europe remains a significant market due to its stringent AML directives and collaborative efforts among financial institutions to establish shared KYC utilities. Meanwhile, Latin America and the Middle East & Africa are witnessing growing adoption as regulators and financial entities recognize the benefits of centralized KYC solutions in combating financial crime and fostering financial sector stability.

Component Analysis

The component segment of the KYC utilities market is categorized into software, services, and platforms, each playing a pivotal role in shaping the industry’s evolution. Software solutions form the backbone of KYC utilities, enabling automation of identity verification, document authentication, and risk assessment processes. These solutions are increasingly leveraging AI and

In 2024, Pietermaritzburg (South Africa) ranked first in the crime index among African cities, with a rating of roughly ** index points. The six most dangerous areas on the continent were South African cities. The index estimates the overall level of crime in a specific territory. According to the score, crime levels are classified as very high (over 80), high (60-80), moderate (40-60), low (20-40), and very low (below 20). South Africa’s crime situation According to the crime index ranking, ************ was the most dangerous country in Africa in 2023, followed by ***************** and ******. Murder and organized crime are particularly widespread in South Africa. In 2023, the country had one of the highest murder rates globally, registering around ** homicides per 100,000 inhabitants. Moreover, South Africa’s crime scene is also characterized by the presence of organized criminal activities, for which the country ranked third in Africa. Reflecting these high levels of crime, a survey conducted in 2023 showed that around ** percent of South Africans were worried about crime and violence in the country. Crime risks in Africa The African continent hosts some of the most dangerous places worldwide. In 2023, *********** and the ******************************** were the least peaceful countries in Africa, according to the Global Peace Index. Worldwide, they ranked fourth and fifth, respectively, behind Afghanistan, Yemen, and Syria. Terrorism is a leading type of crime perpetrated in Africa. Home to Boko Aram, Nigeria is among the countries with the highest number of terrorism-related deaths globally. Furthermore, Burkina Faso had the highest number of fatalities in the world. Human trafficking is also widespread, predominantly in West Africa. The most common forms of exploitation of victims of trafficking in persons are forced labor and sexual exploitation.

Description

This dataset is a valuable resource for financial and real estate analysis since it offers 200,000 records of home purchases made worldwide together with loan approval decisions. Details such property type, furnishing status, size, price, year of construction, and facilities (rooms, baths, garage, garden) are covered, along with information about several cities and nations.

Financially speaking, it comprises the customer's salary, loan amount, loan tenure, monthly expenses, down payment, and EMI-to-income ratio, enabling comprehensive analyses of creditworthiness and affordability. Indicators of risk and quality, including crime statistics, court cases on the property, neighborhood ratings, connectivity scores, and satisfaction scores, are also included.

Applications in corporate intelligence, policy research, and machine learning (classification models) are made possible by the target variable decision, which shows whether a loan was accepted or denied. It can be used by researchers and data scientists to investigate patterns of urban growth, risk assessment, financial decision-making, and housing trends.

In 2024, Guatemala scored 5.45 in the money laundering and terrorism financing risk index. Since 2019, the risk score of this country has been continuously increasing. Still, other Central American countries such as Panama and Honduras had a higher risk of money laundering and terrorist financing than Guatemala.The Basel AML Index is a composite index, a combination of 16 different indicators with regards to corruption, financial standards, political disclosure and rule of law and tries to measure the risk level of money laundering and terrorist financing in different countries. The numbers used are based on publicly available sources such as the FATF, Transparency International, the World Bank and the World Economic Forum and are meant to serve as a starting point for further investigation.