This dataset contains summary data visualizations and clinical data from a broad sampling of over 200 acute myeloid leukemias from 200 patients. The data was gathered as part of the PanCancer Atlas initiative, which aims to answer big, overarching questions about cancer by examining the full set of tumors characterized in the robust TCGA dataset. The clinical data includes mutation count, information about mutated genes, patient demographics, disease status, tumor typing, and chromosomal gain or loss. The data set also includes copy-number segment data downloadable as .seg files and viewable via the Integrative Genomics Viewer.

Anti-money Laundering Market Outlook

According to our latest research, the global anti-money laundering (AML) market size reached USD 3.6 billion in 2024. The industry is demonstrating robust expansion, propelled by a CAGR of 16.2% during the forecast period. By 2033, the AML market size is projected to attain approximately USD 14.8 billion, reflecting the mounting emphasis on regulatory compliance and the escalating sophistication of financial crimes. This growth is primarily driven by the increasing adoption of advanced analytics, artificial intelligence, and regulatory technologies across various sectors, particularly within banking and financial services.

One of the pivotal growth factors for the anti-money laundering market is the intensifying global regulatory landscape. Governments and regulatory bodies worldwide are imposing stringent AML directives, compelling organizations to invest in robust compliance frameworks. The proliferation of financial crimes, such as money laundering, terrorist financing, and fraud, has underscored the necessity for sophisticated AML solutions. This has led to a surge in demand for both software and services that can effectively detect, monitor, and report suspicious activities. Financial institutions, in particular, face mounting pressure to adhere to evolving regulations, such as the EUÂ’s Sixth Anti-Money Laundering Directive (6AMLD) and the USA PATRIOT Act, thereby fueling the adoption of comprehensive AML platforms.

Technological advancements represent another significant driver in the AML marketÂ’s expansion. The integration of artificial intelligence (AI), machine learning (ML), and big data analytics into AML solutions has revolutionized the detection and prevention of illicit activities. These technologies enable real-time transaction monitoring, enhanced customer due diligence, and advanced risk assessment, thereby increasing the efficacy and efficiency of AML processes. The shift towards cloud-based deployments further facilitates scalability and rapid implementation, making AML solutions more accessible to organizations of all sizes. As cybercriminals employ increasingly sophisticated tactics, the need for adaptive, technology-driven AML systems becomes paramount to stay ahead of emerging threats.

Additionally, the growing adoption of digital banking and payments is amplifying the necessity for robust AML frameworks. As financial services transition to digital platforms, the volume and velocity of transactions have surged, making manual monitoring virtually impossible. This digital transformation, while enhancing customer convenience, also introduces new vulnerabilities that can be exploited by money launderers. Consequently, organizations are investing heavily in automated AML systems capable of handling vast amounts of data and identifying complex patterns indicative of financial crime. The convergence of digital innovation and regulatory compliance is thus a major catalyst for sustained market growth.

Transaction Laundering Detection has emerged as a critical component in the fight against financial crimes, particularly as digital payment platforms continue to proliferate. This process involves identifying and intercepting illicit transactions that are disguised as legitimate business activities. As online commerce expands, so does the complexity of detecting these hidden transactions, which often bypass traditional anti-money laundering measures. Financial institutions are increasingly adopting sophisticated technologies that leverage machine learning and artificial intelligence to enhance their transaction laundering detection capabilities. These technologies enable organizations to identify suspicious patterns and anomalies in real-time, thereby mitigating the risks associated with undetected illicit activities. The integration of transaction laundering detection into existing AML frameworks is essential for maintaining the integrity of financial systems and ensuring compliance with evolving regulatory standards.

From a regional perspective, North America maintains its dominance in the global AML market, attributed to the presence of major financial institutions, advanced technological infrastructure, and stringent regulatory requirements. However, the Asia Pacific region is witnessing the fastest growth, driven by the rapid digitalization of financial services, increasing cross

The Remittance Prices Worldwide data is divided into two groups – Remittance Cost at the Sending Countries and Remittance Cost at the Receiving Countries. For each group there are two dependent variables. FTRI data is available for 147 countries over a 14-year period. Remittance Paid and Received is available for 200 countries spanning 63 years. AML data is available for a minimum of 110 countries in certain years, up to 162 countries, and for a maximum period of 11 years. Names of the countries in different databases or within a database over different years may be captured differently due to geo-political reasons. Clean-up of such names is done to identify the observation uniquely. For example, Republic of Korea is treated as South Korea, Russian Federation or Soviet Union is treated as Russia, Ivory Coast is treated as Côte d'Ivoire, Czcheia is treated as Zchec Republic, Siam is treated as Thailand, the United States of America is treated as the United States and Türkiye is treated as Turkey. Remittance prices data is treated as the base. The data file is split into two datasets by using the ‘Sending Country’ and ‘Receiving Country’ columns along with their respective remittance cost percentage value columns. For each data file, the observations where the “Transparent" value is 'No” are omitted. The data is organized in panel format in ascending years and sorted alphabetically by country as a second-level sorting. The observations are numbered, and a unique key is created by concatenating the year and the serial number. A secondary key is created by concatenating “Year” and “_Country Name”. In the Remittance Paid, Remittance Received, FTRI and AML Index data files, a key same as ‘Secondary key’ is created by concatenating “Year” and “_Country Name”. Using the common key, the data is joined in each ‘Sending Country’ and ‘Receiving Country’ data files. The combined data available for the study is from 2011 to 2023. Missing values are not imputed in the panel data.

Anti-money Laundering Software Market Outlook

The global anti-money laundering (AML) software market size was valued at approximately USD 2.2 billion in 2023 and is projected to reach USD 5.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 11.2% during the forecast period. This robust growth trajectory is driven by increasing regulatory requirements and compliance mandates worldwide, as financial institutions seek to bolster their defenses against money laundering activities. The rise in sophisticated financial crimes and the subsequent need for advanced technological solutions also serve as significant growth catalysts for the AML software market. As the financial ecosystem grows more complex, the demand for effective AML solutions is expected to surge, driving market expansion.

A significant growth factor in the AML software market is the escalating volume of financial transactions taking place globally, partly due to the advent of digital banking, cryptocurrency, and other fintech innovations. With these advancements, the risk of money laundering activities has amplified, prompting regulatory bodies to impose stricter compliance requirements on financial institutions. This has created a pressing need for advanced AML software solutions capable of monitoring and analyzing vast amounts of transaction data in real-time. Institutions are increasingly investing in sophisticated software that incorporates artificial intelligence and machine learning technologies to enhance their ability to detect and prevent suspicious activities more effectively.

Another contributing factor to the market's growth is the globalization of businesses and the interconnectedness of financial markets. As companies expand their operations across borders, they are exposed to a diverse array of regulatory environments and compliance challenges. This expansion necessitates the deployment of comprehensive AML solutions that can adapt to and address the varied regulatory requirements of different jurisdictions. Moreover, governments across the world are intensifying their efforts to combat financial crimes, leading to an increase in fines and penalties for non-compliance, which in turn drives the demand for AML software that can ensure adherence to international standards and regulations.

Technological advancements are also playing a pivotal role in driving the AML software market forward. Innovations such as big data analytics, blockchain technology, and artificial intelligence are being integrated into AML solutions to enhance their effectiveness and efficiency. These technologies enable the development of more robust and accurate systems that can rapidly process and analyze large datasets to identify patterns indicative of money laundering. Additionally, the growing emphasis on automation and real-time monitoring in compliance processes has propelled the adoption of cloud-based AML solutions, which offer scalability, flexibility, and cost-effectiveness, further boosting market growth.

Regionally, North America holds a significant share of the AML software market, driven by the presence of major financial institutions and stringent regulatory frameworks in the United States and Canada. The region's early adoption of advanced technology solutions in the banking and financial services sector has also contributed to its market dominance. Meanwhile, the Asia Pacific region is anticipated to exhibit the highest growth rate over the forecast period, fueled by an expanding banking sector, increasing cross-border trade, and enhanced regulatory measures in emerging economies such as China, India, and Southeast Asian countries. These regions are witnessing rapid digital transformation, which is amplifying the need for effective AML solutions.

Component Analysis

The AML software market is segmented into two primary components: software and services. The software component encompasses solutions designed to detect, prevent, and manage money laundering activities. These software solutions are integral to financial institutions, providing critical functions such as transaction monitoring, customer due diligence, and regulatory reporting. The increasing complexity and volume of financial transactions necessitate advanced software tools capable of real-time analysis and reporting, which drives the growth of this segment. Financial institutions are continuously seeking out innovative software solutions that employ artificial intelligence and machine learning to enhance the accuracy of anomaly detection and streamline compliance processes.

On the other hand, the services component

AML Data Quality Solutions Market Outlook

According to our latest research, the global AML Data Quality Solutions market size in 2024 stands at USD 2.42 billion. The market is experiencing robust expansion, propelled by increasing regulatory demands and the proliferation of sophisticated financial crimes. The Compound Annual Growth Rate (CAGR) for the market is estimated at 16.8% from 2025 to 2033, setting the stage for the market to reach USD 7.23 billion by 2033. This growth is largely driven by heightened awareness of anti-money laundering (AML) compliance, growing digital transactions, and the urgent need for advanced data quality management in financial ecosystems.

A primary growth factor for the AML Data Quality Solutions market is the escalating stringency of regulatory frameworks worldwide. Regulatory bodies such as the Financial Action Task Force (FATF), the European Union’s AML directives, and the U.S. Bank Secrecy Act are continuously updating compliance requirements, compelling organizations, particularly in the BFSI sector, to adopt robust AML data quality solutions. These regulations demand not only accurate and timely reporting but also comprehensive monitoring and management of customer and transactional data. As a result, organizations are investing heavily in advanced AML data quality software and services to ensure compliance, minimize risk, and avoid hefty penalties. The growing complexity of money laundering techniques further underscores the necessity for sophisticated data quality solutions capable of identifying and flagging suspicious activities in real time.

Another significant driver is the exponential growth in digital transactions and the adoption of digital banking services. The proliferation of online and mobile banking, digital wallets, and cross-border transactions has expanded the attack surface for financial crimes. This digital transformation is creating vast volumes of structured and unstructured data, making it challenging for organizations to ensure data accuracy, completeness, and consistency. AML data quality solutions equipped with advanced analytics, artificial intelligence, and machine learning algorithms are becoming indispensable for detecting anomalies, reducing false positives, and streamlining compliance processes. The ability to integrate with existing IT infrastructure and provide real-time data validation is also a key factor accelerating market adoption across various industry verticals.

The market’s growth is further fueled by the rising integration of AML data quality solutions across non-banking sectors such as healthcare, government, and retail. These sectors are increasingly recognizing the importance of robust data quality management to prevent fraud, ensure regulatory compliance, and maintain operational integrity. In healthcare, for instance, the adoption of AML data quality solutions is driven by the need to combat insurance fraud and money laundering through medical billing. In government, these solutions are critical for monitoring public funds and detecting illicit financial flows. The expansion of AML regulations to cover a broader range of industries is expected to sustain high demand for data quality solutions throughout the forecast period.

From a regional perspective, North America currently dominates the AML Data Quality Solutions market, accounting for the largest share in 2024. This leadership is attributed to the presence of major financial institutions, a mature regulatory environment, and early adoption of advanced AML technologies. Europe follows closely, driven by stringent AML directives and the increasing adoption of digital banking. The Asia Pacific region is projected to witness the fastest growth during the forecast period, fueled by rapid digitalization, expanding financial services, and rising regulatory enforcement in countries like China, India, and Singapore. Latin America and the Middle East & Africa are also showing increasing adoption, although market penetration remains comparatively lower due to infrastructural and regulatory challenges.

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Harness AML-focused data to swiftly attain comprehensive insights about your clientele. Move past the tedious manual credit assessments and accelerate your decision-making.

Infocredit Group's API seamlessly combines data from globally recognized sources with insights on regulatory compliance and transactional patterns. Access a vast array of business information with a single action.

Key Benefits: - Enhanced workflow efficiency - Immediate, AML insights - Superior client engagement - Increased adaptability and operational efficiency - Dependable business data accuracy - Diminished exposure to risks - Quick client onboarding

Dive into the AML module to explore a vast database highlighting Sanctions, Enforcements, PEPs, and critical media reports.

AML-centric Details: - Comprehensive global AML risk assessments

The Anti-Money Laundering (AML) solutions market is experiencing robust growth, driven by increasing regulatory scrutiny, escalating cross-border financial transactions, and the rise of sophisticated financial crimes. The market, valued at $2.98 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15.06% from 2025 to 2033. This expansion is fueled by several key factors. Firstly, governments worldwide are strengthening AML regulations in response to the evolving tactics of money launderers, demanding robust and compliant solutions from financial institutions. Secondly, the increasing digitization of financial services and the surge in online transactions create new vulnerabilities and necessitate advanced AML technologies for effective risk mitigation. Finally, the growing adoption of cloud-based solutions offers scalability and cost-effectiveness, further boosting market growth. The market is segmented by solution type (KYC Systems, Compliance Reporting, Transaction Monitoring, Auditing and Reporting, Other Solutions), deployment model (On-cloud, On-premise), and software/services offerings. Key players like NICE Actimize, SAS Institute, and Fiserv are actively shaping the market landscape through innovation and strategic partnerships. The North American region currently holds a significant market share, attributable to stringent regulations and a high concentration of financial institutions. However, growth in other regions like Asia-Pacific is expected to accelerate due to rising financial activity and increased regulatory focus. The competitive landscape is characterized by a mix of established players and emerging technology providers. Established players leverage their extensive experience and strong client networks, while innovative companies focus on providing specialized solutions and disruptive technologies. The market is expected to witness further consolidation through mergers and acquisitions, as companies seek to expand their product portfolios and geographical reach. Future growth will depend on advancements in artificial intelligence (AI), machine learning (ML), and big data analytics, which enhance the accuracy and efficiency of AML detection systems. The increasing adoption of RegTech solutions – regulatory technology that streamlines compliance – will also play a significant role in shaping the future trajectory of the AML solutions market. The market's future hinges on its ability to adapt to the constantly evolving landscape of financial crime, necessitating ongoing investment in R&D and collaboration across industries. This in-depth report provides a comprehensive analysis of the Anti-Money Laundering (AML) solutions market, covering the period 2019-2033. It delves into market size, growth drivers, challenges, key players, and future trends, offering valuable insights for businesses, investors, and regulatory bodies. The report utilizes data from the historical period (2019-2024), with the base year being 2025 and the forecast period extending to 2033. The estimated market size for 2025 will be provided upon completion of the comprehensive analysis. Recent developments include: January 2023 - IMTF, one of the leaders in regulatory technology and process automation for financial institutions, acquired the Siron anti-money laundering and compliance solutions developed by US-based FICO Corporation. With this acquisition, IMTF assumed the global operations of all Siron anti-financial crime solutions., February 2023 - Profile Software, a financial solutions provider, successfully implemented the RiskAvert solution at the Cooperative Bank of Epirus for the effective risk management and full coverage of capital requirements' calculations along with the production of the significant supervisory reports required under the EU Capital Requirements Regulation/Directive (CRR/CRD) framework, also referred as Basel framework.. Key drivers for this market are: Increased Adoption of Digital/Mobile Payment Solutions, Stringent Government Regulations for Compliance Management. Potential restraints include: Lack of Skilled Professionals. Notable trends are: Know Your Customer (KYC) Systems to Witness Major Growth.

The United States Geological Survey (USGS) collected a total of 63 samples of surficial sediment from abandoned mine wastepiles, ephemeral channels, nearby outcrops, and background areas representative of the undisturbed lithology within the Free Coinage and Third Term mining districts in the Stansbury Mountains(Krahulec, 2018). The samples were sieved to obtain the less than 177 micron fraction. Geochemical analyses were completed through a third-party contract by AGAT Laboratories. Samples were analyzed for 49 major, minor, and trace elements using Inductively Coupled Plasma-Optical Emission Spectrometry (ICP-OES) and Inductively Coupled Plasma Mass Spectrometry (ICP-MS) methods (Ag, Al, As, Ba, Be, Bi, Ca, Cd, Ce, Co, Cr, Cs, Cu, Fe, Ga, Hf, In, K, La, Li, Lu, Mg, Mn, Mo, Na, Nb, Ni, P, Pb, Rb, S, Sb, Sc, Se, Sn, Sr, Ta, Tb, Te, Th, Ti, Tl, U, V, W, Y, Yb, Zn, Zr).

During August of 2014, the United States Geological Survey (USGS) collected a total of 187 surficial sediment and bedrock samples from abandoned mine wastepiles, ephemeral channels below wastepiles, nearby outcrops, and background areas representative of the undisturbed lithology within Emery County, UT. The samples were clustered into four different groups: Buckmaster Draw (BM), Dry Mesa (DM), Cow Flats (CF), and Cedar (C). These samples were sieved to obtain a less than 177 micron fraction, which was homogenized and subjected to a four acid digestion prior to analysis by Inductively Coupled Plasma (ICP) methods. (Ag, Al, As, Ba, Be, Bi, Ca, Cd, Ce, Co, Cr, Cs, Cu, Fe, Ga, In, K, La, Li, Mg, Mn, Mo, Na, Nb, Ni, P, Pb, Rb, S, Sb, Sc, Sn, Sr, Te, Th, Ti, Tl, U, V, W, Y, Zn).

The Cryptocurrency Laundering Database lists exchanges and cases tied to money laundering with details on names, countries and AML Network risk ratings.

RNA-seq data was downloaded from the TCGA website (Nov 14, 2013) and includes 128 DESEq normalized samples. The data was derived from primary samples of sorted whole blood obtained from AML patients. 100 bootstraps of ARACNe were run using adaptive partitioning, p-value cut off of 1e-8, and full DPI. The regulator list that was used includes all the genes with GO annotation of transcriptional regulation or DNA binding (Shimoni, Yishai; Alvarez, Mariano (2013): TF list. figshare.http://dx.doi.org/10.6084/m9.figshare.871524)

This dataset contains genome-wide DNA methylation data generated from 142 pediatric acute myeloid leukemia (AML) samples originating from bone marrow or peripheral blood samples taken at AML diagnosis (N=123) or relapse (N=19). Further details regarding the samples are available in Supplementary Table S1 from Krali and Palle et. al., 2021 (https://doi.org/10.3390/genes12060895).Genome-wide DNA methylation was analyzed at the SNP&SEQ Technology Platform, SciLifeLab, National Genomics Infrastructure Uppsala, Sweden. 200ng of bisulfite converted DNA was amplified, fragmented and hybridised to Illumina Infinium Human Methylation450k Beadchip using the standard protocol from Illumina (iScan SQ instrument).This metadata record contains information about the raw idat files generated from the Infinium DNA methylation arrays. The Methylprep Python library was used to generate and normalize the beta-value matrix (https://pypi.org/project/methylprep/1.3.3/).The raw idat files along with a samplesheet, processed beta-value matrix, annotation file for CpG annotation, and signal intensities matrix will be made available upon request. Limited phenotype information is available in the Supplemental Table 1 of the manuscript. All scripts that give a walk-through from data preprocessing from the raw idat files until the modelling process with Machine Learning can be found on the following GitHub repository: https://github.com/Molmed/Krali-Palle_2021.Terms for accessThe DNA methylation dataset is only to be used for research that is seeking to advance the understanding of the influence of epigenetic factors on leukemia etiology and biology.The data should not be used for other purposes, i.e. investigating the epigenetic signatures that may lead to identification of a person.For retrieving the data used for the scope of this publication, please contact datacentre@scilifelab.se.

According to Cognitive Market Research, the global AML market size is USD 2986.3 million in 2024 and will expand at a compound yearly growth rate (CAGR) of 16.20% from 2024 to 2031. Market Dynamics of AML Market

Key Drivers for AML Market

Rising Financial Crime Complexity to Increase the Demand Globally - One key driver in the AML market is the rising financial crime complexity. The increasing sophistication of money laundering and financial crime tactics fuels the demand for advanced AML technologies and services. As criminals employ sophisticated techniques to conceal illicit activities, organizations seek innovative AML solutions capable of detecting and preventing evolving threats in real-time, driving the market for more effective and comprehensive AML solutions. Regulatory Compliance Requirements- Stringent regulations mandating financial institutions to implement effective AML measures drive the market to avoid hefty fines, legal penalties, and reputational damage associated with non-compliance.

Key Restraints for AML Market

Regulatory Complexity- Evolving and intricate AML regulations across jurisdictions pose challenges for businesses to adapt, requiring significant resources for compliance, potentially limiting market expansion. Data Privacy Concerns—Gathering and analyzing large volumes of sensitive financial data for AML purposes must navigate stringent privacy laws, which can hinder effective information sharing and analysis and impact AML's effectiveness. Introduction of the AML Market

The Anti-Money Laundering (AML) market addresses the complex challenge of detecting and preventing illicit financial activities within the global financial system. AML solutions comprise technologies, services, and regulatory frameworks designed to identify and mitigate the risks related with money laundering, terrorist financing, and the other financial crimes. These solutions employ advanced analytics, machine learning algorithms, and data analysis techniques to monitor transactions, identify suspicious patterns, and ensure compliance with regulatory requirements. With the increasing sophistication of financial crimes and stringent regulatory mandates worldwide, the AML market is witnessing significant growth. Financial institutions, banks, insurance companies, and other organizations operating in regulated sectors invest in AML solutions to safeguard their operations, protect their reputation, and comply with regulatory standards.

This dataset comprises four prevalent AML subtypes with defining genetic abnormalities and typical morphological features according to the WHO 2022 classification: (i) APL with PML::RARA fusion, (ii) AML with NPM1 mutation, (iii) AML with CBFB::MYH11 fusion (without NPM1 mutation), and (iv) AML with RUNX1::RUNX1T1 fusion, as well as a control group of healthy stem cell donors.

A total of 189 peripheral blood smears from the Munich Leukemia Laboratory (MLL) database from the years 2009 to 2020 were digitized. First, all blood smears were scanned with 10x magnification and an overview image was created. Using the Metasystems Metafer platform, cell detection was performed automatically using a segmentation threshold and logarithmic color transformation. Further analysis regarding the quality of the region within the blood smear was performed automatically. Per patient, 99-500 white blood cells were then scanned in 40x magnification via oil immersion microscopy in .TIF format, corresponding to 24,9μm x 24,9μm (144x144 pixels). For this, a CMOS Color Camera from MetaSystems with a resolution of 4096x3000px and a pixel size of 3,45μm x 3,45μm was used. Four pixels were binned into one, leading to a size of 6.9μm x 6.9μm, and a resolution of 6.9μm / 40 (1px = 0,1725μm). Additional information about patient age, sex and blood counts are provided in a separate .csv file.

To our knowledge, this dataset covers the morphological complexity of acute myeloid leukemia in peripheral blood smears in unseen quality and quantity.

The United States Geological Survey (USGS) collected a total of 179 samples of surficial sediment from abandoned mine wastepiles, ephemeral channels, nearby outcrops, and background areas representative of the undisturbed lithology within the Silver Island and Crater Island mining districts (Krahulec, 2018).The samples were sieved to obtain the less than 177 micron fraction. Geochemical analyses were completed through a third-party contract by SGS Laboratories. Samples were analyzed for 49 major, minor, and trace elements using Inductively Coupled Plasma-Optical Emission Spectrometry (ICP-OES) and Inductively Coupled Plasma Mass Spectrometry (ICP-MS) methods (Ag, Al, As, Ba, Be, Bi, Ca, Cd, Ce, Co, Cr, Cs, Cu, Fe, Ga, Hf, In, K, La, Li, Lu, Mg, Mn, Mo, Na, Nb, Ni, P, Pb, Rb, S, Sb, Sc, Se, Sn, Sr, Ta, Tb, Te, Th, Ti, Tl, U, V, W, Y, Yb, Zn, Zr).

The dataset contains single-cell DNA seq data from 21 patients with Acute Myeloid Leukemia (AML). Samples were collected from bone marrow or blood. Sequencing was performed in paired-end mode and sequencing data is provided in fastq format.

The Anti-Money Laundering (AML) Software market is experiencing robust growth, projected to reach $1063 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 8.6% from 2025 to 2033. This expansion is driven by several key factors. Increasing regulatory scrutiny globally, particularly in response to sophisticated financial crimes, necessitates robust AML compliance solutions. The rising adoption of digital financial technologies, including mobile payments and cryptocurrencies, presents new avenues for illicit financial activity, further fueling demand for advanced AML software capable of detecting and preventing these threats. Furthermore, the growing awareness among financial institutions of the reputational and financial risks associated with AML violations is driving investment in comprehensive AML solutions. The market is segmented by deployment (cloud-based and on-premise), by functionality (transaction monitoring, customer due diligence, and sanctions screening), and by organization size (small, medium, and large enterprises), each demonstrating unique growth trajectories shaped by specific technological advancements and regulatory mandates. Competitive intensity is high, with established players such as Accuity, ACI Worldwide, and FICO alongside specialized AML firms like AML Partners and NICE Actimize vying for market share. Innovation is a crucial element, with a focus on leveraging AI, machine learning, and big data analytics to enhance the accuracy and efficiency of AML detection. The forecast period (2025-2033) anticipates continued market expansion, fueled by ongoing technological advancements and tightening regulatory landscapes. Emerging markets, with their rapid financial growth and evolving regulatory frameworks, present significant opportunities for AML software providers. However, the market is not without challenges. The high cost of implementation and maintenance of advanced AML systems can pose a barrier to entry for smaller institutions. Furthermore, the constant evolution of money laundering techniques requires continuous adaptation and innovation from software providers to maintain effective detection capabilities. The market’s success hinges on the ongoing arms race between those seeking to circumvent AML regulations and the software providers committed to staying ahead of the curve.

This describes the datasets used for training and evaluation Onto-CGAN model sourced from the MIMIC-IV Clinical Database V2.2. The query script used for data extraction is listed below. Due to the restricted access policies of the MIMIC database, we are unable to publish the extracted subset of MIMIC data. However, researchers with authorized access to the MIMIC-IV database may request the experimental patient data from the corresponding author.SQL Query in MIMIC SELECT DISTINCT diag_table.subject_id, diag_table.icd_code, diag_table.hadm_id, patients_table.gender, patients_table.anchor_age, omr_table.result_name, omr_table.result_value,patients_table.dodFROM physionet-data.mimiciv_hosp.diagnoses_icd AS diag_tableJOIN physionet-data.mimiciv_hosp.patients AS patients_table ON diag_table.subject_id = patients_table.subject_idJOIN physionet-data.mimiciv_hosp.omr AS omr_table ON diag_table.subject_id = omr_table.subject_idWHERE diag_table.icd_code LIKE '20%'

About this Dataset

· Comprehensive event-based data asset of global Anti Money Laundering actions, penalties, imprisonments and other actions, along with the background to the event and the associated entities, organizations, regulators and individuals. · As of August 2021, it covers all countries, over 21 years of records constituting of over 8500 events, 300+ Bn Dollars in penalties, 13,000+ individuals and 2000+ organizations, collected by researching and monitoring via 11,000+ unique sources. Users can access this data via the AML Penalties web application (www.amlpenalties.com) to access, research and build context around events and details by adding country data, regulator data and regulations, global indices, and performance. Users can access this data by directly contacting ZIGRAM via sending an email to siddharth.sabu@zigram.tech. Pricing available on request.

Category

Business Information & Financials

Keywords

AML Penalties,Banking,Monitoring,BSA,Compliance

Row Count

294

Price

$250.00

1) Data Introduction • The Anti Money Laundering Transaction Data (SAML-D) is a large tabular dataset that artificially generates more than 9.5 million transactions, 28 types of transactions (11 normal and 17 suspicious) and various characteristics for anti-money laundering (AML) research.

2) Data Utilization (1) Anti Money Laundering Transaction Data (SAML-D) has characteristics that: • Each transaction consists of 12 major variables: transaction date, transmission and reception account information, amount, payment method (credit card, cash, overseas remittance, etc.), transmission and reception bank location, currency, transaction type, and doubt. • Only about 0.1% of all transactions are labeled as suspicious transactions, and the transaction flow is represented by 15 network structures, enabling complex pattern detection and analysis. (2) Anti Money Laundering Transaction Data (SAML-D) can be used to: • Development of money laundering detection models: They can utilize different types of transactions and suspicious transaction labels to detect abnormal transactions, learn and evaluate AML machine learning models. • Financial Network and Pattern Analysis: applicable to research analyzing complex fund flows and suspicious patterns within financial networks based on transaction network structure and sender/receiver information.