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The Global Anti-Money Laundering (AML) Software Market has demonstrated notable progress, achieving a market valuation of approximately USD 2.6 Billion in 2023. Driven by increasing regulatory scrutiny and the rising complexity of financial crimes, the market is expected to grow significantly over the next decade.
By 2033, the AML software market is projected to reach approximately USD 10.3 Billion, expanding at a robust Compound Annual Growth Rate (CAGR) of 14.8% between 2024 and 2033. This sustained growth reflects heightened enforcement of anti-financial crime regulations globally, particularly within banking, insurance, fintech, and cryptocurrency sectors.
Key drivers include the growing need for real-time transaction monitoring, Know Your Customer (KYC) compliance, and the integration of AI and machine learning to enhance fraud detection accuracy. Institutions are increasingly investing in AML platforms not only to meet compliance requirements but also to reduce reputational and operational risks.
The Anti-Money Laundering (AML) landscape in 2022 revealed significant operational and regulatory complexities, reflecting both the scale of illicit financial flows and the evolving challenges in detection and enforcement. It is estimated that USD 800 Billion, equivalent to nearly 5% of global GDP, is laundered each year, emphasizing the vastness of the problem. Alarmingly, according to the United Nations, approximately 90% of global money laundering activities remain undetected, underscoring systemic inefficiencies in current global compliance mechanisms.
A key operational challenge identified was the high incidence of false-positive alerts, reported by 41% of financial organizations, based on Deloitte’s analysis. These alerts often strain compliance teams by diverting critical resources toward non-threatening cases, delaying responses to actual financial crime. Compounding this issue, 48% of banks reportedly continue to rely on outdated AML technology, hindering their ability to meet modern compliance expectations effectively.
Despite these challenges, there are some positive outcomes. Data from the UK’s National Crime Agency shows that 31% of illicit financial flows are intercepted annually via Suspicious Activity Reports (SARs). Yet, the impact is considerably muted when contrasted with findings from the University of Melbourne, which show that only 0.1% of laundered funds are eventually recovered post-investigation, highlighting the limited success of asset recovery efforts.
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This 6MB download is a zip file containing 5 pdf documents and 2 xlsx spreadsheets. Presentation on COVID-19 and the potential impacts on employment
May 2020Waka Kotahi wants to better understand the potential implications of the COVID-19 downturn on the land transport system, particularly the potential impacts on regional economies and communities.
To do this, in May 2020 Waka Kotahi commissioned Martin Jenkins and Infometrics to consider the potential impacts of COVID-19 on New Zealand’s economy and demographics, as these are two key drivers of transport demand. In addition to providing a scan of national and international COVID-19 trends, the research involved modelling the economic impacts of three of the Treasury’s COVID-19 scenarios, to a regional scale, to help us understand where the impacts might be greatest.
Waka Kotahi studied this modelling by comparing the percentage difference in employment forecasts from the Treasury’s three COVID-19 scenarios compared to the business as usual scenario.
The source tables from the modelling (Tables 1-40), and the percentage difference in employment forecasts (Tables 41-43), are available as spreadsheets.
Arataki - potential impacts of COVID-19 Final Report
Employment modelling - interactive dashboard
The modelling produced employment forecasts for each region and district over three time periods – 2021, 2025 and 2031. In May 2020, the forecasts for 2021 carried greater certainty as they reflected the impacts of current events, such as border restrictions, reduction in international visitors and students etc. The 2025 and 2031 forecasts were less certain because of the potential for significant shifts in the socio-economic situation over the intervening years. While these later forecasts were useful in helping to understand the relative scale and duration of potential COVID-19 related impacts around the country, they needed to be treated with care recognising the higher levels of uncertainty.
The May 2020 research suggested that the ‘slow recovery scenario’ (Treasury’s scenario 5) was the most likely due to continuing high levels of uncertainty regarding global efforts to manage the pandemic (and the duration and scale of the resulting economic downturn).
The updates to Arataki V2 were framed around the ‘Slower Recovery Scenario’, as that scenario remained the most closely aligned with the unfolding impacts of COVID-19 in New Zealand and globally at that time.
Find out more about Arataki, our 10-year plan for the land transport system
May 2021The May 2021 update to employment modelling used to inform Arataki Version 2 is now available. Employment modelling dashboard - updated 2021Arataki used the May 2020 information to compare how various regions and industries might be impacted by COVID-19. Almost a year later, it is clear that New Zealand fared better than forecast in May 2020.Waka Kotahi therefore commissioned an update to the projections through a high-level review of:the original projections for 2020/21 against performancethe implications of the most recent global (eg International monetary fund world economic Outlook) and national economic forecasts (eg Treasury half year economic and fiscal update)The treasury updated its scenarios in its December half year fiscal and economic update (HYEFU) and these new scenarios have been used for the revised projections.Considerable uncertainty remains about the potential scale and duration of the COVID-19 downturn, for example with regards to the duration of border restrictions, update of immunisation programmes. The updated analysis provides us with additional information regarding which sectors and parts of the country are likely to be most impacted. We continue to monitor the situation and keep up to date with other cross-Government scenario development and COVID-19 related work. The updated modelling has produced employment forecasts for each region and district over three time periods - 2022, 2025, 2031.The 2022 forecasts carry greater certainty as they reflect the impacts of current events. The 2025 and 2031 forecasts are less certain because of the potential for significant shifts over that time.
Data reuse caveats: as per license.
Additionally, please read / use this data in conjunction with the Infometrics and Martin Jenkins reports, to understand the uncertainties and assumptions involved in modelling the potential impacts of COVID-19.
COVID-19’s effect on industry and regional economic outcomes for NZ Transport Agency [PDF 620 KB]
Data quality statement: while the modelling undertaken is high quality, it represents two point-in-time analyses undertaken during a period of considerable uncertainty. This uncertainty comes from several factors relating to the COVID-19 pandemic, including:
a lack of clarity about the size of the global downturn and how quickly the international economy might recover differing views about the ability of the New Zealand economy to bounce back from the significant job losses that are occurring and how much of a structural change in the economy is required the possibility of a further wave of COVID-19 cases within New Zealand that might require a return to Alert Levels 3 or 4.
While high levels of uncertainty remain around the scale of impacts from the pandemic, particularly in coming years, the modelling is useful in indicating the direction of travel and the relative scale of impacts in different parts of the country.
Data quality caveats: as noted above, there is considerable uncertainty about the potential scale and duration of the COVID-19 downturn. Please treat the specific results of the modelling carefully, particularly in the forecasts to later years (2025, 2031), given the potential for significant shifts in New Zealand's socio-economic situation before then.
As such, please use the modelling results as a guide to the potential scale of the impacts of the downturn in different locations, rather than as a precise assessment of impacts over the coming decade.
As of June 9, 2020, the coronavirus outbreak posed a level seven threat to businesses, meaning that severe and widespread economic impacts were likely. The composite index, which has level ten as its highest warning, was raised to level six on March 12 and to level seven on April 13.
Strong plans needed in response to coronavirus Countries are taking small steps on the road to economic recovery by gradually lifting lockdown measures. Manufacturing firms were among the first to return to work, and governments are now permitting shops, bars, and restaurants to reopen. However, there is no guarantee that consumers will return to their normal habits. In order to reduce the risks, businesses are being encouraged to activate contingency plans that include separating all essential operations from non-essential and focusing on high-priority areas and clients.
A focus on the U.S. economy COVID-19 has left the United States facing an economic crisis, and the country’s GDP fell by 4.8 percent in the first quarter of 2020. Record numbers of Americans have lost their jobs during the pandemic, and the unemployment rate jumped to 14.7 percent in April 2020. The Dow Jones, which monitors the stock prices of the 30 largest companies in the United States, has rallied since the U.S. economy restarted but continues to feel the effects of a destructive period that wiped out years of gains in a matter of weeks.
In addition to displaying earthquakes by magnitude, this service also provide earthquake impact details. Impact is measured by population as well as models for economic and fatality loss. For more details, see: PAGER Alerts. Consumption Best Practices:
As a service that is subject to very high usage, ensure peak performance and accessibility of your maps and apps by avoiding the use of non-cache-able relative Date/Time field filters. To accommodate filtering events by Date/Time, we suggest using the included "Age" fields that maintain the number of days or hours since a record was created or last modified, compared to the last service update. These queries fully support the ability to cache a response, allowing common query results to be efficiently provided to users in a high demand service environment.When ingesting this service in your applications, avoid using POST requests whenever possible. These requests can compromise performance and scalability during periods of high usage because they too are not cache-able.Update Frequency: Events are updated as frequently as every 5 minutes and are available up to 30 days with the following exceptions:
Events with a Magnitude LESS than 4.5 are retained for 7 daysEvents with a Significance value, 'sig' field, of 600 or higher are retained for 90 days In addition to event points, ShakeMaps are also provided. These have been dissolved by Shake Intensity to reduce the Layer Complexity.The specific layers provided in this service have been Time Enabled and include: Events by Magnitude: The event’s seismic magnitude value.Contains PAGER Alert Level: USGS PAGER (Prompt Assessment of Global Earthquakes for Response) system provides an automated impact level assignment that estimates fatality and economic loss.Contains Significance Level: An event’s significance is determined by factors like magnitude, max MMI, ‘felt’ reports, and estimated impact.Shake Intensity: The Instrumental Intensity or Modified Mercalli Intensity (MMI) for available events.For field terms and technical details, see: ComCat DocumentationAlternate SymbologiesVisit the Classic USGS Feature Layer item for a Rainbow view of Shakemap features.RevisionsAug 14, 2024: Added a default Minimum scale suppression of 1:6,000,000 on Shake Intensity layer.Jul 11, 2024: Updated event popup, setting 'Tsunami Warning' text to 'Alert Possible' when flag is present. Also included hyperlink to tsunami warning center.Feb 13, 2024: Updated feed logic to remove Superseded eventsThis map is provided for informational purposes and is not monitored 24/7 for accuracy and currency. Always refer to USGS source for official guidance.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!
This live webmap is a subset of Global Recent Earthquakes feature layer.This Web Map displays earthquakes by magnitude, this service also provide earthquake impact details. Impact is measured by population as well as models for economic and fatality loss. For more details, see: PAGER Alerts.Consumption Best Practices:As a service that is subject to very high usage, ensure peak performance and accessibility of your maps and apps by avoiding the use of non-cache-able relative Date/Time field filters. To accommodate filtering events by Date/Time, we suggest using the included "Age" fields that maintain the number of days or hours since a record was created or last modified, compared to the last service update. These queries fully support the ability to cache a response, allowing common query results to be efficiently provided to users in a high demand service environment.When ingesting this service in your applications, avoid using POST requests whenever possible. These requests can compromise performance and scalability during periods of high usage because they too are not cache-able.Update Frequency: Events are updated as frequently as every 5 minutes and are available up to 30 days with the following exceptions:Events with a Magnitude LESS than 4.5 are retained for 7 daysEvents with a Significance value, 'sig' field, of 600 or higher are retained for 90 daysIn addition to event points, ShakeMaps are also provided. These have been dissolved by Shake Intensity to reduce the Layer Complexity.The specific layers provided in this service have been Time Enabled and include:Events by Magnitude: The event’s seismic magnitude value.Contains PAGER Alert Level: USGS PAGER (Prompt Assessment of Global Earthquakes for Response) system provides an automated impact level assignment that estimates fatality and economic loss.Contains Significance Level: An event’s significance is determined by factors like magnitude, max MMI, ‘felt’ reports, and estimated impact.Shake Intensity: The Instrumental Intensity or Modified Mercalli Intensity (MMI) for available events.For field terms and technical details, see: ComCat DocumentationAlternate SymbologiesVisit the Classic USGS Feature Layer item for a Rainbow view of Shakemap features.RevisionsFeb 13, 2024: Updated feed logic to remove Superseded eventsThis map is provided for informational purposes and is not monitored 24/7 for accuracy and currency. Always refer to USGS source for official guidance.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!
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The Gross Domestic Product per capita (gross domestic product divided by mid-year population converted to international dollars, using purchasing power parity rates) has been identified as an important determinant of susceptibility and vulnerability by different authors and used in the Disaster Risk Index 2004 (Peduzzi et al. 2009, Schneiderbauer 2007, UNDP 2004) and is commonly used as an indicator for a country's economic development (e.g. Human Development Index). Despite some criticisms (Brooks et al. 2005) it is still considered useful to estimate a population's susceptibility to harm, as limited monetary resources are seen as an important factor of vulnerability. However, collection of data on economic variables, especially sub-national income levels, is problematic, due to various shortcomings in the data collection process. Additionally, the informal economy is often excluded from official statistics. Night time lights satellite imagery of NOAA grid provides an alternative means for measuring economic activity. NOAA scientists developed a model for creating a world map of estimated total (formal plus informal) economic activity. Regression models were developed to calibrate the sum of lights to official measures of economic activity at the sub-national level for some target Country and at the national level for other countries of the world, and subsequently regression coefficients were derived. Multiplying the regression coefficients with the sum of lights provided estimates of total economic activity, which were spatially distributed to generate a 30 arc-second map of total economic activity (see Ghosh, T., Powell, R., Elvidge, C. D., Baugh, K. E., Sutton, P. C., & Anderson, S. (2010).Shedding light on the global distribution of economic activity. The Open Geography Journal (3), 148-161). We adjusted the GDP to the total national GDPppp amount as recorded by IMF (International Monetary Fund) for 2010 and we divided it by the population layer from Worldpop Project. Further, we ran a focal statistics analysis to determine mean values within 10 cell (5 arc-minute, about 10 Km) of each grid cell. This had a smoothing effect and represents some of the extended influence of intense economic activity for local people. Finally we apply a mask to remove the area with population below 1 people per square Km.
This dataset has been produced in the framework of the "Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)" project, Work Package 4 (WP4). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.
Data publication: 2014-06-01
Supplemental Information:
ClimAfrica was an international project funded by European Commission under the 7th Framework Programme (FP7) for the period 2010-2014. The ClimAfrica consortium was formed by 18 institutions, 9 from Europe, 8 from Africa, and the Food and Agriculture Organization of United Nations (FAO).
ClimAfrica was conceived to respond to the urgent international need for the most appropriate and up-to-date tools and methodologies to better understand and predict climate change, assess its impact on African ecosystems and population, and develop the correct adaptation strategies. Africa is probably the most vulnerable continent to climate change and climate variability and shows diverse range of agro-ecological and geographical features. Thus the impacts of climate change can be very high and can greatly differ across the continent, and even within countries.
The project focused on the following specific objectives:
Develop improved climate predictions on seasonal to decadal climatic scales, especially relevant to SSA;
Assess climate impacts in key sectors of SSA livelihood and economy, especially water resources and agriculture;
Evaluate the vulnerability of ecosystems and civil population to inter-annual variations and longer trends (10 years) in climate;
Suggest and analyse new suited adaptation strategies, focused on local needs;
Develop a new concept of 10 years monitoring and forecasting warning system, useful for food security, risk management and civil protection in SSA;
Analyse the economic impacts of climate change on agriculture and water resources in SSA and the cost-effectiveness of potential adaptation measures.
The work of ClimAfrica project was broken down into the following work packages (WPs) closely connected. All the activities described in WP1, WP2, WP3, WP4, WP5 consider the domain of the entire South Sahara Africa region. Only WP6 has a country specific (watershed) spatial scale where models validation and detailed processes analysis are carried out.
Contact points:
Metadata Contact: FAO-Data
Resource Contact: Selvaraju Ramasamy
Resource constraints:
copyright
Online resources:
Project deliverable D4.1 - Scenarios of major production systems in Africa
Climafrica Website - Climate Change Predictions In Sub-Saharan Africa: Impacts And Adaptations
Burglar Alarm Systems Market Size 2024-2028
The burglar alarm systems market size is forecast to increase by USD 1.72 billion at a CAGR of 6.78% between 2023 and 2028. In the market, the growing adoption of smart homes and increasing advances in sensor technologies are key growth factors. The integration of alarm sensors into home automation systems allows for remote monitoring and control, enhancing security and convenience. Sensor technology innovations, such as pet immune sensors and glass break detectors, reduce false alarms, ensuring accurate system activation. However, challenges persist, including the increasing number of false alarms and the impact of plant closures and economic downturns, such as those caused by the novel coronavirus, on the automotive industry. This sector's technology companies must continue to innovate to address these challenges and meet evolving consumer demands.
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The market is witnessing significant shifts as a result of the novel coronavirus pandemic and societal changes. This transformation is driven by various factors, including the need for advanced security solutions in both residential and commercial sectors. The demand for sound detection technologies and microphones in burglar alarm systems is increasing due to their ability to detect intrusions silently. These systems can identify unusual sounds, such as breaking glass or footsteps, and alert security personnel or law enforcement agencies. Contactless Biometric Systems: With the pandemic leading to increased concerns around social interaction and touchpoints, contactless biometric systems are gaining popularity in the market.
In addition, these systems use technologies like facial recognition and fingerprint scanning to grant access, ensuring security while maintaining social distancing. The pandemic has forced many businesses to close temporarily or shift to remote work, leading to a decrease in manufacturing output and workplace attendance. This, in turn, has affected the production of surveillance systems in regions like Hubei province. However, the need for advanced security solutions remains high, especially in sectors like banking and healthcare, which are experiencing increased demand due to the pandemic. Impact on Residential Segment: The residential segment of the market is also witnessing changes.
Moreover, with the rise in remote work and travel restrictions, there is an increased focus on home security. Radar-based systems are gaining popularity due to their ability to detect intrusions from a distance, providing an added layer of security. The pandemic has led to significant changes in society and the economy, with many people working from home and businesses adapting to new norms. The market is responding to these changes by offering solutions that cater to the new reality. For instance, video surveillance solutions are being integrated with AI and machine learning to provide real-time alerts and insights.
Furthermore, the automotive industry is also being affected by the pandemic, with plant closures leading to a decrease in production. However, the demand for advanced security features in vehicles remains high. Burglar alarm systems with microphones and sound detection technologies are being integrated into cars to provide an added layer of security. In conclusion, the market is undergoing significant changes due to the pandemic and societal shifts. The need for advanced security solutions, contactless biometric systems, and radar-based technologies are some of the key trends driving the market. The market is expected to continue evolving as businesses and individuals adapt to the new normal.
Market Segmentation
The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.
Type
Wireless alarm system
Wired alarm system
End-user
Residential
Commercial and industrial
Geography
North America
Canada
US
Europe
UK
APAC
China
Japan
South America
Middle East and Africa
By Type Insights
The wireless alarm system segment is estimated to witness significant growth during the forecast period. The wireless segment in the market is a significant and expanding sector, fueled by the rising demand for intelligent home security solutions and the need for more efficient and cost-effective security systems. Wireless alarm systems have garnered popularity due to their ease of installation, adaptability, and cost-effectiveness in contrast to wired systems. These systems employ radio frequency (RF) or cellular technology to transmit signals to the control panel, eradicating the requirement for physical wiring. This adaptability enables more creative system designs and placement, ma
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According to our latest research, the global Drone-Assisted Crop Frost Alert market size was valued at USD 432.7 million in 2024 and is expected to reach USD 1.21 billion by 2033, growing at a robust CAGR of 11.8% during the forecast period. The primary growth factor for this market is the increasing adoption of precision agriculture technologies to mitigate crop losses due to frost, which is a persistent threat to high-value crops worldwide.
One of the most significant growth drivers for the Drone-Assisted Crop Frost Alert market is the escalating need for real-time, actionable data in agriculture. Climate variability and unseasonal frost events have become more frequent, causing substantial losses in crops such as fruits, vegetables, and vineyards. Drones equipped with advanced sensors and thermal cameras can quickly scan large agricultural fields, detect temperature anomalies, and provide early warnings to farmers. This technological edge allows for timely interventions, such as deploying frost mitigation measures, thus reducing crop damage and improving yields. The integration of artificial intelligence and machine learning algorithms further enhances the accuracy of frost prediction, making these systems indispensable tools for modern farmers.
Another critical factor fueling market expansion is the growing awareness among farmers and agricultural enterprises regarding the economic impact of frost damage. Government agencies, agricultural cooperatives, and insurance companies are increasingly supporting drone-based frost alert systems through subsidies, pilot projects, and awareness campaigns. The return on investment for drone-assisted solutions is becoming more evident as they not only prevent losses but also optimize resource utilization, such as targeted irrigation or the use of anti-frost fans. Additionally, the declining costs of drone hardware and the proliferation of user-friendly software platforms are making these technologies accessible to a broader spectrum of end-users, from smallholder farmers to large agribusinesses.
The evolution of regulatory frameworks is also contributing to the market’s positive trajectory. Many countries are updating their aviation and agriculture laws to accommodate the use of drones for commercial applications, including crop monitoring and frost alerting. These regulatory advancements are encouraging both established agricultural enterprises and startups to invest in drone technology. Furthermore, collaborations between research institutes and technology providers are accelerating innovation in sensor miniaturization, battery life, and data analytics, further strengthening the market’s foundation for sustained growth.
Regionally, North America and Europe are leading the adoption of Drone-Assisted Crop Frost Alert systems, owing to their advanced agricultural infrastructure and high-value crops susceptible to frost. The Asia Pacific region is emerging as a high-growth market, driven by large-scale investments in agri-tech and government initiatives to modernize farming practices. Latin America and the Middle East & Africa are also showing increasing interest, particularly in export-oriented crops and regions prone to sudden temperature drops. The interplay of technological innovation, regulatory support, and market demand is shaping a dynamic, globally integrated landscape for drone-assisted frost management.
The Component segment of the Drone-Assisted Crop Frost Alert market is categorized into hardware, software, and services, each playing a pivotal role in the overall ecosystem. Hardware includes the physical drones, thermal and multispectral sensors, GPS modules, and onboard computing units. The rapid advancements in sensor accuracy and drone endurance have significantly enhanced the effectiveness of frost alert systems. Hardware costs are gradually decreasing due to mass production and technological innovations, making drone solutions more affordable for a wider range of farmers and agricultural enterprises. The integration of lightweight, high-resolution thermal cameras has improved the detection of microclimatic changes, enabling more precise frost alerts.
Software is another critical component, encompassing data analytics platforms, AI-driven prediction algorithms, and user interfaces for real-time monitoring. The software segment has witn
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In recent years, with the continuous improvement of the financial system and the rapid development of the banking industry, the competition of the banking industry itself has intensified. At the same time, with the rapid development of information technology and Internet technology, customers’ choice of financial products is becoming more and more diversified, and customers’ dependence and loyalty to banking institutions is becoming less and less, and the problem of customer churn in commercial banks is becoming more and more prominent. How to predict customer behavior and retain existing customers has become a major challenge for banks to solve. Therefore, this study takes a bank’s business data on Kaggle platform as the research object, uses multiple sampling methods to compare the data for balancing, constructs a bank customer churn prediction model for churn identification by GA-XGBoost, and conducts interpretability analysis on the GA-XGBoost model to provide decision support and suggestions for the banking industry to prevent customer churn. The results show that: (1) The applied SMOTEENN is more effective than SMOTE and ADASYN in dealing with the imbalance of banking data. (2) The F1 and AUC values of the model improved and optimized by XGBoost using genetic algorithm can reach 90% and 99%, respectively, which are optimal compared to other six machine learning models. The GA-XGBoost classifier was identified as the best solution for the customer churn problem. (3) Using Shapley values, we explain how each feature affects the model results, and analyze the features that have a high impact on the model prediction, such as the total number of transactions in the past year, the amount of transactions in the past year, the number of products owned by customers, and the total sales balance. The contribution of this paper is mainly in two aspects: (1) this study can provide useful information from the black box model based on the accurate identification of churned customers, which can provide reference for commercial banks to improve their service quality and retain customers; (2) it can provide reference for customer churn early warning models of other related industries, which can help the banking industry to maintain customer stability, maintain market position and reduce corporate losses.
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In recent years, with the continuous improvement of the financial system and the rapid development of the banking industry, the competition of the banking industry itself has intensified. At the same time, with the rapid development of information technology and Internet technology, customers’ choice of financial products is becoming more and more diversified, and customers’ dependence and loyalty to banking institutions is becoming less and less, and the problem of customer churn in commercial banks is becoming more and more prominent. How to predict customer behavior and retain existing customers has become a major challenge for banks to solve. Therefore, this study takes a bank’s business data on Kaggle platform as the research object, uses multiple sampling methods to compare the data for balancing, constructs a bank customer churn prediction model for churn identification by GA-XGBoost, and conducts interpretability analysis on the GA-XGBoost model to provide decision support and suggestions for the banking industry to prevent customer churn. The results show that: (1) The applied SMOTEENN is more effective than SMOTE and ADASYN in dealing with the imbalance of banking data. (2) The F1 and AUC values of the model improved and optimized by XGBoost using genetic algorithm can reach 90% and 99%, respectively, which are optimal compared to other six machine learning models. The GA-XGBoost classifier was identified as the best solution for the customer churn problem. (3) Using Shapley values, we explain how each feature affects the model results, and analyze the features that have a high impact on the model prediction, such as the total number of transactions in the past year, the amount of transactions in the past year, the number of products owned by customers, and the total sales balance. The contribution of this paper is mainly in two aspects: (1) this study can provide useful information from the black box model based on the accurate identification of churned customers, which can provide reference for commercial banks to improve their service quality and retain customers; (2) it can provide reference for customer churn early warning models of other related industries, which can help the banking industry to maintain customer stability, maintain market position and reduce corporate losses.
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The global market size for Automatic Weather Stations (AWS) was valued at USD 750 million in 2023 and is projected to grow to USD 1.52 billion by 2032, driven by a compound annual growth rate (CAGR) of 8.2%. This robust growth is fueled by a combination of technological advancements and increasing demand for accurate weather forecasting across various sectors such as agriculture, aviation, and meteorology.
One of the primary growth drivers for the AWS market is the increasing need for precise and real-time weather data. This demand is particularly high in the agriculture sector, where weather conditions can significantly impact crop yield and quality. Farmers and agribusinesses are increasingly investing in AWS to optimize irrigation, maximize yield, and reduce the risk of crop damage due to unexpected weather changes. Furthermore, the integration of big data analytics and Internet of Things (IoT) technologies with AWS has enhanced the accuracy and reliability of weather data, contributing to market growth.
Another critical growth factor is the rising awareness and implementation of climate change adaptation strategies. Governments, research institutions, and international bodies are investing heavily in AWS to monitor and predict weather patterns. This investment is crucial for disaster management and mitigation strategies, especially in regions prone to natural calamities such as hurricanes, floods, and droughts. The data collected from AWS is invaluable for creating early warning systems, thereby saving lives and reducing economic losses.
Technological advancements have also played a significant role in the expansion of the AWS market. Innovations such as wireless communication, satellite data integration, and solar-powered stations have made AWS more efficient and accessible. These advancements have reduced operational costs and improved the accuracy of weather data, making AWS a valuable tool for various applications, including aviation, marine, and environmental monitoring. Additionally, the development of compact and portable AWS units has opened new opportunities for deployment in remote and hard-to-reach areas.
From a regional perspective, North America holds the largest market share in the AWS market, driven by substantial investments in weather monitoring infrastructure and technological advancements. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, owing to the increasing adoption of AWS in agriculture and rising government initiatives for disaster management. Europe also presents significant growth opportunities, particularly in the field of environmental research and renewable energy applications. Latin America and the Middle East & Africa are gradually embracing AWS technology, with a focus on improving agricultural productivity and managing water resources.
The development of Portable Small Automatic Weather Station units is a significant advancement in the AWS market. These compact and mobile stations offer the flexibility to be deployed in various settings, including remote and hard-to-reach areas. Their portability ensures that accurate weather data can be collected in regions where traditional weather stations are not feasible. This innovation is particularly beneficial for field researchers and environmental scientists who require real-time data for their studies. The ability to easily transport and set up these stations makes them ideal for temporary installations, such as during field campaigns or in response to natural disasters. The growing demand for portable AWS solutions is driving further innovation in this segment, with manufacturers focusing on enhancing their durability and functionality.
The sensors segment is a critical component of Automatic Weather Stations, responsible for measuring various atmospheric parameters such as temperature, humidity, wind speed, and precipitation. Over the years, advancements in sensor technology have significantly improved the accuracy and reliability of weather data. High-precision sensors are now capable of providing real-time data with minimal margin of error, which is crucial for applications such as aviation and meteorology. The integration of IoT technology with sensors has further enhanced their functionality, allowing for remote monitoring and data collection.
The demand for specialized sensors,
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The Global Anti-Money Laundering (AML) Software Market has demonstrated notable progress, achieving a market valuation of approximately USD 2.6 Billion in 2023. Driven by increasing regulatory scrutiny and the rising complexity of financial crimes, the market is expected to grow significantly over the next decade.
By 2033, the AML software market is projected to reach approximately USD 10.3 Billion, expanding at a robust Compound Annual Growth Rate (CAGR) of 14.8% between 2024 and 2033. This sustained growth reflects heightened enforcement of anti-financial crime regulations globally, particularly within banking, insurance, fintech, and cryptocurrency sectors.
Key drivers include the growing need for real-time transaction monitoring, Know Your Customer (KYC) compliance, and the integration of AI and machine learning to enhance fraud detection accuracy. Institutions are increasingly investing in AML platforms not only to meet compliance requirements but also to reduce reputational and operational risks.
The Anti-Money Laundering (AML) landscape in 2022 revealed significant operational and regulatory complexities, reflecting both the scale of illicit financial flows and the evolving challenges in detection and enforcement. It is estimated that USD 800 Billion, equivalent to nearly 5% of global GDP, is laundered each year, emphasizing the vastness of the problem. Alarmingly, according to the United Nations, approximately 90% of global money laundering activities remain undetected, underscoring systemic inefficiencies in current global compliance mechanisms.
A key operational challenge identified was the high incidence of false-positive alerts, reported by 41% of financial organizations, based on Deloitte’s analysis. These alerts often strain compliance teams by diverting critical resources toward non-threatening cases, delaying responses to actual financial crime. Compounding this issue, 48% of banks reportedly continue to rely on outdated AML technology, hindering their ability to meet modern compliance expectations effectively.
Despite these challenges, there are some positive outcomes. Data from the UK’s National Crime Agency shows that 31% of illicit financial flows are intercepted annually via Suspicious Activity Reports (SARs). Yet, the impact is considerably muted when contrasted with findings from the University of Melbourne, which show that only 0.1% of laundered funds are eventually recovered post-investigation, highlighting the limited success of asset recovery efforts.