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.

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.

CONTEXT

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• [ Paper describing generation of data and uses of it -- Also to appear at Neurips'2023 ]
• [ Github Site with GNN Models to Predict Laundering ]
• [ Provably Powerful Graph Neural Networks for Directed Multigraphs ] Paper with more detailed description of the GNN models.
• If use the datasets and code and publish papers, we would appreciate if you cite these works.

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Money laundering is a multi-billion dollar issue. Detection of laundering is very difficult. Most automated algorithms have a high false positive rate: legitimate transactions incorrectly flagged as laundering. The converse is also a major problem -- false negatives, i.e. undetected laundering transactions. Naturally, criminals work hard to cover their tracks.

Access to real financial transaction data is highly restricted -- for both proprietary and privacy reasons. Even when access is possible, it is problematic to provide a correct tag (laundering or legitimate) to each transaction -- as noted above. This synthetic transaction data from IBM avoids these problems.

The data provided here is based on a virtual world inhabited by individuals, companies, and banks. Individuals interact with other individuals and companies. Likewise, companies interact with other companies and with individuals. These interactions can take many forms, e.g. purchase of consumer goods and services, purchase orders for industrial supplies, payment of salaries, repayment of loans, and more. These financial transactions are generally conducted via banks, i.e. the payer and receiver both have accounts, with accounts taking multiple forms from checking to credit cards to bitcoin.

Some (small) fraction of the individuals and companies in the generator model engage in criminal behavior -- such as smuggling, illegal gambling, extortion, and more. Criminals obtain funds from these illicit activities, and then try to hide the source of these illicit funds via a series of financial transactions. Such financial transactions to hide illicit funds constitute laundering. Thus, the data available here is labelled and can be used for training and testing AML (Anti Money Laundering) models and for other purposes.

The data generator that created the data here not only models illicit activity, but also tracks funds derived from illicit activity through arbitrarily many transactions -- thus creating the ability to label laundering transactions many steps removed from their illicit source. With this foundation, it is straightforward for the generator to label individual transactions as laundering or legitimate.

Note that this IBM generator models the entire money laundering cycle: - Placement: Sources like smuggling of illicit funds. - Layering: Mixing the illicit funds into the financial system. - Integration: Spending the illicit funds.

As another capability possible only with synthetic data, note that a real bank or other institution typically has access to only a portion of the transactions involved in laundering: the transactions involving that bank. Transactions happening at other banks or between other banks are not seen. Thus, models built on real transactions from one institution can have only a limited view of the world.

By contrast these synthetic transactions contain an entire financial ecosystem. Thus it may be possible to create laundering detection models that undertand the broad sweep of transactions across institutions, but apply those models to make inferences only about transactions at a particular bank.

As another point of reference, IBM previously released data from a very early version of this data generator: https://ibm.box.com/v/AML-Anti-Money-Laundering-Data

The generator has been made significantly more robust since that previous data was released, and these transactions reflect improved realism, bug fixes, and other improvements compared to the previous release.

Credit card transaction data labeled for fraud and built using a related generator is also available on Kaggle: https://www.kaggle.com/datasets/ealtman2019/credit-card-transactions

CONTENT

We release 6 datasets here divided into two groups of three: - Group HI has a relatively higher illicit ratio (more laundering). - Group LI has a relatively lower illicit ratio (less laundering).

Both HI and LI internally have three sets of data: small, medium, and large. The goal is to support a broad degree of modeling and computational resources. All of these datasets are independent, e.g. the small datasets are not ...

The Cancer Moonshot Biobank is a National Cancer Institute initiative to support current and future investigations into drug resistance and sensitivity and other NCI-sponsored cancer research initiatives, with an aim of improving researchers' understanding of cancer and how to intervene in cancer initiation and progression. During the course of this study, biospecimens (blood and tissue removed during medical procedures) and associated data will be collected longitudinally from at least 1000 patients across at least 10 cancer types, who are receiving standard of care cancer treatment at multiple NCI Community Oncology Research Program (NCORP) sites.

This collection contains de-identified radiology and histopathology imaging procured from subjects in NCI’s Cancer Moonshot Biobank - Acute Myeloid Leukemia Cancer (CMB-AML) cohort. Associated genomic, phenotypic and clinical data will be hosted by The Database of Genotypes and Phenotypes (dbGaP) and other NCI databases. A summary of Cancer Moonshot Biobank imaging efforts can be found on the Cancer Moonshot Biobank Imaging page.

Discover a dataset of companies and institutions tied to corporate laundering, with names, countries, and AML network risk ratings for compliance insight.

AML Data Quality Solutions Market Outlook

According to our latest research, the global AML Data Quality Solutions market size reached USD 2.15 billion in 2024, demonstrating robust momentum driven by increasing regulatory scrutiny and the rising sophistication of financial crimes. The market is expected to grow at a CAGR of 15.2% from 2025 to 2033, with projections indicating the market will attain USD 6.04 billion by 2033. The primary growth factor for this market is the escalating demand for advanced anti-money laundering (AML) technologies as organizations worldwide strive to ensure compliance, mitigate financial risks, and enhance operational efficiency in data management processes.

One of the central growth drivers for the AML Data Quality Solutions market is the intensification of global regulatory frameworks. Financial institutions and other regulated entities are under increasing pressure to comply with stringent anti-money laundering regulations such as the EU’s Sixth Anti-Money Laundering Directive, the US Bank Secrecy Act, and FATF guidelines. These regulations mandate robust data quality controls and comprehensive reporting, compelling organizations to invest in sophisticated AML data quality solutions. The proliferation of cross-border transactions and the globalization of financial services further amplify the need for accurate and timely data, as any lapses in data integrity can lead to severe penalties, reputational damage, and increased vulnerability to financial crimes. As a result, organizations are prioritizing investments in technologies that can ensure continuous data validation, enrichment, and monitoring, thereby fueling the market's growth trajectory.

Another significant factor propelling the expansion of the AML Data Quality Solutions market is the rapid digital transformation across industries. The adoption of digital banking, mobile payments, and online financial services has led to an exponential increase in data volumes and complexity. This digital shift has heightened the risk of money laundering and fraud, necessitating advanced solutions capable of handling large-scale, heterogeneous data sources. AML data quality solutions equipped with artificial intelligence, machine learning, and advanced analytics are increasingly in demand, as they enable real-time data processing, anomaly detection, and predictive risk assessment. Furthermore, the integration of these solutions with existing enterprise systems ensures seamless data flow and enhances the overall efficiency of AML compliance operations. The continuous evolution of digital channels and the emergence of new payment methods are expected to sustain the demand for high-performance AML data quality solutions in the coming years.

The growing recognition of data quality as a strategic asset is also contributing to the market's robust growth. Organizations are increasingly aware that poor data quality can undermine the effectiveness of AML programs, leading to false positives, missed suspicious activities, and inefficient resource allocation. As such, there is a heightened focus on deploying end-to-end data quality management solutions that encompass data profiling, cleansing, integration, and governance. These solutions not only improve compliance outcomes but also support broader business objectives such as customer experience enhancement, operational agility, and cost optimization. The convergence of data quality initiatives with enterprise risk management strategies is expected to drive sustained investments in AML data quality solutions, as organizations seek to build resilient and future-ready compliance infrastructures.

From a regional perspective, North America continues to dominate the AML Data Quality Solutions market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The dominance of North America is attributed to the presence of a highly regulated financial sector, early adoption of advanced technologies, and a strong focus on innovation. Europe benefits from stringent regulatory mandates and a mature financial ecosystem, while Asia Pacific is witnessing rapid growth driven by expanding financial services, increasing cross-border transactions, and rising regulatory awareness. Latin America and the Middle East & Africa are also emerging as promising markets, supported by regulatory reforms and digital transformation initiatives. The regional landscape is expected to evolve dynamically, with Asia Pacific projected to exhibit the highest CAGR during the forecast period, reflecting the region's growing emphasis on financial

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.

The Anti-Money Laundering (AML) solutions market is booming, projected to reach $9.44 billion by 2033 with a 15.06% CAGR. Discover key market trends, leading companies (NICE Actimize, SAS, Fiserv), and regional insights in this comprehensive analysis. Learn how AI and RegTech are shaping the future of AML compliance. 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.

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 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).

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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

Discover the booming Anti-Money Laundering (AML) solutions market! This in-depth analysis reveals market size, CAGR, key trends, regional breakdowns, and leading companies, providing insights for businesses and investors. Learn about the impact of AI, regulatory changes, and the growing demand for robust AML compliance.

Tokenized Data Collaboration for AML Market Outlook

As per our latest research, the global Tokenized Data Collaboration for AML market size was recorded at USD 1.47 billion in 2024, with a robust CAGR of 17.8% projected through the forecast period. This growth trajectory will drive the market to reach an estimated USD 7.05 billion by 2033. The primary growth factor fueling this expansion is the increasing demand for secure, privacy-preserving data collaboration solutions that enable organizations to effectively combat money laundering while maintaining regulatory compliance in an era of heightened data privacy concerns.

The market for Tokenized Data Collaboration for AML is experiencing significant momentum due to the convergence of advanced data security technologies and the growing complexity of financial crimes. As anti-money laundering (AML) regulations become more stringent globally, financial institutions and related industries are under immense pressure to enhance their monitoring and detection capabilities without exposing sensitive customer data. Tokenization, which substitutes sensitive data with unique identification symbols that retain all the essential information without compromising security, is increasingly being adopted for secure data sharing and analytics. This approach not only reduces the risk of data breaches but also enables organizations to collaborate across borders and entities, a critical requirement in the fight against sophisticated money laundering networks. The rise in cross-border transactions and digital payment platforms further compounds the need for robust AML solutions, driving the adoption of tokenized data collaboration technologies across sectors.

Another significant growth factor is the rapid digital transformation of the financial services sector, which has led to an exponential increase in the volume and velocity of data generated. Traditional AML systems often struggle to keep pace with this data deluge, resulting in inefficiencies and false positives. Tokenized data collaboration platforms, leveraging artificial intelligence and machine learning, are being integrated to enable real-time transaction monitoring and risk assessment across multiple data sources. This not only enhances the accuracy of AML processes but also facilitates compliance with evolving global data protection regulations such as GDPR and CCPA. The integration of tokenization with advanced analytics tools empowers organizations to derive actionable insights from shared data while ensuring privacy, thus supporting both operational efficiency and regulatory compliance.

The proliferation of digital banking, fintech innovations, and the rise of decentralized finance (DeFi) have also contributed significantly to the growth of the Tokenized Data Collaboration for AML market. As new digital channels and products emerge, so do opportunities for money laundering and financial fraud. Regulatory bodies worldwide are responding with stricter mandates for customer due diligence and continuous transaction monitoring. Tokenized data collaboration enables financial institutions, fintech firms, and regulators to securely share and analyze data on suspicious activities in real-time, bridging the gap between compliance and innovation. This is particularly relevant in emerging markets where digital adoption is accelerating, and regulatory frameworks are rapidly evolving to keep pace with technological advancements.

From a regional perspective, North America currently leads the global Tokenized Data Collaboration for AML market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The dominance of North America is attributed to the presence of major financial institutions, robust regulatory frameworks, and early adoption of advanced data security technologies. Europe’s growth is propelled by stringent data privacy laws and cross-border financial activities, while Asia Pacific is emerging as the fastest-growing region due to rapid digitalization, increasing financial inclusion, and the rising threat of financial crimes. Latin America and the Middle East & Africa are also witnessing increased adoption, driven by regulatory reforms and the need to mitigate growing financial crime risks. The global landscape is characterized by a dynamic interplay of regulatory compliance, technological innovation, and the ever-evolving threat landscape, shaping the future trajectory of the Tokenized Data Collaboration for AML market.

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).

Confidential Data Clean Rooms for AML Market Outlook

According to our latest research, the global Confidential Data Clean Rooms for AML market size reached USD 1.42 billion in 2024, reflecting robust momentum in the adoption of secure data collaboration technologies for anti-money laundering (AML) initiatives. The market is projected to grow at a CAGR of 22.6% from 2025 to 2033, reaching a forecasted value of USD 10.31 billion by 2033. This significant growth is primarily driven by the escalating regulatory requirements, increasing sophistication of financial crimes, and the urgent need for organizations to leverage collaborative analytics without compromising sensitive data privacy.

The growth trajectory of the Confidential Data Clean Rooms for AML market is underpinned by the intensification of global regulatory frameworks around anti-money laundering and counter-terrorist financing. Regulatory bodies are tightening compliance standards, compelling financial institutions and other regulated entities to adopt advanced tools that enable secure, privacy-preserving data analytics. Confidential data clean rooms offer an environment where multiple stakeholders can share and analyze sensitive data without exposing underlying information, thus addressing both compliance and privacy concerns. The ability to conduct joint investigations and analytics while adhering to data protection regulations, such as GDPR and CCPA, is fueling the widespread adoption of these solutions across various sectors.

Another pivotal growth factor is the rapid evolution of financial crime tactics, which has necessitated the use of sophisticated machine learning and artificial intelligence (AI) for effective AML operations. Confidential data clean rooms facilitate the aggregation and analysis of disparate datasets from multiple sources, enhancing the accuracy and efficiency of transaction monitoring, customer due diligence, and risk assessment. By enabling organizations to collaborate securely, these platforms help identify complex money laundering patterns that would be difficult to detect in isolated data silos. The convergence of AI-driven analytics and secure data sharing is therefore a key driver accelerating the market’s expansion.

Furthermore, the proliferation of digital banking, fintech innovations, and cross-border transactions has expanded the attack surface for financial crimes, prompting organizations to seek advanced AML solutions. Confidential data clean rooms address the dual challenge of maintaining data privacy while enabling collaborative intelligence, which is crucial in the era of open banking and digital transformation. The increasing adoption of cloud-based deployment models further enhances the scalability and accessibility of these solutions, making them attractive to both large enterprises and small and medium-sized enterprises (SMEs). As organizations strive to balance innovation with compliance, the demand for confidential data clean rooms is expected to surge across diverse end-user segments.

Regionally, North America currently dominates the Confidential Data Clean Rooms for AML market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The presence of stringent regulatory regimes, advanced technological infrastructure, and a high concentration of financial institutions in these regions drives market leadership. Meanwhile, Asia Pacific is emerging as the fastest-growing region, fueled by rapid digitalization, increasing regulatory scrutiny, and expanding financial ecosystems in countries such as China, India, and Singapore. Latin America and the Middle East & Africa are also witnessing growing adoption, albeit at a relatively moderate pace, as regulatory landscapes and digital maturity continue to evolve.

Component Analysis

The Component segment of the Confidential Data Clean Rooms for AML market is bifurcated into software and services, each playing a critical role in the ecosystem. Software solutions constitute the backbone of confidential data clean rooms, providing the technological framework for secure data sharing, analytics, and collaboration. These platforms are designed to ensure data privacy, enable advanced analytics, and facilitate compliance with regulatory requirements. They often integrate with existing AML systems, offering seamless interoperability and robust security features such as encryption, access controls, and audit trails. The ongoing

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.

Tokenized Data Collaboration for AML Market Outlook

According to our latest research, the Global Tokenized Data Collaboration for AML market size was valued at $1.2 billion in 2024 and is projected to reach $7.6 billion by 2033, expanding at a robust CAGR of 22.8% during 2024–2033. The primary driver fueling this remarkable growth is the escalating demand for secure, privacy-preserving data sharing technologies in the face of increasingly sophisticated money laundering tactics. As financial institutions and regulatory bodies worldwide tighten compliance requirements, the adoption of tokenized data collaboration solutions has become essential for effective anti-money laundering (AML) operations, enabling organizations to share critical intelligence without compromising sensitive information or violating data privacy regulations.

Regional Outlook

North America holds the largest share of the global Tokenized Data Collaboration for AML market, accounting for over 38% of total revenue in 2024. This dominance is attributed to the region’s mature financial sector, early adoption of advanced AML technologies, and stringent regulatory frameworks such as the Bank Secrecy Act and the USA PATRIOT Act. Major US and Canadian banks have been at the forefront of implementing tokenized data collaboration platforms to enhance cross-institutional intelligence sharing while maintaining compliance with evolving privacy laws. The presence of leading technology providers and a robust ecosystem of fintech innovators further bolster North America’s leadership position. Additionally, ongoing investments in cybersecurity infrastructure and government-backed initiatives to combat financial crime continue to drive market expansion in this region.

The Asia Pacific region is projected to exhibit the fastest growth, with a forecasted CAGR of 27.3% from 2024 to 2033. Rapid digitalization of banking and financial services, coupled with rising incidents of financial fraud and money laundering, is accelerating the adoption of tokenized data collaboration solutions across key markets such as China, India, Singapore, and Australia. Governments and regulatory agencies in the region are increasingly mandating advanced AML compliance measures, spurring investment in innovative data security and privacy-preserving technologies. The influx of fintech startups and the expansion of cross-border digital payment ecosystems are further catalyzing demand, as organizations seek scalable and interoperable solutions to manage AML risks in a highly dynamic regulatory environment.

Emerging economies in Latin America, the Middle East, and Africa are gradually embracing tokenized data collaboration for AML, albeit at a slower pace. Challenges such as limited technological infrastructure, fragmented regulatory landscapes, and lower awareness about the benefits of tokenization hinder widespread adoption. However, localized demand is rising as financial crimes become more sophisticated and cross-border transactions increase. Policy reforms and international collaborations aimed at strengthening AML frameworks are expected to unlock future growth potential. Regional governments are beginning to recognize the strategic importance of secure data collaboration, and pilot projects in countries like Brazil, South Africa, and the UAE signal a positive outlook for gradual market penetration.

Report Scope

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

Snapshot img

Anti-Money Laundering (AML) Software Market Size 2024-2028

The anti-money laundering (aml) software market size is valued to increase by USD 3.57 billion, at a CAGR of 16.54% from 2023 to 2028. Increased need for suspicious activity reporting will drive the anti-money laundering (aml) software market.

Major Market Trends & Insights

APAC dominated the market and accounted for a 35% growth during the forecast period.
By Deployment - On-premises segment was valued at USD 1.16 billion in 2022
By Component - Software segment accounted for the largest market revenue share in 2022

Market Size & Forecast

Market Opportunities: USD 295.59 million
Market Future Opportunities: USD 3566.40 million
CAGR from 2023 to 2028 : 16.54%

Market Summary

The market witnesses growing significance as financial institutions and businesses strive to mitigate financial risks and ensure regulatory compliance. AML software plays a crucial role in detecting and preventing money laundering, terrorist financing, and other financial crimes. One of the key drivers for the market is the increasing need for suspicious activity reporting, which has become a regulatory requirement in many jurisdictions. Another trend in the market is the integration of AML software with visualization tools, enabling financial institutions to gain a more comprehensive understanding of complex transaction patterns and relationships. However, the high cost of implementation and maintenance remains a challenge for many organizations, particularly smaller ones.
For instance, a large retailer implemented an AML solution to optimize its supply chain operations and improve compliance. The solution helped the retailer reduce false positives by 30%, enabling its compliance team to focus on high-risk transactions. This resulted in significant time and cost savings, as well as improved operational efficiency. Despite the benefits, the implementation of AML software requires a significant investment in terms of time, resources, and expertise. Organizations must carefully evaluate their needs, budgets, and regulatory requirements before selecting a solution. By doing so, they can ensure that they are effectively mitigating financial risks and maintaining regulatory compliance.

What will be the Size of the Anti-Money Laundering (AML) Software Market during the forecast period?

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How is the Anti-Money Laundering (AML) Software Market Segmented ?

The anti-money laundering (aml) software industry 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.

Deployment

  On-premises
  Cloud-based


Component

  Software
  Services


Geography

  North America

    US


  Europe

    Germany
    UK


  APAC

    China
    India


  Rest of World (ROW)

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.

The market continues to evolve, with regulatory compliance and financial crime prevention remaining top priorities for financial institutions. AML compliance software integrates features such as due diligence workflow, Data Analytics dashboard, and machine learning algorithms for investigative data analysis. Regulatory reporting, behavioral biometrics, and network analysis techniques are also crucial components, enabling suspicious activity reporting and transaction screening. On-premises AML solutions, which account for a significant market share, offer enterprise-wide monitoring, investigations, and reporting, along with customized thresholds and risk scoring.

Adoption of these robust, expensive, and highly customizable solutions is prevalent among large complex financial firms, with 70% of them preferring on-premises deployment for enhanced security and control. The market also encompasses compliance automation, case management systems, and risk mitigation strategies, including sanctions screening, AML investigation tools, and risk scoring models. Data aggregation platforms and identity verification technology further bolster financial crime prevention efforts.

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The On-premises segment was valued at USD 1.16 billion in 2018 and showed a gradual increase during the forecast period.

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Regional Analysis

APAC is estimated to contribute 35% 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.

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The market is experiencing significant evolution, driven by the increasing complexity of money

Data Collaboration for AML Market Outlook

According to our latest research, the Global Data Collaboration for AML market size was valued at $2.6 billion in 2024 and is projected to reach $8.1 billion by 2033, expanding at a robust CAGR of 13.2% during the forecast period of 2025–2033. The primary driver behind this impressive growth trajectory is the increasing sophistication of financial crimes, which has necessitated the adoption of advanced anti-money laundering (AML) solutions that leverage data collaboration across institutions, geographies, and regulatory environments. The proliferation of digital transactions, combined with evolving regulatory mandates, has compelled financial institutions and related entities to invest heavily in collaborative AML platforms, ensuring comprehensive risk detection and compliance management.

Regional Outlook

North America currently holds the largest share of the global Data Collaboration for AML market, accounting for approximately 38% of the total market value in 2024. This dominance is attributed to the mature financial ecosystem, stringent regulatory frameworks such as the Bank Secrecy Act and the USA PATRIOT Act, and a high degree of technological adoption among banks and financial institutions. The region’s well-established infrastructure for digital payments and cross-border transactions further amplifies the demand for sophisticated AML solutions that enable secure, real-time data sharing and collaborative threat detection. Leading vendors and innovative fintech startups are concentrated in the United States and Canada, accelerating the integration of artificial intelligence, machine learning, and blockchain technologies in AML workflows, which in turn boosts market growth.

The Asia Pacific region is expected to register the fastest CAGR, projected at 16.7% during 2025–2033, fueled by rapid digitalization, the surge in online financial services, and increasing regulatory scrutiny across emerging economies. Countries such as China, India, Singapore, and Australia are at the forefront of adopting cloud-based AML collaboration tools, driven by rising incidents of financial fraud and government initiatives to strengthen anti-money laundering frameworks. The influx of foreign investments, expansion of fintech ecosystems, and supportive policy reforms are catalyzing the deployment of advanced AML technologies. Additionally, regional collaborations among financial institutions and regulatory bodies are fostering a culture of shared intelligence, further propelling market expansion in this geography.

In emerging economies across Latin America and the Middle East & Africa, the adoption of Data Collaboration for AML solutions remains at a nascent stage, primarily due to infrastructural constraints, fragmented regulatory environments, and limited awareness among smaller financial entities. However, there is growing interest in leveraging collaborative platforms to combat rising financial crime rates, especially as governments introduce new compliance mandates and cross-border trade increases. Localized demand is beginning to pick up, particularly in urban financial hubs, but challenges such as data privacy concerns, lack of skilled workforce, and inconsistent enforcement of AML standards continue to hinder widespread adoption. Nevertheless, international partnerships and capacity-building initiatives are expected to gradually bridge these gaps, unlocking new growth avenues in the long term.

Report Scope