Credit Risk Database Market size was valued at USD 7.31 Billion in 2023 and is projected to reach USD 18.43 Billion by 2031, growing at a CAGR of 14.2% during the forecast period 2024-2031.

Global Credit Risk Database Market Drivers

Regulatory Compliance: Stringent regulations imposed by financial authorities and government bodies require financial institutions to assess and manage credit risks effectively. Compliance with regulations such as Basel III, Dodd-Frank Act, and Anti-Money Laundering (AML) guidelines increases demand for comprehensive credit risk databases. Increasing Loan Origination: With the rise in consumer spending and economic recovery, the demand for loans from individuals and businesses has increased. This growth in loan origination necessitates robust credit risk assessment tools, driving the need for effective credit risk databases.

Global Credit Risk Database Market Restraints

Regulatory Compliance: Stringent regulations surrounding data privacy, banking, and finance can limit the ways in which companies collect, store, and utilize credit risk data. Compliance with regulations such as GDPR (General Data Protection Regulation) and CCPA (California Consumer Privacy Act) can impose significant operational burdens. Data Security Concerns: The sensitive nature of credit risk data makes it a target for cyberattacks. Companies must invest heavily in cybersecurity measures to protect against breaches, which can be a financial burden and deter some firms from entering or expanding in the market.

Global Credit Risk Database Market is segmented by Application (Financial Services_ Credit Scoring_ Risk Management), Type (Data Analytics_ Reporting_ Compliance Solutions), and Geography (North America_ LATAM_ West Europe_Central & Eastern Europe_ Northern Europe_ Southern Europe_ East Asia_ Southeast Asia_ South Asia_ Central Asia_ Oceania_ MEA)

Comparison of FEMA and CRSI Risk assessment indices and how to convert from one to the other. Portions of this dataset are inaccessible because: Part of FEMA and not EPA. They can be accessed through the following means: https://www.fema.gov/flood-maps/products-tools/national-risk-index. Format: FEMA RISK DATABASE. This dataset is associated with the following publications: Williams, A., K. Summers, and L. Harwell. Using Existing Indicators to Bridge the Exposure Data Gap: A Novel Natural Hazard Assessment. Sustainability. MDPI, Basel, SWITZERLAND, 16(23): 10778, (2024). Summers, J., A. Lamper, C. Mcmillion, and L. Harwell. Observed Changes in the Frequency, Intensity, and Spatial Patterns of Nine Natural Hazards in the United States from 2000 to 2019. Sustainability. MDPI, Basel, SWITZERLAND, 14(7): 4158, (2022).

The size of the Credit Risk Database market was valued at USD XXX million in 2024 and is projected to reach USD XXX million by 2033, with an expected CAGR of XX % during the forecast period.

Risk Registers for major subsystems of the StingRAY WEC completed in compliance with the DOE Risk Management Framework developed by NREL.

Dataset

This dataset was created by Yugandhari Bodapati

Contents

The Physical Vulnerability Database for Critical Infrastructure Hazard Risk Assements is a database that contains fragility and vulnerability curves that can be used to evaluate the expected or potential damages to infrastructure assets due to flooding, earthquakes, windstorms and landslides. The database consists of three Excel-spreadsheets:

• Table_D1_Summary_CI_Vulnerability_Data: summary table with information on hazard, exposure, and vulnerability characteristics as well as a number of details regarding reliability and reference purposes.
• Table_D2_Hazard_Fragility_and_Vulnerability Curves: collection of fragility and vulnerability curves
• Table_D3_Costs: cost values that can be used in combination with the curves for the estimation of asset damages

Please consult the following publication for detailed information: Nirandjan, S., Koks, E. E., Ye, M., Pant, R., van Ginkel, K. C. H., Aerts, J. C. J. H., and Ward, P. J.: Review article: Physical Vulnerability Database for Critical Infrastructure Multi-Hazard Risk Assessments – A systematic review and data collection, Nat. Hazards Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/nhess-2023-208, in review, 2024.

The Credit Risk Database market has emerged as a crucial component for financial institutions, offering a centralized platform for the assessment and management of credit risk. With the global financial landscape becoming increasingly complex, the demand for comprehensive credit risk analysis tools has surged. These

Risk Register Platforms for Public Safety Market Outlook

According to our latest research, the global market size for Risk Register Platforms for Public Safety reached USD 2.47 billion in 2024, with a robust compound annual growth rate (CAGR) of 12.3% projected from 2025 to 2033. This growth trajectory is expected to take the market to a value of USD 7.01 billion by 2033. The primary drivers behind this expansion are the increasing prioritization of risk mitigation strategies by public safety organizations, rapid digital transformation, and the rising frequency and complexity of emergencies and compliance requirements worldwide.

The growth of the Risk Register Platforms for Public Safety market is significantly influenced by the mounting pressure on public safety agencies to proactively manage and mitigate risks. With the increasing occurrence of both natural and man-made disasters, agencies are compelled to adopt advanced digital solutions that can centralize, track, and manage risks effectively. The integration of risk register platforms enables these organizations to systematically identify, assess, and prioritize risks, ensuring swift and informed decision-making during critical situations. The proliferation of data-driven governance and the need for real-time risk assessment further fuel the demand for robust, scalable, and user-friendly risk register solutions, especially as public safety operations become more complex and interconnected.

Furthermore, the ongoing digital transformation across public safety sectors is playing a pivotal role in propelling the adoption of risk register platforms. The shift towards cloud-based and integrated digital tools is empowering agencies with enhanced visibility, collaboration, and automation capabilities. These platforms not only streamline incident management and compliance processes but also facilitate seamless communication among stakeholders. Additionally, the increasing regulatory scrutiny and emphasis on transparency and accountability in public safety operations are prompting agencies to invest in systems that can document, audit, and report risks comprehensively. This trend is particularly pronounced in regions with stringent compliance mandates and a high focus on public safety modernization.

Another crucial growth factor is the rising need for interoperability and data integration among various public safety departments and agencies. Risk register platforms are evolving to offer cross-functional capabilities that cater to the unique requirements of law enforcement, fire departments, emergency medical services, and government agencies. The ability to integrate with other mission-critical systems, such as incident response, asset management, and communication platforms, is becoming a key differentiator. As agencies seek to break down silos and foster a holistic approach to risk management, the demand for flexible, customizable, and scalable risk register platforms continues to surge, driving market expansion across diverse end-user segments.

From a regional perspective, North America currently dominates the Risk Register Platforms for Public Safety market, accounting for the largest share in 2024, owing to its advanced public safety infrastructure, high technology adoption rate, and strong regulatory frameworks. However, the Asia Pacific region is anticipated to witness the highest growth rate over the forecast period, fueled by increasing investments in public safety modernization, urbanization, and the adoption of digital risk management solutions by emerging economies. Europe also represents a significant market, driven by stringent compliance requirements and proactive risk management initiatives by government agencies and public safety organizations.

Component Analysis

The Risk Register Platforms for Public Safety market is segmented by component into software and services. The software segment holds the largest market share, as public safety organizations seek advanced digital solutions for risk identification, assessment, and mitigation. Leading software platforms offer comprehensive features such as customizable risk matrices, automated notifications, real-time analytics, and integration with other public safety systems. The demand for intuitive, scalable, and secure software is rising, as agencies require platforms that can adapt to evolving risk profiles and operational requirements. The proliferation o

Abstract

The Maranoa-Balonne-Condamine Impact and Risk Analysis Database (Analysis Database) is a fit-for-purpose geospatial information system developed for the Impact and Risk Analysis (Component 3-4) products of the Bioregional Assessment Technical Programme (BATP).

The version provided here for public download has been slightly modified to remove restricted material such as the co-ordinates of protected or threatened species. This version was used to populate BA Explorer.

The Analysis Database brings together many of the data sets used in Components 1 and 2 of the assessments and includes hydrology and hydrogeology modelling results, landscape classes and economic, sociocultural and ecological assets. These data sets are listed in the Component 1 and 2 products under the Assessments tab in http://www.bioregionalassessments.gov.au/.

An Analysis Database of common design and schema was implemented for each subregion where a full Impact and Risk Analysis was completed. To populate each database, input datasets were transformed, normalised and inserted into their respective Analysis Databases in accord with the common design and schema. The approach enabled the universal treatment of data analysis across all bioregions despite data being of different specifications and origins.

The Analysis Database includes all the data used for the assessment of the subregion with the exception of those datasets that were not provided to the program with an open access licence. The database is constructed using the Open Source platform PostgreSQL coupled with PostGIS. This technology was considered to better enable the provenance and transparency requirements of the Programme. The files provided here have been prepared using the PostgreSQL version 9.5 SQL Dump function - pg_dump.

A detailed description of the Analysis Database, its design, structure and application is provided in the supporting documentation: http://data.bioregionalassessments.gov.au/dataset/05e851cf-57a5-4127-948a-1b41732d538c

Purpose

The Maranoa-Balonne-Condamine Impact and Risk Analysis Database (Analysis Database) is the geospatial database for completing the Impact and Risk Analysis component of the Maranoa-Balonne-Condamine Bioregional Assessment. This includes the creating of results, tables and maps that appear in the relevant Products of each assessment. The database also manages the data used by the BA Explorer.

An individual instance of the Analysis Database was developed for each subregion where a component 3-4 Impact and Risks Assessment was conducted. With the exception of the subregion-specific data contained within it and the removal of restricted data records, each analysis database is of identical design and structure.

Dataset History

This Analysis Database is an instance of PostgreSQL version 9.5 hosted on Linux Red Hat Enterprise Linux version 4.8.5-4. PostgreSQL geospatial capabilities are provided by POSTGIS version 2.2.

Data pre-processing and upload into each PostgreSQL database was completed using FME Desktop (Oracle Edition) version 2016.1.2.1. Analysis data and results are provided to users and systems via the geospatial services of Geoserver version 2.9.1. Scientific analysis and mapping was undertaken by connecting a range of data using a combination of Microsoft Excel, QGIS and ArcMap systems.

During the Programme and for its working life, the Analysis Database was hosted and managed on instances of Amazon Web Services managed by Geoscience Australia and the Bureau of Meteorology.

Dataset Citation

Bioregional Assessment Programme (2017) MBC Impact and Risk Analysis Database v01. Bioregional Assessment Derived Dataset. Viewed 25 October 2017, http://data.bioregionalassessments.gov.au/dataset/69075f3e-67ba-405b-8640-96e6cb2a189a.

Dataset Ancestors

• Derived From QLD Dept of Natural Resources and Mines, Groundwater Entitlements 20131204

• Derived From Surface Geology of Australia, 1:1 000 000 scale, 2012 edition

• Derived From Asset database for the Maranoa-Balonne-Condamine subregion on 16 June 2015

• Derived From South East Queensland GDE (draft)

• Derived From Geofabric Surface Cartography - V2.1

• Derived From Environmental Asset Database - Commonwealth Environmental Water Office

• Derived From QLD Dept of Natural Resources and Mines, Surface Water Entitlements 131204

• Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

• Derived From Catchment Scale Land Use of Australia - 2014

• Derived From Surface water preliminary assessment extent for the Maranoa-Balonne-Condamine subregion - v02

• Derived From MBC Groundwater model domain boundary

• Derived From Key Environmental Assets - KEA - of the Murray Darling Basin

• Derived From Bioregional Assessment areas v03

• Derived From MBC Groundwater model ACRD 5th to 95th percentile drawdown

• Derived From Permanent and Semi-Permanent Waterbodies of the Lake Eyre Basin (Queensland and South Australia) (DRAFT)

• Derived From Receptors for the Maranoa-Balonne-Condamine subregion

• Derived From Bioregional Assessment areas v01

• Derived From Bioregional Assessment areas v02

• Derived From MBC Assessment Units 20160714 v01

• Derived From Victoria - Seamless Geology 2014

• Derived From Matters of State environmental significance (version 4.1), Queensland

• Derived From Communities of National Environmental Significance Database - RESTRICTED - Metadata only

• Derived From Bioregional Assessment areas v06

• Derived From Asset database for the Maranoa-Balonne-Condamine subregion on 9 June 2015

• Derived From Queensland wetland data version 3 - wetland areas.

• Derived From Groundwater Preliminary Assessment Extent (PAE) for the Maranoa Balonne Condamine (MBC) subregion - v02

• Derived From National Groundwater Dependent Ecosystems (GDE) Atlas (including WA)

• Derived From Asset database for the Maranoa-Balonne-Condamine subregion on 05 February 2016

• Derived From MBC Groundwater model layer boundaries

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From Baseline drawdown Layer 1 - Condamine Alluvium

• Derived From MBC Assessment unit codified by regional watertable

• Derived From QLD Dept of Natural Resources and Mines, Groundwater Entitlements linked to bores and NGIS v4 28072014

• Derived From MBC Assessment Units 20160714 v02

• Derived From MBC Groundwater model water balance areas

• Derived From Asset database for the Maranoa-Balonne-Condamine subregion on 25 February 2015

• Derived From Australia - Species of National Environmental Significance Database

• Derived From MBC Groundwater model uncertainty analysis

• Derived From Spring vents assessed for the Surat Underground Water Impact Report 2012

• Derived From Collaborative Australian Protected Areas Database (CAPAD) 2010 (Not current release)

• **Derived

Risk Data Platform for Insurance Market Outlook

According to our latest research, the Risk Data Platform for Insurance market size reached USD 5.6 billion globally in 2024, demonstrating robust momentum driven by digital transformation across the insurance industry. The market is anticipated to expand at a CAGR of 13.2% from 2025 to 2033, with the forecasted market size projected to reach USD 16.1 billion by 2033. This substantial growth can be attributed to the increasing demand for advanced analytics, regulatory compliance, and the need for robust risk management frameworks in a rapidly evolving insurance landscape.

One of the primary growth factors fueling the Risk Data Platform for Insurance market is the exponential rise in data volume and complexity within the insurance sector. As insurers handle vast datasets from multiple sources, including IoT devices, telematics, social media, and third-party databases, the need for sophisticated risk data platforms becomes paramount. These platforms enable insurers to aggregate, cleanse, and analyze data in real time, supporting more accurate risk assessment and pricing. Furthermore, the proliferation of digital channels and the adoption of connected devices have introduced new types of risks, compelling insurers to leverage advanced data platforms to detect emerging threats and mitigate potential losses. The shift towards data-driven decision-making has thus become a cornerstone for insurers aiming to maintain competitiveness and operational efficiency.

Another significant driver is the evolving regulatory landscape, which places increasing emphasis on transparency, data governance, and compliance. Regulatory bodies worldwide are implementing stringent guidelines for data management, privacy, and reporting, particularly in the wake of high-profile data breaches and financial scandals. Risk data platforms are essential tools for insurers to ensure compliance with frameworks such as Solvency II, IFRS 17, and GDPR, providing robust audit trails and automated reporting capabilities. The integration of artificial intelligence and machine learning within these platforms further enhances their ability to identify suspicious patterns, support anti-fraud initiatives, and ensure adherence to regulatory mandates. Consequently, insurers are investing heavily in risk data platforms to avoid costly penalties and reputational damage while fostering a culture of compliance.

The growing sophistication of cyber threats and the increasing incidence of insurance fraud are also propelling the adoption of risk data platforms. As digital transformation accelerates, insurers face heightened risks related to cyberattacks, identity theft, and fraudulent claims. Advanced risk data platforms equipped with real-time analytics, anomaly detection, and predictive modeling capabilities empower insurers to proactively identify and mitigate these risks. By leveraging big data and AI-driven insights, insurers can enhance their fraud detection mechanisms, streamline claims processing, and improve underwriting accuracy. This not only results in reduced losses but also enhances customer trust and satisfaction, further driving market growth.

From a regional perspective, North America continues to lead the Risk Data Platform for Insurance market, accounting for the largest revenue share in 2024. The region's dominance is underpinned by the presence of major insurance providers, early adoption of advanced technologies, and a mature regulatory environment. Europe follows closely, driven by stringent compliance requirements and a strong focus on data privacy. Meanwhile, the Asia Pacific region is witnessing the fastest growth, fueled by rapid digitalization, expanding insurance penetration, and increasing investments in insurtech. Latin America and the Middle East & Africa are also emerging as promising markets, albeit at a slower pace, as insurers in these regions begin to recognize the value of integrated risk data solutions.

Abstract

The Galilee Impact and Risk Analysis Database (Analysis Database) is a fit-for-purpose geospatial information system developed for the Impact and Risk Analysis (Component 3-4) products of the Bioregional Assessment Technical Programme (BATP). The Analysis Database brings together many of the data sets of the scientific disciplines of the Programme and includes modelling results from hydrogeology and hydrology, landscape classes and economic, sociocultural and ecological assets. These data sets are listed in the Data Register for each subregion and can be found on the Bioregional Assessments web site (http://www.bioregionalassessments.gov.au/).

An Analysis Database of common design and schema was implemented for each individual subregion where a full Impact and Risk Analysis was completed. To populate each database, input datasets were transformed, normalised and inserted into their respective Analysis Database in accord with the common design and schema. The approach enabled the universal treatment of data analysis across all bioregions despite data being of a different specification and origin.

The Analysis Database provided for this subregion is an exact replica of the original used for the assessment of the subregion with the exception that a few spatial data for individual Assets subject to restrictions have been removed before publication. The restrictions are typically for threatened species spatial data but occasionally, restrictive licencing conditions imposed by some custodians prevented publication of some data. The database is constructed using the Open Source platform PostgreSQL coupled with PostGIS. This technology was considered to better enable the provenance and transparency requirements of the Programme. The files provided here have been prepared using the PostgreSQL version 9.5 SQL Dump function - pg_dump.

A detailed description of the Analysis Database, its design, structure and application is provided in the supporting documentation: http://data.bioregionalassessments.gov.au/dataset/05e851cf-57a5-4127-948a-1b41732d538c

Purpose

The Galilee Impact and Risk Analysis Database (Analysis Database) is the geospatial database for completing the Impact and Risk Analysis component of a Bioregional Assessment. This includes the creating of results, tables and maps that appear in the relevant Products of each assessment. The database also manages the data used by the BA Explorer.

An individual instance of the Analysis Database was developed for each subregion where a component 3-4 Impact and Risks Assessment was conducted. With the exception of the subregion-specific data contained within it and the removal of restricted data records, each analysis database is of identical design and structure.

Dataset History

This Analysis Database is an instance of PostgreSQL version 9.5 hosted on Linux Red Hat Enterprise Linux version 4.8.5-4. PostgreSQL geospatial capabilities are provided by POSTGIS version 2.2.

Data pre-processing and upload into each PostgreSQL database was completed using FME Desktop (Oracle Edition) version 2016.1.2.1. Analysis data and results are provided to users and systems via the geospatial services of Geoserver version 2.9.1. Scientific analysis and mapping was undertaken by connecting a range of data using a combination of Microsoft Excel, QGIS and ArcMap systems.

During the Programme and for its working life, the Analysis Database was hosted and managed on instances of Amazon Web Services managed by Geoscience Australia and the Bureau of Meteorology.

Dataset Citation

Bioregional Assessment Programme (2018) GAL Impact and Risk Analysis Database v01. Bioregional Assessment Derived Dataset. Viewed 12 December 2018, http://data.bioregionalassessments.gov.au/dataset/3dbb5380-2956-4f40-a535-cbdcda129045.

Dataset Ancestors

• Derived From QLD Dept of Natural Resources and Mines, Groundwater Entitlements 20131204

• Derived From Galilee Drawdown Rasters

• Derived From Galilee Groundwater Model, Hydrogeological Formation Extents v01

• Derived From GAL SW Quantiles Interpolation for IMIA Database

• Derived From SA Petroleum Production License Applications

• Derived From Galilee tributary catchments

• Derived From Springs of the Galilee subregion - Points Geometry

• Derived From GAL Aquifer Formation Extents v01

• Derived From Geofabric Surface Cartography - V2.1

• Derived From SA Mineral and/or Opal Exploration Licenses

• Derived From Environmental Asset Database - Commonwealth Environmental Water Office

• Derived From Geoscience Australia GEODATA TOPO series - 1:1 Million to 1:10 Million scale

• Derived From GAL Assessment Units 1000m 20160522 v01

• Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

• Derived From Phanerozoic OZ SEEBASE v2 GIS

• Derived From Asset database for the Galilee subregion on 2 December 2014

• Derived From Key Environmental Assets - KEA - of the Murray Darling Basin

• Derived From Bioregional Assessment areas v03

• Derived From SA Petroleum Exploration Licences/Permits

• Derived From South Australia Mineral Leases Production, 6 March 2013

• Derived From BA ALL Assessment Units 1000m 'super set' 20160516_v01

• Derived From Kevin's Corner Project Environmental Impact Statement

• Derived From Galilee Hydrological Response Variable (HRV) model

• Derived From Asset list for Galilee - 20140605

• Derived From Bioregional Assessment areas v01

• Derived From Bioregional Assessment areas v02

• Derived From QLD Current Exploration Permits for Minerals (EPM) in Queensland 6/3/2013

• Derived From Victoria - Seamless Geology 2014

• Derived From Galilee groundwater numerical modelling AEM models

• Derived From GAL Surface Water Reaches for Risk and Impact Analysis 20180803

• Derived From Matters of State environmental significance (version 4.1), Queensland

• Derived From Communities of National Environmental Significance Database - RESTRICTED - Metadata only

• Derived From National Groundwater Dependent Ecosystems (GDE) Atlas

• Derived From GAL Aquifer Formation Extents v02

• Derived From Queensland wetland data version 3 - wetland areas.

• Derived From Galilee surface water modelling nodes

• Derived From GAL Eco HRV SW Quantiles Interpolation for IMIA Database

• Derived From National Groundwater Dependent Ecosystems (GDE) Atlas (including WA)

• Derived From China Stone Coal Project initial advice statement

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From South Australia Mineral Production Claims, 6 March 2013

• Derived From Onsite and offsite mine infrastructure for the Carmichael Coal Mine and Rail Project, Adani Mining Pty Ltd 2012

*

Primary data are 25 yrs of discharge (i.e. river flow) for multiple sites on the Ohio River. Supporting data are water quality variables for select sites on the Ohio river, including nutrient species, information from algal cell counts, and in-situ sensor data. This dataset is associated with the following publication: Nietch, C., L. Gains-Germain, J. Lazorchak, S. Keely, G. Youngstrom, E.M. Urichich, B. Astifan, A. DaSilva, and H. Mayfield. Development of a Risk Characterization Tool for Harmful Cyanobacteria Blooms on the Ohio River. WATER. MDPI AG, Basel, SWITZERLAND, 14(4): 644, (2022).

Risk Data Aggregation and Reporting for Banks Market Outlook

According to our latest research, the global risk data aggregation and reporting for banks market size reached USD 7.9 billion in 2024, driven by the increasing regulatory requirements and the growing complexity of banking operations. The market is expected to expand at a robust CAGR of 14.2% from 2025 to 2033, reaching a projected value of USD 22.3 billion by 2033. This impressive growth is primarily fueled by the ongoing digital transformation initiatives within the banking sector, as well as the heightened focus on risk management and compliance. As per our latest research, banks globally are investing in advanced data aggregation and reporting solutions to meet evolving regulatory mandates and enhance operational efficiency.

One of the principal growth factors for the risk data aggregation and reporting for banks market is the tightening regulatory landscape. Financial authorities such as the Basel Committee on Banking Supervision (BCBS) have established stringent guidelines, notably BCBS 239, which require banks to improve their risk data aggregation capabilities and reporting practices. This has led to a surge in demand for robust solutions that can ensure data accuracy, consistency, and timeliness. Banks are compelled to invest in advanced software and services that facilitate real-time data integration, risk assessment, and regulatory reporting. The growing volume and complexity of banking transactions further underscore the need for comprehensive risk data aggregation and reporting frameworks, as traditional manual processes are no longer sufficient to meet regulatory expectations.

Another significant driver is the rapid digitalization of the banking sector. As banks embrace digital transformation, they are generating massive amounts of data from various sources, including online transactions, customer interactions, and third-party integrations. Efficient risk data aggregation and reporting solutions enable banks to harness this data, providing actionable insights for risk management and strategic decision-making. The adoption of technologies such as artificial intelligence, machine learning, and big data analytics is enhancing the capabilities of these solutions, allowing banks to identify emerging risks, optimize capital allocation, and improve overall governance. This digital shift is not just a response to regulatory pressure but also a strategic move to gain competitive advantage in a fast-evolving financial landscape.

Furthermore, the increasing focus on operational resilience and business continuity is propelling the adoption of risk data aggregation and reporting solutions. Banks are recognizing the need to quickly aggregate and analyze data from multiple sources to detect vulnerabilities, prevent fraud, and ensure compliance with internal and external policies. The COVID-19 pandemic has further highlighted the importance of real-time risk management and agile reporting, as financial institutions faced unprecedented disruptions and market volatility. As a result, investments in risk data infrastructure are becoming a top priority for banks of all sizes, paving the way for sustained market growth over the forecast period.

From a regional perspective, North America currently dominates the risk data aggregation and reporting for banks market, followed closely by Europe and Asia Pacific. The United States, in particular, has a mature banking sector with stringent regulatory requirements, driving early adoption of advanced risk data solutions. Meanwhile, the Asia Pacific region is witnessing the fastest growth, fueled by rapid digitalization, expanding banking networks, and increasing regulatory oversight in emerging economies such as China and India. Europe remains a key market due to the implementation of comprehensive financial regulations and the presence of major global banks. Latin America and the Middle East & Africa are also showing steady progress, albeit at a slower pace, as banks in these regions gradually upgrade their risk management capabilities.

Main data files comprise 22 variables in three subcategories of risk (political, financial, and economic) for 146 countries for 1984-2021. Data are annual averages of the components of the ICRG Risk Ratings (Tables 3B, 4B, and 5B) published in the International Country Risk Guide. Indices include: political: government stability, socioeconomic conditions, investment profile, internal conflict, external conflict, corruption, military in politics, religion in politics, law and order, ethnic tensions, democratic accountability, and bureaucratic quality; financial: foreign debt, exchange rate stability, debt service, current account, international liquidity; and economic: inflation, GDP per head, GDP growth, budget balance, current account as % of GDP. Table 2B provides annual averages of the composite risk rating. Table 3Ba provides historical political risk subcomponents on a monthly basis from May 2001-February 2022. Also includes the IRIS-3 dataset by Steve Knack and Philip Keefer, which covers the period of 1982-1997 and computed scores for six additional political risk variables: corruption in government, rule of law, bureaucratic quality, ethnic tensions, repudiation of contracts by government, and risk of expropriation. Additional data files provide country risk ratings and databanks (economic and social indicators) for new emerging markets for 2000-2009.

The credit risk evaluation data generated by a commercial bank’s personal consumption loans.

Abstract

The Namoi Impact and Risk Analysis Database (Analysis Database) is a fit-for-purpose geospatial information system developed for the Impact and Risk Analysis (Component 3-4) products of the Bioregional Assessment Technical Programme (BATP). The Analysis Database brings together many of the data sets of the scientific disciplines of the Programme and includes modelling results from hydrogeology and hydrology, landscape classes and economic, sociocultural and ecological assets. These data sets are listed in the Data Register for each subregion and can be found on the Bioregional Assessments web site (http://www.bioregionalassessments.gov.au/).

An Analysis Database of common design and schema was implemented for each individual subregion where a full Impact and Risk Analysis was completed. To populate each database, input datasets were transformed, normalised and inserted into their respective Analysis Database in accord with the common design and schema. The approach enabled the universal treatment of data analysis across all bioregions despite data being of a different specification and origin.

The Analysis Database provided for this subregion is an exact replica of the original used for the assessment of the subregion with the exception that a few spatial data for individual Assets subject to restrictions have been removed before publication. The restrictions are typically for threatened species spatial data but occasionally, restrictive licencing conditions imposed by some custodians prevented publication of some data. The database is constructed using the Open Source platform PostgreSQL coupled with PostGIS. This technology was considered to better enable the provenance and transparency requirements of the Programme. The files provided here have been prepared using the PostgreSQL version 9.5 SQL Dump function - pg_dump.

A detailed description of the Analysis Database, its design, structure and application is provided in the supporting documentation: http://data.bioregionalassessments.gov.au/dataset/05e851cf-57a5-4127-948a-1b41732d538c

Purpose

The Namoi Impact and Risk Analysis Database (Analysis Database) is the geospatial database for completing the Impact and Risk Analysis component of a Bioregional Assessment. This includes the creating of results, tables and maps that appear in the relevant Products of each assessment. The database also manages the data used by the BA Explorer.

An individual instance of the Analysis Database was developed for each subregion where a component 3-4 Impact and Risks Assessment was conducted. With the exception of the subregion-specific data contained within it and the removal of restricted data records, each analysis database is of identical design and structure.

Dataset History

This Analysis Database is an instance of PostgreSQL version 9.5 hosted on Linux Red Hat Enterprise Linux version 4.8.5-4. PostgreSQL geospatial capabilities are provided by POSTGIS version 2.2.

Data pre-processing and upload into each PostgreSQL database was completed using FME Desktop (Oracle Edition) version 2016.1.2.1. Analysis data and results are provided to users and systems via the geospatial services of Geoserver version 2.9.1. Scientific analysis and mapping was undertaken by connecting a range of data using a combination of Microsoft Excel, QGIS and ArcMap systems.

During the Programme and for its working life, the Analysis Database was hosted and managed on instances of Amazon Web Services managed by Geoscience Australia and the Bureau of Meteorology.

Dataset Citation

Bioregional Assessment Programme (2018) NAM Impact and Risk Analysis Database v01. Bioregional Assessment Derived Dataset. Viewed 11 December 2018, http://data.bioregionalassessments.gov.au/dataset/1549c88d-927b-4cb5-b531-1d584d59be58.

Dataset Ancestors

• Derived From River Styles Spatial Layer for New South Wales

• Derived From Geofabric Surface Network - V2.1

• Derived From Surface Geology of Australia, 1:1 000 000 scale, 2012 edition

• Derived From HUN SW footprint shapefiles v01

• Derived From HUN Groundwater footprint polygons v01

• Derived From Namoi Environmental Impact Statements - Mine footprints

• Derived From Namoi CMA Groundwater Dependent Ecosystems

• Derived From Landscape classification of the Namoi preliminary assessment extent

• Derived From Environmental Asset Database - Commonwealth Environmental Water Office

• Derived From Soil and Landscape Grid National Soil Attribute Maps - Clay 3 resolution - Release 1

• Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

• Derived From Bioregional_Assessment_Programme_Catchment Scale Land Use of Australia - 2014

• Derived From Interim Biogeographic Regionalisation for Australia (IBRA), Version 7 (Regions)

• Derived From Key Environmental Assets - KEA - of the Murray Darling Basin

• Derived From Bioregional Assessment areas v03

• Derived From GIS analysis of HYDMEAS - Hydstra Groundwater Measurement Update: NSW Office of Water - Nov2013

• Derived From BA ALL Assessment Units 1000m 'super set' 20160516_v01

• Derived From Mean Annual Climate Data of Australia 1981 to 2012

• Derived From Asset list for Namoi - CURRENT

• Derived From Bioregional Assessment areas v01

• Derived From Bioregional Assessment areas v02

• Derived From Namoi bore locations, depth to water for June 2012

• Derived From Victoria - Seamless Geology 2014

• Derived From Murray-Darling Basin Aquatic Ecosystem Classification

• Derived From HUN SW GW Mine Footprints for IMIA 20170303 v03

• Derived From Climate model 0.05x0.05 cells and cell centroids

• Derived From Namoi hydraulic conductivity measurements

• Derived From Namoi groundwater uncertainty analysis

• Derived From Historical Mining footprints DTIRIS HUN 20150707

• Derived From Namoi NGIS Bore analysis for 2012

• Derived From Australian 0.05º gridded chloride deposition v2

• Derived From Communities of National Environmental Significance Database - RESTRICTED - Metadata only

• Derived From Bioregional Assessment areas v06

• Derived From NAM Analysis Boundaries 20160908 v01

• Derived From Namoi groundwater drawdown grids

• Derived From National Groundwater Dependent Ecosystems (GDE) Atlas (including WA)

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From BOM, Australian Average Rainfall Data from 1961 to 1990

• Derived From Namoi Existing Mine Development Surface Water Footprints

• Derived From Surface water Preliminary Assessment Extent (PAE) for the Namoi (NAM) subregion - v03

• Derived From BILO Gridded Climate Data: Daily Climate Data for each year from 1900 to 2012

• Derived From [National Surface Water sites

The Surgery Risk Assessment (SRA) database is part of the VA Surgical Quality Improvement Program (VASQIP). This database contains assessments of selected surgical operations performed at Veteran Affairs Medical Centers (VAMCs). Addition to the SRA database requires that the surgery is Major (as defined by the Current Procedural Terminology (CPT) codes assigned to the surgery), it must not be cardiac related, and it may not be concurrent with another surgery. Frequently performed other types of surgeries may also be excluded. Nurse reviewers at VAMCs gather the information from surgical data located in the Veterans Health Information Systems and Technology Architecture (VistA) environment. Information is also collected from pre-and post-operative charts and from interviews with patients. This information is entered into VistA and transmitted daily by a batch process to the Hines Office of Information & Technology (OI&T) Field Office. While the database has been in operation since 1995, the system only contains data for the current fiscal year. The data from previous fiscal years is archived if later retrieval is needed. Valid transmissions are sent to the VASQIP office at Denver for analysis. Information from non-assessed surgeries is transmitted from the VAMCs to the Hines OI Field Office monthly. This is also passed along to VASQIP at Denver. The users of this database include the VASQIP Executive Board.

The files contain our firm-level measure of cybersecurity risk as well as replication codes in SAS & STATA for our study entitled "Cybersecurity Risk"

These datasets provide aggregated community risk scores for exposure to flooding using the First Street Foundation Flood Model (Version 1.3) at the county and zip code level. county_flood_score and zcta_flood_score provide the overall community risk score. county_flood_category_score and zcta_flood_category_score provide the risk score to specific categories of infrastructure. Each category; critical infrastructure, social infrastructure, residential properties, roads, and commercial properties, is a component of the overall community risk.

If you are interested in acquiring First Street flood data, you can request to access the data here. More information on First Street's flood risk statistics can be found here and information on First Street's hazards can be found here.

The following fields are in the overall risk datasets:

Attribute

Description

county_id

The county FIPS code

count

The count (#) of infrastructure facilities

flood_score

A score of 1, 2, 3, 4, or 5 is shown. Community risk rankings represent risk as Minimal, Minor (1), Moderate (2), Major (3), Severe (4) and Extreme (5). Minimal risk is a case where no facilities within a category have flood risk. County level risks are ranked based on how their total depths compare to counties across the country.

The following fields are in the category risk datasets:

Attribute

Description

FIPS

County FIPS code

ZIP_CODE

ZIP code

count

The approximate length of roads (miles) within the geography of aggregation (i.e. ZIP Code, County)

flood_score

A score (Community Risk level) of 0, 1, 2, 3, 4, or 5 is shown. Community risk levels represent risk as Minimal (0), Minor (1), Moderate (2), Major (3), Severe (4) and Extreme (5). Minimal risk is a case where no facilities within a category have flood risk. ZIP Code and County level risks are assessed based on how their total depths compare to ZIP Codes and Counties across the country.

risk_direction

A score of 1, -1, or 0 is shown. These note if flood risk is expected to increase (1), decrease (-1), or remain constant (0) over the next 30 years.

infrastructure_category_id

1= critical infrastructure, 4 = social infrastructure , 6 = residential properties, 8 - roads, 9 = commercial properties