This dataset contains 2147 records of operational risk events for retail banking, combining real-world observed loss events (OLE) and synthetic available loss events (ALE) generated via Monte Carlo simulation.

Each record captures structured details about banking operational risk incidents, including:

Event Type (e.g., Cyber-Fraud, System Failure, Phishing)

Process Area (Retail Banking, ATM Network, E-Banking, etc.)

Loss Amount (kUSD) and Frequency (Nk)

Severity (Xi) estimated via loss distribution modeling

OpVar (%) at 99.9% confidence level (Basel II/III standard)

ML (Magnitude of Loss ratio) comparing synthetic stress to observed baseline

Macro-Financial Indicators (GDP growth %, VIX volatility index)

Target column for classification modeling (risk level: Low, Medium, High)

The dataset is designed to support operational risk estimation, scenario analysis, stress testing, and machine learning model development (e.g. for predicting loss severity or classifying risk levels).

It reflects realistic banking risk conditions across multiple business lines—such as e-banking, ATM networks, and payment platforms—and includes both historical data and simulated future scenarios to evaluate model generalization under diverse risk environments.

Nowadays the collection of operational risk data worldwide highly relies on human labour, leading to slow updates, data inconsistency, and limited quantity. There remains a substantial shortage of publicly accessible operational risk databases for risk analysis. This study proposes a new data collection framework by aggregating text mining methods to replace the exhausting manual collection process. The news about operational risk can be automatically collected from the web page, then its content is analyzed and the key information is extracted. Finally, the Public-Chinese Operational Loss Data (P-COLD) database for financial institutions is constructed and expanded. Each record contains 12 key information, such as occurrence time, loss amount, and business lines, offering a more thorough description of operational risk events. With 3,723 data records from 1986 to 2023, the P-COLD database has become one of the largest and most comprehensive external operational risk databases in China. We anticipate the P-COLD database will contribute to advancements in operational risk capital calculations, dependence analysis, and institutional internal controls.The P-COLD-English ver.xlsx is a cross-institutional database on operational risk data in China's banking sector, collected from publicly available sources and translated into English.The P-COLD-Chinese ver.xlsx is a cross-institutional database on operational risk data in China's banking sector, collected from publicly available sources and recorded in Chinese.(The P-COLD-English ver.xlsx is the English-translated version of P-COLD-Chinese ver.xlsx.)The Data dictionary.xlsx records the description of each field in P-COLD database.

Operational Loss Data Management Market Outlook

According to our latest research, the global Operational Loss Data Management market size reached USD 2.43 billion in 2024, and is projected to grow at a CAGR of 10.7% from 2025 to 2033. By the end of 2033, the market is expected to attain a value of USD 6.1 billion. This robust growth is primarily driven by the increasing regulatory pressure on financial institutions to enhance their risk management frameworks and the growing adoption of advanced analytics for operational risk mitigation.

The rapid digitalization of financial services and the proliferation of complex operational processes have significantly contributed to the expansion of the Operational Loss Data Management market. Organizations across various sectors, particularly in BFSI, are increasingly recognizing the importance of capturing, analyzing, and managing loss data to mitigate operational risks and comply with stringent regulatory requirements. The surge in cyber threats, internal fraud, and process failures has compelled enterprises to adopt comprehensive data management platforms that not only record loss events but also provide actionable insights to prevent future occurrences. This trend is further amplified by the integration of artificial intelligence and machine learning technologies, which enhance the predictive capabilities of operational loss data management systems, thus enabling organizations to proactively address vulnerabilities.

Another significant driver for the market is the evolving regulatory landscape. Regulatory bodies such as the Basel Committee on Banking Supervision (BCBS) have set rigorous standards for operational risk capital calculation, necessitating robust operational loss data management solutions. Financial institutions are mandated to maintain detailed records of loss events, near misses, and risk exposures, which has led to a surge in demand for sophisticated data management platforms. Additionally, the growing emphasis on transparency and accountability in risk reporting has prompted organizations to invest in solutions that ensure data integrity, auditability, and traceability. This regulatory push is not limited to banking but extends to insurance, asset management, and even non-financial sectors, broadening the addressable market for operational loss data management providers.

The increasing complexity of business operations and the interconnectedness of global supply chains have further underscored the need for robust operational loss data management. As organizations expand their footprints across geographies, they encounter diverse risk scenarios that require comprehensive data capture and analysis. The ability to aggregate and analyze loss data from multiple sources and jurisdictions is becoming a critical differentiator for organizations aiming to maintain resilience and continuity. Furthermore, the rise of remote work and digital transformation initiatives has introduced new operational risks, such as data breaches and system outages, necessitating agile and scalable data management solutions. As a result, vendors are focusing on offering cloud-based and modular platforms that cater to the evolving needs of modern enterprises.

Regionally, North America continues to dominate the Operational Loss Data Management market owing to its advanced financial ecosystem and stringent regulatory environment. However, Asia Pacific is emerging as the fastest-growing region, fueled by rapid digitalization, increasing regulatory adoption, and the expansion of the BFSI sector. Europe also holds a significant market share, driven by the implementation of GDPR and other risk management directives. The Middle East & Africa and Latin America are witnessing steady growth, supported by the modernization of financial infrastructure and the rising awareness of operational risk management. Overall, the global market is poised for sustained expansion, with technological advancements and regulatory developments serving as key catalysts.

Component Analysis

The Component segment of the Operational Loss Data Management market is bifurcated into software and services. Software solutions form the backbone of operational risk management frameworks, offering comprehensive platforms for data capture, aggregation, analytics, and reporting. These platforms are designed to seamlessly integrate with existing IT infrastructure, enabling organizations to automate the collection of loss

Elevated Risk Event Prediction Market Outlook

According to our latest research, the global Elevated Risk Event Prediction market size reached USD 5.63 billion in 2024, and is expected to grow at a robust CAGR of 19.7% during the forecast period, reaching USD 26.99 billion by 2033. This strong growth trajectory is primarily driven by the increasing adoption of advanced analytics and artificial intelligence (AI) solutions for proactive risk management across industries, as organizations seek to mitigate operational, financial, and reputational risks in an increasingly volatile global environment.

One of the most significant growth factors for the Elevated Risk Event Prediction market is the rising complexity and frequency of risk events affecting businesses worldwide. The proliferation of digital technologies, interconnected supply chains, and globalized operations has exposed organizations to a broader spectrum of risks, including cyber threats, financial fraud, regulatory compliance issues, and operational disruptions. As a result, enterprises are investing heavily in predictive analytics platforms that can leverage big data, machine learning, and AI algorithms to identify potential risk events before they escalate. These solutions not only enhance situational awareness but also enable businesses to implement preventive measures, thereby minimizing losses and ensuring business continuity. The integration of real-time data sources, such as IoT sensors, social media feeds, and transactional records, further amplifies the predictive accuracy of these platforms, making them indispensable tools for modern risk management strategies.

Another key driver fueling the expansion of the Elevated Risk Event Prediction market is the increasing regulatory pressure across various sectors, particularly in financial services, healthcare, and energy. Regulatory bodies worldwide are mandating stricter compliance and risk monitoring frameworks, compelling organizations to adopt advanced event prediction systems. These technologies facilitate automated risk assessments, real-time alerts, and detailed audit trails, enabling enterprises to demonstrate compliance and avoid hefty fines. Moreover, the growing awareness of the financial and reputational repercussions of risk events has prompted C-suites and boards to prioritize investments in predictive risk management technologies. This trend is further supported by the rapid advancements in cloud computing and data analytics, which have made sophisticated risk prediction solutions more accessible and scalable for organizations of all sizes.

Technological innovation remains a cornerstone of the Elevated Risk Event Prediction market. The ongoing development of AI-driven analytics, natural language processing (NLP), and deep learning models has significantly enhanced the precision and speed of risk event detection. Vendors are increasingly offering modular, API-driven solutions that can be seamlessly integrated with existing enterprise systems, such as ERP, CRM, and cybersecurity platforms. This interoperability not only accelerates deployment times but also maximizes the return on investment for end-users. Additionally, the emergence of edge computing and 5G connectivity is enabling real-time risk monitoring in remote or high-risk environments, such as manufacturing plants, energy grids, and transportation networks. These technological advancements are expected to drive further adoption and innovation in the market over the coming years.

From a regional perspective, North America currently dominates the Elevated Risk Event Prediction market, accounting for the largest market share in 2024, followed by Europe and Asia Pacific. The strong presence of leading technology vendors, early adoption of AI and analytics, and stringent regulatory requirements in the United States and Canada have positioned North America as a frontrunner in this space. However, the Asia Pacific region is witnessing the fastest growth, fueled by rapid digital transformation, increasing investments in risk management infrastructure, and rising awareness among enterprises about the benefits of predictive analytics. Europe continues to be a significant market, driven by robust regulatory frameworks and the presence of large multinational corporations. The Middle East & Africa and Latin America are also emerging as promising markets, supported by growing digitization and the need for enhanced risk mitigation solutions.

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Risk Analytics Market Size 2024-2028

The risk analytics market size is estimated to grow by USD 39.55 billion at a CAGR of 11.73% between 2023 and 2028. Market growth is influenced by various factors, including the rising incidence of data breaches and security lapses among enterprises, the increasing need for stringent compliance with government regulations, and the growing complexities inherent in modern business processes. These challenges drive demand for robust security solutions and compliance frameworks, stimulating market expansion. Additionally, the escalating threat landscape underscores the criticality of adopting comprehensive security measures, further fueling market growth. The need to navigate intricate regulatory environments and protect sensitive data propels organizations to invest in advanced security solutions and compliance strategies, driving market dynamics. It also includes an in-depth analysis of drivers, trends, and challenges. Furthermore, the report includes historic market data from 2018 to 2022.

What will be the Size of the Market During the Forecast Period?

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Market Segmentation

By Component

The market share growth by the solution segments is estimated to witness significant growth during the forecast period. The solution segment is a crucial component in the global risk analytics market. With the increasing demand for solutions that can help mitigate risks and improve overall organizational performance, the solution segment is expected to experience significant growth during the forecast period. This growth is fueled by advancements in technology, increasing data volumes, and the need for effective risk analysis and management.

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The solution segments showed a gradual increase in the market share of USD 19.06 billion in 2018. The ability to assist in the automation of processes that are not too difficult or complicated, and require a significant amount of time is one of the biggest advantages of using risk analytics solutions. Compliance management solutions, for instance, are considered to be a means of ensuring that an organization complies with applicable legislation. This allows the software to simplify compliance procedures, reduce errors and eliminate the need for costly and time-consuming manual processes.

By Deployment

The on-premises segment in the market refers to a solution that is installed and deployed on the organization's own server and managed by its IT department. To ensure that organizational policies and security standards are respected, this arrangement allows any part of a risk management system or data to be controlled by an organization. One of the most significant advantages offered by an on-premises segment is to be able to configure threat management systems in line with company-specific requirements, which allows for a more tailored solution.

By Region

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APAC is estimated to contribute 34% to the growth of the global market during the forecast period. Technavio’s analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period. Yet another significant region is North America, which dominated the market in terms of revenue share. This is due to the presence of a large number of organizations belonging to the BFSI, IT, healthcare, and other industries. Organizations belonging to these end-user industries are the major buyers of these analytics solutions in this region, as it enables cash management, payments, financial instruments, accounting, banking, risk management, and hedge accounting.

Market Dynamics and Customer Landscape

The Risk Analytics Market is witnessing significant growth due to the increasing adoption of technology in risk management. Machine Learning (ML), Big Data, and Cloud computing are key technologies driving the market. ML algorithms enable real-time analysis of vast amounts of data, helping enterprises identify and mitigate risks more effectively. Cloud deployment of risk analytics software is gaining popularity due to its flexibility and cost-effectiveness. However, on-premise solutions continue to be preferred by some enterprises due to data security concerns. Risk analytics software is used to manage both physical and operational risks. Physical risks include climate change and natural disasters, while operational risks include internal factors such as human errors, systems failures, and fraud cybercrime. Cybersecurity is a major concern for enterprises, and risk analytics software plays a crucial role in mitigating cyber risks. Security data sources, such as firewalls, intrusion detection systems, and security information and event management (SIEM) systems, provide valuable data for risk analysi

The Risk Identification Database (RIDB) is an organized collection of data, which includes the identification of threats, risk sources, risk factors, causal relations and the description of risk events. The objective of the RIDB therefore is to identify risk events, related to physical and cyber threats, which can occur in water distribution systems utilities, their locations, and causes.

Event Risk Assessment Platforms Market Outlook

According to our latest research, the global Event Risk Assessment Platforms market size reached USD 1.42 billion in 2024 and is expected to grow at a robust CAGR of 13.2% through the forecast period, reaching approximately USD 4.17 billion by 2033. This surge is primarily driven by the increasing frequency and scale of corporate, public, and government events worldwide, coupled with the rising need for comprehensive risk management solutions to ensure event safety and regulatory compliance. Enhanced digitalization and the adoption of advanced analytics are also fueling the expansion of this market as organizations seek to mitigate a dynamic spectrum of risks in real-time.

A significant growth factor for the Event Risk Assessment Platforms market is the rising complexity of event management in the post-pandemic era. Organizations are now more focused than ever on ensuring the safety of attendees, staff, and assets, especially as events become larger and more interconnected. The proliferation of hybrid and virtual events, alongside traditional in-person gatherings, has introduced new layers of risk, including cybersecurity threats and logistical challenges. As a result, demand for integrated risk assessment tools that can provide end-to-end visibility and actionable intelligence has soared. These platforms leverage artificial intelligence, machine learning, and real-time data analytics to identify, assess, and mitigate risks proactively, ensuring a seamless event experience.

Another key driver is the tightening of regulatory requirements and industry standards related to event safety and security. Governments and regulatory bodies across regions are mandating stricter compliance measures, particularly for large-scale public gatherings and high-profile corporate events. This has compelled event organizers and stakeholders to invest in sophisticated risk assessment technologies that can generate detailed compliance reports, automate risk evaluation processes, and facilitate rapid response in the event of an incident. The integration of risk assessment platforms with existing event management systems further enhances their utility, allowing organizations to streamline workflows and reduce operational overhead.

The rapid advancement of technology, particularly in cloud computing and data analytics, is also accelerating market growth. Cloud-based event risk assessment platforms offer superior scalability, flexibility, and cost-effectiveness, making them an attractive choice for organizations of all sizes. These solutions enable real-time collaboration among stakeholders, centralized data management, and seamless integration with third-party systems such as emergency response and communication tools. As the event ecosystem becomes increasingly digital, the ability to harness big data for predictive risk modeling and scenario planning is emerging as a critical differentiator for market leaders.

From a regional perspective, North America currently dominates the Event Risk Assessment Platforms market, owing to the high concentration of corporate headquarters, frequent large-scale public events, and stringent regulatory frameworks. However, Asia Pacific is expected to witness the fastest growth over the forecast period, driven by the rapid expansion of the events industry in countries such as China, India, and Japan, coupled with increasing investments in digital infrastructure and event security. Europe remains a significant market, characterized by a strong emphasis on compliance and safety, particularly in the context of sports and government events. Latin America and the Middle East & Africa are also experiencing steady growth, supported by rising urbanization and the proliferation of international events.

Component Analysis

The Event Risk Assessment Platforms market is segmented by component into software and services, each playing a pivotal role in addressing diverse market needs. The software segment, which enc

Elevated Risk Event Prediction Market Outlook

According to our latest research, the global Elevated Risk Event Prediction market size reached USD 7.1 billion in 2024, with a robust year-on-year growth trajectory. The market is projected to expand at a CAGR of 19.7% through the forecast period, reaching an estimated USD 28.1 billion by 2033. This remarkable growth is primarily driven by increasing adoption of advanced analytics and artificial intelligence (AI) technologies across critical sectors, aiming to proactively identify, assess, and mitigate high-impact risks before they materialize.

A primary growth factor for the Elevated Risk Event Prediction market is the mounting complexity of modern business operations and the concurrent rise in sophisticated threats, both physical and digital. Organizations across industries are recognizing the necessity of predictive analytics to safeguard assets, ensure regulatory compliance, and maintain operational continuity. The integration of machine learning and big data analytics is enabling businesses to process vast volumes of structured and unstructured data, thereby identifying patterns and anomalies indicative of emerging risks. This proactive approach is particularly vital in sectors such as financial services, healthcare, and energy, where the cost of unforeseen events can be catastrophic both financially and reputationally.

Another significant driver is the rapid digital transformation accelerated by the global pandemic, which has led to increased connectivity and, consequently, a broader attack surface for cyber and operational risks. The proliferation of IoT devices, cloud computing, and remote work arrangements has compelled organizations to invest in more sophisticated risk prediction solutions. Elevated Risk Event Prediction platforms are increasingly being leveraged for real-time monitoring and scenario analysis, enhancing an organization’s ability to anticipate and respond to incidents such as cyberattacks, supply chain disruptions, and public health emergencies. The growing regulatory scrutiny around data protection and risk management is further fueling demand for these solutions, as compliance becomes a non-negotiable aspect of business operations.

Additionally, the market is benefiting from advancements in AI and deep learning algorithms, which have significantly improved the accuracy and speed of risk predictions. The availability of scalable cloud-based platforms is democratizing access to these technologies, making them viable for small and medium enterprises (SMEs) alongside large corporations. Strategic collaborations between technology providers, industry stakeholders, and government agencies are fostering innovation and driving the adoption of elevated risk event prediction tools. This ecosystem approach is not only accelerating product development cycles but also expanding the range of applications across sectors such as transportation, logistics, and government services.

Regionally, North America continues to dominate the market, accounting for the largest share in 2024, owing to the early adoption of advanced technologies and the presence of major industry players. However, the Asia Pacific region is emerging as the fastest-growing market, driven by rapid industrialization, increasing digitalization, and heightened awareness of risk management best practices. Europe also holds a significant share, bolstered by stringent regulatory frameworks and a strong focus on corporate governance. The Middle East and Africa, though relatively nascent, are witnessing increasing investments in predictive analytics, particularly in sectors like energy and government, signaling promising growth prospects over the forecast period.

Component Analysis

The Component segment of the Elevated Risk Event Prediction market is categorized into software, hardware, and services, each playing a pivotal role in the overall ecosystem. Software solutions form the backbone of risk event prediction, encompassing advanced analytics platforms, AI-driven modeling tools, and real-time data processing applications. These software offerings are continuously evolving, integrating cutting-edge technologies such as natural language processing, deep learning, and graph analytics to enhance predictive accuracy and scalability. The demand for customizable and interoperable software is particularly high among large enterprises, which require seamless integration with existing IT infr

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Event Mesh for Real-Time Risk Signals Market Outlook

According to our latest research, the global Event Mesh for Real-Time Risk Signals market size reached USD 1.84 billion in 2024. The market is exhibiting strong momentum, propelled by increasing demand for instant risk detection and mitigation across industries. The market is anticipated to grow at a CAGR of 17.2% from 2025 to 2033, reaching a forecasted value of USD 8.04 billion by 2033. This remarkable growth is being fueled by the proliferation of real-time data streams, rising regulatory scrutiny, and the critical need for robust risk management frameworks in an increasingly digitalized business environment.

One of the primary growth drivers for the Event Mesh for Real-Time Risk Signals market is the rapid acceleration of digital transformation initiatives across multiple sectors, particularly in the BFSI, healthcare, and retail industries. Organizations are increasingly adopting event-driven architectures to facilitate seamless data flow, real-time analytics, and automated decision-making. The ability of event mesh technologies to integrate disparate data sources, process high-velocity event streams, and generate actionable risk signals in real time is a crucial advantage. As cyber threats, financial fraud, and operational risks continue to escalate, enterprises are prioritizing investments in advanced risk management solutions that can proactively identify and neutralize threats before they escalate. This shift toward real-time risk intelligence is expected to sustain high demand for event mesh platforms throughout the forecast period.

Another significant factor propelling market growth is the evolving regulatory landscape, which is imposing stricter compliance requirements on organizations, especially those operating in highly regulated sectors such as banking, insurance, and healthcare. Regulatory bodies worldwide are mandating real-time monitoring and reporting of risk events, necessitating the adoption of sophisticated event mesh architectures capable of delivering timely and accurate risk signals. The integration of artificial intelligence and machine learning with event mesh platforms further enhances their ability to detect anomalies, predict potential risks, and ensure compliance with complex regulatory frameworks. This convergence of regulatory pressure and technological innovation is expected to drive substantial investments in the Event Mesh for Real-Time Risk Signals market over the coming years.

The increasing complexity of enterprise IT environments, characterized by hybrid and multi-cloud deployments, is also contributing to the expansion of the market. Organizations are seeking scalable and flexible solutions that can support real-time data processing across diverse infrastructures. Event mesh technologies, with their capability to orchestrate events across on-premises and cloud environments, are emerging as the preferred choice for enterprises looking to enhance their risk management capabilities. The rising adoption of microservices, IoT devices, and edge computing is generating massive volumes of event data, which, when harnessed through event mesh platforms, can provide organizations with a comprehensive and real-time view of their risk landscape. This trend is expected to further accelerate market growth, as businesses increasingly recognize the strategic value of real-time risk intelligence.

From a regional perspective, North America currently dominates the Event Mesh for Real-Time Risk Signals market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The strong presence of leading technology vendors, high levels of digital adoption, and stringent regulatory frameworks in North America are key factors driving regional growth. Meanwhile, Asia Pacific is expected to witness the fastest CAGR during the forecast period, fueled by rapid economic development, increasing investments in digital infrastructure, and a growing focus on risk management among enterprises. Europe remains a significant market, supported by robust regulatory requirements and a mature financial sector. Latin America and the Middle East & Africa are also experiencing steady growth, albeit from a smaller base, as organizations in these regions gradually embrace real-time risk management solutions.

Component Analysis

The Component segment of the Event Mesh for Real-Time Risk Signals market is broadly categorized into Softw

About the Dataset This dataset contains financial transaction records and risk management data for accounting systems. It includes a variety of transactional data, such as transaction IDs, amounts, categories, and payment methods, alongside associated risk incidents like fraud, errors, and misstatements. The dataset also captures system metadata, such as user activity, transaction processing time, login frequency, and geographical region of the IP. The data is designed to simulate real-world accounting system operations and risk events, enabling the development and testing of AI-driven risk prediction models. The dataset can be used for research in real-time financial risk management, fraud detection, and improving decision-making processes in accounting systems using artificial intelligence.

The Global Flood Mortality Risks and Distribution is a 2.5 minute grid of global flood mortality risks. Gridded Population of the World, Version 3 (GPWv3) data provided a baseline population per grid cell from which to estimate potential mortality risks due to flood hazard. Mortality loss estimates per flood event are calculated using regional, hazard-specific mortality records of the Emergency Events Database (EM-DAT) that span the 20 years between 1981 and 2000. Data regarding the frequency and distribution of flood hazard are obtained from the Global Flood Hazard Frequency and Distribution data set. In order to more accurately reflect the confidence associated with the data and the procedures, the potential mortality estimate range is classified into deciles, 10 classes of increasing hazard with an approximately equal number of grid cells per class, producing a relative estimate of flood-based mortality risks. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).

The Global Flood Mortality Risks and Distribution is a 2.5 minute grid of global flood mortality risks. Gridded Population of the World, Version 3 (GPWv3) data provided a baseline population per grid cell from which to estimate potential mortality risks due to flood hazard. Mortality loss estimates per flood event are calculated using regional, hazard-specific mortality records of the Emergency Events Database (EM-DAT) that span the 20 years between 1981 and 2000. Data regarding the frequency and distribution of flood hazard are obtained from the Global Flood Hazard Frequency and Distribution data set. In order to more accurately reflect the confidence associated with the data and the procedures, the potential mortality estimate range is classified into deciles, 10 classes of increasing hazard with an approximately equal number of grid cells per class, producing a relative estimate of flood-based mortality risks. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).

The Insider Risk Management (IRM) market is experiencing robust growth, driven by the increasing frequency and severity of data breaches stemming from insider threats. The market, estimated at $2 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This significant expansion is fueled by several factors: the rising adoption of cloud-based applications and remote work models, which expand the attack surface; evolving regulatory landscapes mandating stronger data protection; and the sophisticated nature of modern insider threats, requiring advanced detection and response capabilities. The market is witnessing a shift towards AI-powered solutions that offer advanced analytics and automation to identify and mitigate risks proactively. Furthermore, the integration of IRM with other security solutions like Security Information and Event Management (SIEM) and User and Entity Behavior Analytics (UEBA) is gaining traction, enhancing overall security posture. Leading vendors like Darktrace, Microsoft, Splunk, and others are actively investing in R&D to enhance their IRM offerings. The market is segmented by deployment type (cloud, on-premises), organization size (SMEs, large enterprises), and industry vertical (financial services, healthcare, government). While the high cost of implementation and a shortage of skilled professionals pose challenges, the escalating financial and reputational risks associated with insider threats are compelling organizations to prioritize IRM investments. The market's growth trajectory reflects a broader recognition of the critical need for proactive, intelligent solutions to manage the multifaceted threat landscape posed by insiders. This market is expected to reach approximately $6 billion by 2033, showcasing its consistent and substantial growth.

Data prepared for the implementation of the Directive 2007/60/EC of the European Parliament and of the Council of 23 October 2007 on the assessment and management of flood risks. Data has been updated 2020-07-20.

This dataset is used for paper "Impact of the political risk on food reserve ratio: evidence across countries". We explore how the political risk impacts on food reserve ratio using an unbalanced panel data covering 75 countries from1991 to 2019. This dataset includes International Country Risk Guide ratings (ICRG) database, FAOSTAT database, Production, Supply, and Distribution (PSD) online database, Emergency Events Database (EM-DAT), and World Bank Open (WBO) database.

Data Contracts for Banking Events Market Outlook

According to our latest research, the global data contracts for banking events market size reached USD 1.47 billion in 2024, driven by the increasing need for reliable data sharing and regulatory compliance across the financial sector. The market is expected to grow at a CAGR of 17.9% during the forecast period, with the market size projected to reach USD 6.13 billion by 2033. This robust expansion is underpinned by the proliferation of digital banking, the escalating frequency of banking transactions, and the growing emphasis on data governance and security. As per our latest research, evolving customer expectations and regulatory mandates are accelerating the adoption of data contracts solutions within the global banking ecosystem.

One of the primary growth drivers for the data contracts for banking events market is the increasing complexity of banking operations in the digital era. With the surge in real-time banking transactions, banks are under pressure to maintain data integrity, consistency, and traceability across multiple channels. Data contracts facilitate structured agreements on data exchange, ensuring that all parties involved—be it internal departments or external partners—adhere to predefined data standards and protocols. This not only enhances operational efficiency but also mitigates the risk of data discrepancies and errors, which are critical in high-stakes financial environments. Furthermore, as banks expand their digital services, the demand for robust data contracts to manage event-driven architectures and APIs is intensifying, positioning this market for sustained growth.

Regulatory compliance is another significant catalyst fueling the growth of the data contracts for banking events market. Financial institutions worldwide are grappling with stringent regulations such as GDPR in Europe, CCPA in the United States, and various anti-money laundering (AML) and know-your-customer (KYC) directives. Data contracts play a pivotal role in enabling transparent, auditable, and compliant data flows, providing a clear framework for data ownership, consent, and accountability. As regulatory scrutiny intensifies, banks are increasingly investing in data contract solutions to avoid hefty penalties and reputational damage. The integration of advanced analytics and AI-driven monitoring further amplifies the value proposition of data contracts in ensuring ongoing compliance and proactive risk management.

The rapid adoption of cloud technologies and open banking initiatives is also reshaping the landscape of data contracts for banking events. Cloud-based deployments offer scalability, agility, and cost-effectiveness, making them particularly attractive for both large enterprises and small and medium-sized banks. Open banking, which mandates secure data sharing between banks and third-party providers, necessitates robust data contracts to govern these interactions. As a result, financial institutions are increasingly leveraging data contracts to facilitate seamless interoperability, foster innovation, and enhance customer experiences. This trend is particularly pronounced in regions with progressive regulatory frameworks and vibrant fintech ecosystems, further propelling market growth.

From a regional perspective, North America currently dominates the data contracts for banking events market, accounting for the largest share in 2024. This leadership is attributed to the region's advanced banking infrastructure, early adoption of digital technologies, and a strong regulatory environment. Europe follows closely, driven by stringent data protection laws and the widespread implementation of open banking. Meanwhile, the Asia Pacific region is witnessing the fastest growth, fueled by rapid digitalization, a burgeoning fintech sector, and increasing investments in banking modernization. Latin America and the Middle East & Africa are also emerging as promising markets, albeit from a smaller base, as local banks accelerate their digital transformation journeys.

IntroductionEculizumab is a C5 complement inhibitor approved by the FDA for the targeted treatment of four rare diseases, paroxysmal nocturnal hemoglobinuria (PNH), atypical hemolytic uremic syndrome (aHUS), generalized myasthenia gravis (gMG), and aquaporin-4 immunoglobulin G-positive optic neuromyelitis optica spectrum disorders (AQP4-IgG+NMOSD). The current study was conducted to assess real-world adverse events (AEs) associated with eculizumab through data mining of the FDA Adverse Event Reporting System (FAERS).MethodsDisproportionality analyses, including Reporting Ratio Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-Item Gamma Poisson Shrinker (MGPS) algorithms were used to quantify the signals of eculizumab-associated AEs.ResultsA total of 46,316 eculizumab-related ADEs reports were identified by analyzing 19,418,776 reports in the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) database. A total of 461 PTs were identified as satisfying by all four algorithms. These PTs reported adverse reactions consistent with the specifications, such as fatigue, nasopharyngitis, meningococcal infection, fever, and anemia. Some PTs, such as aplastic anemia, gene mutation, mastication disorder, kidney fibrosis, BK virus infection, abnormal neutrophil count, C3 glomerulopathy, neuroblastoma, and glomerulonephritis membranoproliferative, were also detected outside the instructions. The median time to onset of eculizumab adverse events was 159 days (interquartile range [IQR] 11∼738 days). In addition, at the PT level, 51 PTs were determined to have an imbalance in the occurrence of ADEs between the sexes.ConclusionThese findings provide valuable insights into the occurrence of ADEs following the use of eculizumab and could support clinical monitoring and risk identification efforts.

The evolving regulatory landscape has increasingly recognized the value of real-world data (RWD) in enhancing drug safety surveillance across the clinical development lifecycle. Enabled by frameworks such as the FDA’s Real-World Evidence (RWE) Programs and other international regulatory bodies, sponsors now have expanded opportunities to use RWD to detect, evaluate, and manage safety signals in both pre- and post-market settings. This paper examines how the integration of RWD, particularly through privacy-preserving record linkage (PPRL) methods like tokenization, can improve pharmacovigilance by enabling longitudinal safety monitoring while protecting patient privacy. Traditional safety surveillance methods, such as spontaneous adverse event reporting and aggregate signal detection, are limited by under-reporting and fragmented data sources. In contrast, linked RWD offers more comprehensive, patient-level insights into safety outcomes, including rare events, off-label use, and long-term risks. The paper outlines regulatory considerations for using de-identified, linked RWD in safety reporting, emphasizing the importance of clear protocols, IRB engagement, and legal compliance with HIPAA. It further highlights emerging best practices for integrating RWD into clinical development, such as early regulatory engagement and the incorporation of linked RWD-derived safety signals into risk management plans. Ultimately, we propose that leveraging linked RWD in a privacy-focused manner enables more proactive, scalable, and effective pharmacovigilance. This approach supports earlier detection of safety issues, enhances post-market follow-up, and promotes data continuity between trial and real-world settings, positioning RWD as a cornerstone of modern safety surveillance.

Abstract One of the most characteristic aspects of artisanal mining is the lack of geological exploration. Going into production without previous exploration is here defined as a gambling scenario. The present study intended to quantify the risk associated to such gambling by analyzing the operations of an area of artisanal underground gold mining in central Chile. To quantify the risks and the probable outcomes, a risk analysis technique called Event Tree Analysis has been applied. This technique is based on the analysis of possible multiple outcomes of single events or decisions and the probability of occurrence of each. Results show that chances of negative vs. positive revenue are 83% vs. 17%, with an order of magnitude of difference between worst-case and best-case scenarios.