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.

Credit Risk Database Market Outlook

The global credit risk database market size was valued at USD 2.8 billion in 2023 and is expected to reach USD 5.6 billion by 2032, growing at a CAGR of 7.8% during the forecast period. The growth of this market can be attributed to increasing regulatory requirements for risk management, advancements in data analytics, and the rising need for efficient credit risk assessment tools across various industries. With financial institutions and enterprises focusing more on mitigating risks and ensuring robust financial health, the demand for comprehensive credit risk databases is poised to rise significantly.

One of the primary growth factors driving the credit risk database market is the increasing regulatory scrutiny across the globe. Financial institutions are under immense pressure to comply with stringent regulations such as Basel III in banking, which necessitates robust risk assessment and management frameworks. These regulations mandate institutions to maintain adequate capital reserves and to perform comprehensive risk evaluations, thereby driving the demand for advanced credit risk databases. Such tools provide crucial insights that help in identifying potential defaults and enabling proactive risk mitigation strategies.

Technological advancements, particularly in the realms of big data and artificial intelligence, are significantly contributing to the market's growth. Modern credit risk databases leverage AI and machine learning algorithms to analyze vast datasets in real-time, providing more accurate and timely risk assessments. By utilizing predictive analytics, these databases can forecast potential credit risks and financial distress, which allows companies to take preemptive measures. The integration of such advanced technologies is expected to propel market growth further as businesses increasingly adopt these solutions for enhanced decision-making processes.

Moreover, the growing digitization and the proliferation of digital financial services have elevated the importance of efficient credit risk management tools. As financial transactions increasingly shift online, the volume of data generated has surged, necessitating more sophisticated analysis tools to manage credit risk. This trend is especially prominent in emerging economies where digital banking and fintech services are rapidly expanding. The ability to process and analyze vast amounts of data accurately and quickly is becoming indispensable, further driving the adoption of credit risk databases.

Credit Rating Software plays a pivotal role in the landscape of credit risk databases by providing essential tools that enhance the accuracy and efficiency of credit assessments. These software solutions integrate seamlessly with credit risk databases, offering advanced analytics and real-time data processing capabilities. By leveraging sophisticated algorithms and data models, credit rating software enables organizations to evaluate creditworthiness with greater precision, thereby reducing the likelihood of defaults. The integration of credit rating software into existing systems not only streamlines the risk assessment process but also supports compliance with regulatory requirements, making it an indispensable component for financial institutions and enterprises aiming to maintain robust credit risk management frameworks.

From a regional perspective, North America is expected to hold the largest market share due to the early adoption of advanced technologies and stringent regulatory frameworks. The presence of major market players and a well-established financial sector also contribute to the region's dominance. However, the Asia Pacific region is anticipated to witness the fastest growth, driven by the rapid expansion of the financial sector, increasing regulatory requirements, and growing awareness about the benefits of credit risk databases. This region's burgeoning economies, such as China and India, offer lucrative opportunities for market players.

Component Analysis

The credit risk database market by component is segmented into software and services. The software segment includes platforms and applications that provide credit risk assessment and management functionalities. These software solutions are equipped with advanced analytics tools and machine learning algorithms to facilitate real-time risk analysis and decision-making. The rising demand for integrated software solutions that offer seamless data integration and com

The Flood Risk Database (FRD) contains risk information and supporting data used to depict risk data on a project level. The primary risk data developed are the Changes Since Last FIRM, Risk Assessment Results, Areas of Mitigation Interest, and Depth and Annual Chance grids. The FRD is derived from Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, new mapping data, where available, and Hazus risk analysis. The Flood Risk Database and any accompanying products (e.g., Flood Risk Report, Flood Risk Map, and the average annualized loss) are published by the Federal Emergency Management Agency (FEMA).

Market Analysis: Credit Risk Database The global credit risk database market is projected to reach a value of USD XXX million in 2033, exhibiting a CAGR of XX% from 2025 to 2033. This growth is attributed to the increasing demand for comprehensive and accurate credit information to mitigate financial risks in various industries. Key growth drivers include rising regulatory compliance and risk management requirements, the proliferation of digital lending channels, and the adoption of advanced technologies such as AI and machine learning to enhance credit assessment capabilities. The market is segmented by type (Personal Data, Enterprise Data, Other) and application (Enterprise, Government, Other). Major players in the industry include Visymo, iZito, Creditbpo, Creditriskmonitor, Fidelity National Information Services, Inc., Experian plc, Creditsafe Group, SimpleRisk, Graydon UK Ltd, RepRisk AG, Marsh & McLennan Companies, Inc. Regionally, North America and Europe hold significant market shares due to the presence of established financial institutions and stringent regulatory frameworks. Asia-Pacific is expected to witness notable growth driven by the expansion of the fintech landscape and increasing demand for credit access in emerging markets.

Abstract Four documents describe the specifications, methods and scripts of the Impact and Risk Analysis Databases developed for the Bioregional Assessments Programme. They are: Bioregional Assessment Impact and Risk Databases Installation Advice (IMIA Database Installation Advice v1.docx). Naming Convention of the Bioregional Assessment Impact and Risk Databases (IMIA Project Naming Convention v39.docx). Data treatments for the Bioregional Assessment Impact and Risk Databases (IMIA Project …Show full descriptionAbstract Four documents describe the specifications, methods and scripts of the Impact and Risk Analysis Databases developed for the Bioregional Assessments Programme. They are: Bioregional Assessment Impact and Risk Databases Installation Advice (IMIA Database Installation Advice v1.docx). Naming Convention of the Bioregional Assessment Impact and Risk Databases (IMIA Project Naming Convention v39.docx). Data treatments for the Bioregional Assessment Impact and Risk Databases (IMIA Project Data Treatments v02.docx). Quality Assurance of the Bioregional Assessment Impact and Risk Databases (IMIA Project Quality Assurance Protocol v17.docx). This dataset also includes the Materialised View Information Manager (MatInfoManager.zip). This Microsoft Access database is used to manage the overlay definitions of materialized views of the Impact and Risk Analysis Databases. For more information about this tool, refer to the Data Treatments document. The documentation supports all five Impact and Risk Analysis Databases developed for the assessment areas: Maranoa-Balonne-Condamine: http://data.bioregionalassessments.gov.au/dataset/69075f3e-67ba-405b-8640-96e6cb2a189a Gloucester: http://data.bioregionalassessments.gov.au/dataset/d78c474c-5177-42c2-873c-64c7fe2b178c Hunter: http://data.bioregionalassessments.gov.au/dataset/7c170d60-ff09-4982-bd89-dd3998a88a47 Namoi: http://data.bioregionalassessments.gov.au/dataset/1549c88d-927b-4cb5-b531-1d584d59be58 Galilee: http://data.bioregionalassessments.gov.au/dataset/3dbb5380-2956-4f40-a535-cbdcda129045​ Purpose These documents describe end-to-end treatments of scientific data for the Impact and Risk Analysis Databases, developed and published by the Bioregional Assessment Programme. The applied approach to data quality assurance is also described. These documents are intended for people with an advanced knowledge in geospatial analysis and database administration, who seek to understand, restore or utilise the Analysis Databases and their underlying methods of analysis. Dataset History The Impact and Risk Analysis Database Documentation was created for and by the Information Modelling and Impact Assessment Project (IMIA Project). Dataset Citation Bioregional Assessment Programme (2018) Impact and Risk Analysis Database Documentation. Bioregional Assessment Source Dataset. Viewed 12 December 2018, http://data.bioregionalassessments.gov.au/dataset/05e851cf-57a5-4127-948a-1b41732d538c.

Synthesised loan risk database data on Loans to non-financial corporations includes loan amounts, maturities, and interest rates. To be accurate but not identify specific companies, the data was synthesised using specific software.

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.

Open the Data Resource: https://experience.arcgis.com/experience/e492db86d9b348399f4bd20330b4b274 This viewer shares a variety of flood hazard and risk data produced by the Federal Emergency Management Agency (FEMA). Some flood hazard and flood risk data produced by FEMA define minimum requirements for the National Flood Insurance Program (NFIP). This viewer includes these required NFIP data and includes other data showing current and potential future flood hazard and risk.

Floodlight provides comprehensive, asset-level climate risk and physical hazard assessments utilizing advanced satellite imagery, ground sensor data, artificial intelligence, and proprietary modeling techniques. Our approach is grounded in decades of scientific research, rigorous validation processes, and alignment with international standards, including ISO 9001 and the Intergovernmental Panel on Climate Change (IPCC) frameworks.

Our climate risk analysis covers both acute events—such as hurricanes, floods, wildfires, heatwaves, storms, and earthquakes—and chronic shifts, including sea-level rise, prolonged droughts, water scarcity, temperature variability, and permafrost thaw. By integrating detailed geographic, meteorological, and climate projection data, we quantify vulnerabilities specific to each asset, enabling organizations to proactively implement targeted adaptation and resilience measures.

Floodlight’s methodology combines global climate scenarios (Shared Socioeconomic Pathways - SSPs) with localized risk indices to provide precise insights into the likelihood and potential impact of various hazards. We use established tools, such as FEMA’s risk assessment models in the United States, and enhance these with cutting-edge satellite data from NASA, ESA, and commercial providers. Our assessments clearly outline potential financial exposures, such as rising insurance premiums, increased operational costs, asset value impacts, and capital expenditure requirements necessary to mitigate identified risks.

We recognize that climate risks are dynamic and evolving. As such, our platform emphasizes continuous monitoring and regular updating of asset vulnerability assessments to identify emerging threats promptly and evaluate the effectiveness of implemented mitigation measures.

Floodlight empowers businesses, property managers, governments, and communities to safeguard their investments, ensure operational continuity, and enhance long-term sustainability and resilience in the face of a changing climate.

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

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The Flood Risk Database (FRD) contains risk information and supporting data used to depict risk data on a project level. The primary risk data developed are the Changes Since Last FIRM, Risk Assessment Results, Areas of Mitigation Interest, and Depth and Annual Chance grids. The FRD is derived from Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, new mapping data, where available, and Hazus risk analysis. The Flood Risk Report, Flood Risk Map, and the average annualized loss are published by the Federal Emergency Management Agency (FEMA).

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 …Show full descriptionAbstract 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 From Queensland QLD - Regional - NRM - Water Asset Information Tool - WAIT - databases Derived From NSW Office of Water GW licence extract linked to spatial locations NIC v2 (28 February 2014) Derived From NSW Office of Water Surface Water Entitlements Locations v1_Oct2013 Derived From MBC Assessment Units 20161211 v01 Derived From Asset database for the Maranoa-Balonne-Condamine subregion on 26 June 2015 Derived From Geofabric Surface Catchments - V2.1 Derived From National Groundwater Information System (NGIS) v1.1 Derived From Birds Australia - Important Bird Areas (IBA) 2009 Derived From MBC ZoPHC and component layers 20170117 Derived From Queensland groundwater dependent ecosystems Derived From Gippsland Project boundary Derived From Queensland Water Commission, Underground Water Impact Report for the Surat Cumulative Management Area, 2012 - Report and Data Derived From Natural Resource Management (NRM) Regions 2010 Derived From Great Artesian Basin - Hydrogeology and Extent Boundary Derived From Species Profile and Threats Database (SPRAT) - Australia - Species of National Environmental Significance Database (BA subset - RESTRICTED - Metadata only) Derived From Geological Provinces - Full Extent Derived From MBC Analysis Boundaries 20160718 v01 Derived From MBC Groundwater model mine footprints Derived From MBC Groundwater model uncertainty plots Derived From Groundwater Preliminary Assessment Extent (PAE) for the Maranoa Balonne Condamine (MBC) subregion - v01 Derived From National Heritage List Spatial Database (NHL) (v2.1) Derived From QLD DNRM Licence Locations Linked to Cadastre Plan - v1 - 20140307 Derived From NSW Office of Water combined geodatabase of regulated rivers and water sharing plan regions Derived From Australia World Heritage Areas Derived From New South Wales NSW Regional CMA Water Asset Information WAIT tool databases, RESTRICTED Includes ALL Reports Derived From Landscape classification of the Maranoa-Balonne-Condamine preliminary assessment extent v02 Derived From New South Wales NSW - Regional - CMA - Water Asset Information Tool - WAIT - databases Derived From NSW Office of Water Groundwater Licence Extract NIC- Oct 2013 Derived From Landscape classification of the Maranoa-Balonne-Condamine preliminary assessment extent v03 Derived From National Groundwater Dependent Ecosystems (GDE) Atlas Derived From Directory of Important Wetlands in Australia (DIWA) Spatial Database (Public) Derived From MBC Coal mine extents Derived From MBC Groundwater model baseline 5th to 95th percentile drawdown Derived From Impact and Risk Analysis Database Documentation Derived From Preliminary Assessment Extent (PAE) for the Maranoa-Balonne-Condamine subregion - v03 Derived From NSW Office of Water Surface Water Licences in NIC linked to locations v1 (22 April 2014) Derived From Bioregional Assessment areas v04 Derived From Queensland

Data related to operational risk 2013

Problem Statement

👉 Download the case studies here

A global manufacturing company faced frequent supply chain disruptions caused by unforeseen events such as natural disasters, geopolitical issues, and supplier failures. These disruptions led to production delays, increased costs, and diminished customer satisfaction. The company required a proactive solution to identify potential risks, mitigate their impact, and ensure supply chain continuity.

Challenge

Implementing an effective supply chain risk management system presented several challenges:

Analyzing vast and diverse data sets, including supplier performance, logistics data, and external risk factors.

Predicting potential disruptions and their impact on the supply chain.

Providing actionable insights to decision-makers in real time to reduce response times.

Solution Provided

An advanced supply chain risk management system was developed using predictive analytics, machine learning models, and AI-driven risk assessment tools. The solution was designed to:

Monitor and analyze data from multiple sources, including suppliers, weather forecasts, and geopolitical indicators.

Predict potential risks and disruptions using machine learning algorithms.

Recommend mitigation strategies and alternative plans to minimize the impact of identified risks.

Development Steps

Data Collection

Aggregated data from internal supply chain systems, external risk databases, and third-party sources, such as weather services and market reports.

Preprocessing

Standardized and cleaned data to ensure accuracy and compatibility across multiple data sources and formats.

Model Development

Built predictive models to identify risks, such as supplier delays, transportation bottlenecks, and market volatility. Developed risk scoring algorithms to prioritize and classify risks based on severity and likelihood.

Validation

Tested the system using historical supply chain data and simulated risk scenarios to ensure accuracy and reliability in risk prediction.

Deployment

Integrated the system with the company’s supply chain management tools, enabling real-time monitoring and risk assessments.

Continuous Monitoring & Improvement

Established a feedback loop to refine predictive models and risk assessment algorithms based on new data and emerging trends.

Results

Enhanced Risk Detection

The system provided early warnings for potential disruptions, allowing the company to take proactive measures.

Reduced Supply Chain Disruptions

Risk mitigation strategies minimized the impact of disruptions, ensuring continuity in production and deliveries.

Improved Resilience

The system strengthened the company’s supply chain by identifying vulnerabilities and suggesting improvements.

Cost Savings

Proactive risk management reduced costs associated with delays, penalties, and emergency logistics.

Real-Time Decision-Making

AI-driven insights enabled quick and informed decisions, enhancing the overall efficiency of supply chain operations.

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

This dataset is an input to several tutorials for the software package an UNcertain Structure and Fragility Ensemble (UNSAFE) framework for property-level flood risk esimation. We upload this dataset as this persistent and unique link so that the UNSAFE examples are easier to follow, as the original data is only available at a URL.

This dataset was obtained in April, 2024 on the FEMA Flood Map Service Center: Welcome landing page. We clicked on Search All Products (highlighted in blue just below "Looking for more than just a current flood map?" and searched for Product ID FRD_02040202_PA_GeoTIFFs. Alternatively, one can search under "Jurisdiction" for PENNSYLVANIA -> PHILADELPHIA COUNTY -> PHILADELPHIA COUNTY ALL JURISDICTIONS and then click "Search." As of April of 2024 the corresponding directories were named "Effective Products," "Preliminary Products," "Pending Product," "Historic Products," and "Flood Risk Products." We clicked on Flood Risk Products -> Flood Risk Database and then download the GeoTIFFs file. This dataset was posted on 08/01/2016 and is 2190MB.

You can learn more about the FEMA Flood Risk Map products here: https://www.fema.gov/flood-maps/tools-resources/risk-map.

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

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.

DomainIQ is a comprehensive global Domain Name dataset for organizations that want to build cyber security, data cleaning and email marketing applications. The dataset consists of the DNS records for over 267 million domains, updated daily, representing more than 90% of all public domains in the world.

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