AI-Based Anti-Money Laundering (AML) Solutions Market
AI-Based Anti-Money Laundering (AML) Solutions Market Study by Transaction Monitoring, KYC, Crime Pattern Detection, Risk Scoring Customers & Accounts, and Others from 2023 to 2033
Analysis of AI-Based Anti-Money Laundering (AML) Solutions Market Covering 30+ Countries Including Analysis of US, Canada, UK, Germany, France, Nordics, GCC countries, Japan, Korea and many more
AI-Based Anti-Money Laundering (AML) Solutions Market
According to Fact.MR’s latest industry analysis, the global AI-based anti-money laundering (AML) solutions market stands at a valuation of US$ 1.94 billion in 2023. Sales of AI-based anti-money laundering solutions are forecasted to increase at a robust CAGR of 15.9% and reach US$ 8.49 billion by the end of 2033.
Money laundering is a complex and widespread criminal activity that involves concealing the source of illegally obtained funds, making it difficult to trace and detect. To combat this issue, financial institutions and regulatory bodies are turning to advanced technologies, particularly artificial intelligence (AI), to enhance their anti-money laundering efforts.
AI-based AML solutions leverage the power of machine learning algorithms and data analytics to identify suspicious activities, reduce false positives, and improve overall detection capabilities. AI-based anti-money laundering solutions are widely used for transaction monitoring, KYC, crime pattern detection, risk scoring customers & accounts, watchlist screening, alert management & reporting, and fraud, risk, & compliance.
- Demand for AI-based AML solutions for fraud detection and risk & compliance is predicted to increase at a 16.2% CAGR from 2023 to 2033.
AI algorithms can analyze vast amounts of structured and unstructured data from diverse sources, including transaction records, customer profiles, news articles, and social media posts. By analyzing patterns, anomalies, and relationships within the data, AI can identify potential money laundering activities more accurately and efficiently than traditional rule-based systems.
Fraud and compliance risks are constantly evolving, and criminals employ sophisticated techniques to evade detection. AI-based AML solutions can adapt to these emerging threats by continuously learning from new data and evolving their detection models accordingly. This enables financial institutions to stay ahead of fraudsters and mitigate risks effectively.
- In February 2021, Experian introduced an upgraded version of its fraud prevention platform, specifically designed to cater to businesses facing increased demand for digital services or a surge in the number of online accounts.
| Report Attributes | Details |
|---|---|
|
AI-Based AML Solutions Market Size (2023E) |
US$ 1.94 Billion |
|
Forecasted Market Value (2033F) |
US$ 8.49 Billion |
|
Global Market Growth Rate (2023 to 2033) |
15.9% CAGR |
|
Germany Market Growth Rate (2023 to 2033) |
16.6% CAGR |
|
United States Market Growth Rate (2023 to 2033) |
16.4% CAGR |
|
United Kingdom Market Value (2033F) |
US$ 517.84 Million |
|
China Market Value (2033F) |
US$ 1.71 Billion |
|
India Market Growth Rate (2023 to 2033) |
15.1% CAGR |
|
Key Companies Profiled |
|
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Why is Demand for AI-Based AML Solutions Gaining Traction?
“Technological Revolution and Rising Cases of Financial Fraud”
Since the enactment of the Bank Secretary Act (BSA) in 1970, anti-money laundering compliance has played a vital role in fortifying financial systems against money laundering, terrorist financing, and other illicit financial crimes. Over the years, AML technology compliance has witnessed a profound transformation, embracing artificial intelligence (AI) and regulatory layers within the financial domain.
- Despite rigorous regulatory reforms, instances of money laundering and breaches continue to rise, leading to significant penalties. The United States Department of the Treasury estimates that US$ 1.6 trillion of global money is involved in money laundering each year, accounting for 2.7% of the global GDP.
The remarkable technological revolution has brought about a paradigm shift in AML compliance services, empowering financial systems to confront the challenges posed by money laundering head-on. While the escalating occurrences of money laundering and illicit financial transactions present lucrative opportunities for AI-based AML solution providers, the proliferation of regulations and technological constraints pose future milestones for AML compliance providers to overcome.
Relentless surge in anti-money laundering activities and transaction volumes has intensified the demand for AI-based AML software and solutions, significantly enhancing the operational efficiency of companies and banks in recent years. Artificial intelligence has emerged as a game-changer, reducing the cost of AML compliance by seamlessly automating tasks that were once carried out by humans.
With AI at the helm, AML compliance providers can efficiently analyze vast volumes of data, detect intricate patterns, and identify suspicious activities in real time. These advanced solutions not only bolster the speed and accuracy of fraud detection but also alleviate the burden of manual investigations, enabling financial institutions to focus their resources on more complex tasks and strategic decision-making.
“Increasing Adoption of Big Data Analytics in Uncovering Fraud and Money Laundering”
The advent of big data analytics has revolutionized the fight against fraud and money laundering by enabling extensive searches for suspicious abnormalities among millions of transactions. With the responsibility to identify fraudulent activities falling on companies, substantial investments have been made in highly advanced big data approaches, amounting to billions of dollars. This technology has proven particularly valuable for financial institutions, such as banks, in analyzing their customers and transactional data.
By combining data analytics and machine learning, businesses can refine their transaction monitoring algorithms to detect more instances of suspicious activity while minimizing false positives. This empowers organizations to swiftly identify potential fraud and money laundering attempts, ensuring proactive intervention to mitigate risks.
In addition to improved detection capabilities, modern case management technologies have simplified the reporting and investigation processes. These tools streamline the handling of suspicious activities, enabling efficient collaboration among different stakeholders involved in the AML process.
AML laws are continually evolving and expanding under the guidance of organizations such as the Financial Crimes Enforcement Network (FinCEN), Financial Action Task Force (FATF), and Office of Foreign Assets Control (OFAC), aiming to keep the banking sector one step ahead of criminal activities.
Compliance with these regulations is not limited to banks alone; any company facilitating the movement of funds, such as online marketplaces, cryptocurrencies, fintech firms, or gaming platforms, must establish robust AML programs to prevent hefty fines and uphold their integrity.
As the battle against financial crime intensifies, the synergy between advanced technologies and regulatory frameworks becomes crucial. Leveraging the potential of big data analytics and machine learning, businesses can enhance their AML programs, ensuring compliance, protecting their customers, and safeguarding the integrity of the financial ecosystem.
What are the Hindrances to the Deployment of AI-based AML Solutions?
“Lack of Expertise of Implementation and Maintenance Workforce in AI Technologies and AML”
Implementing and managing AI-based AML solutions requires a skilled workforce with expertise in both AI technologies and AML processes. The shortage of professionals with these combined skills poses a challenge for organizations aiming to adopt AI-based AML solutions effectively. Bridging this skill gap and building robust teams with the required expertise can be a time-consuming and costly process.
“High Cost of AI-based AML Solutions”
Implementing AI-based AML solutions can involve significant upfront costs, including investment in technology infrastructure, data management systems, and skilled personnel. The integration of AI into existing AML systems and processes can also be complex and time-consuming, requiring careful planning and coordination.
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What Steps are New Companies Adopting to Stay Ahead of the Curve in the Market?
“Strong Focus of Start-ups on Investments in R&D Activities for Enhanced AML Detection Solutions”
In the dynamic landscape of the AI-based anti-money laundering solutions market, new entrants can employ creative strategies to overcome the challenges and earn more. They can also follow new trends to achieve a steady market position.
Developing close partnerships with regulatory bodies would aid newcomers to gain insights into evolving compliance requirements. They can also proactively engage in industry forums and contribute to shaping regulations by providing expertise and thought leadership. Developing flexible and modular solutions that can adapt to regulatory changes swiftly, ensuring compliance without disrupting operations would also help new entrants to earn more.
Start-ups should also stay at the forefront of technological advancements in AI, such as machine learning, natural language processing, and deep learning. Continuously investing in research and development activities to employ the latest algorithms and methodologies that enhance AML detection capabilities is expected to boost the newcomers’ revenue growth.
- Merlon Intelligence founded in 2016 specializes in AI-driven solutions for AML and transaction monitoring. The company’s platform leverages machine learning algorithms to detect suspicious activities, generate risk assessments, and facilitate regulatory reporting for financial institutions.
Country-wise Analysis
What Makes the United States a Lucrative Market for Providers of AI-based Anti-money Laundering Solutions?
“Stringent Regulations Governing Money Laundering Detection and Prevention”
Demand for AI-based AML solutions in the United States is estimated to evolve at a CAGR of 16.4% from 2023 to 2033.
The United States has stringent regulations and compliance requirements for financial institutions to prevent money laundering. This has propelled the demand for AI-based AML solutions that can effectively detect suspicious transactions and ensure compliance with regulatory standards.
Banks, insurance companies, and other financial institutions are embracing AI-based AML solutions to strengthen their anti-money laundering efforts. These solutions leverage advanced machine learning algorithms to analyze large volumes of data and identify patterns indicative of money laundering activities.
- In June 2022, ACI Worldwide, a renowned global provider of mission-critical real-time payment software, announced its decision to divest its corporate online banking solutions, ACI Digital Business Banking. The divestment agreement was made with One Equity Partners, a prominent middle-market private equity firm. This strategic move allows ACI Worldwide to streamline its focus on its core offerings in the real-time payment software domain while providing an opportunity for One Equity Partners to invest and grow ACI Digital Business Banking as a separate entity.
What is the Demand Outlook for AI-based AML Solutions in Germany?
“Partnerships between Banks and Fintech Companies Driving Demand for AI-Based AML Solutions for Enhanced Data Protection and Privacy Compliance”
The market in Germany is projected to reach US$ 755.54 million by 2033.
In Germany, several banks are partnering with fintech companies specializing in AI and data analytics to develop innovative AML solutions. This collaboration is enabling the integration of advanced technologies into existing financial systems, enhancing detection capabilities.
Germany places a strong focus on data protection and privacy. AI-based AML solutions must adhere to strict data security measures, including encryption and anonymization, to comply with German regulations. Thus, vendors offering robust data protection mechanisms have a competitive advantage in this marketplace.
Why is China Evolving as a Huge Market for AI-based AML Solution Providers?
“Rapid Digitalization and Consequent Increase in Online Transactions Boosting Demand for Integration of AI with Big Data Analytics”
Sales of AI-based AML solutions in China are forecasted to reach US$ 1.71 billion by the end of the forecast period.
Rapid digitalization in China is increasing the number of online transactions, making it crucial for financial institutions to deploy AI-based AML solutions to detect and prevent money laundering in digital channels. The market is witnessing the emergence of innovative solutions that leverage AI, machine learning, and natural language processing to analyze digital data and identify illicit activities.
Integration of AI with big data analytics enables efficient analysis of vast amounts of information, facilitating the identification of suspicious transactions. Consequently, this is boosting the demand for AI-based AML solutions in the country.
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Category-wise Analysis
Why are AI-based AML Solutions Extensively Deployed in Banks?
“Need for Reducing Compliance Audit Costs and Minimizing False Positive Fraud Alerts in Banks”
According to Fact.MR research, banks currently hold 56.9% share of the global AI-based anti-money laundering solutions market.
Banks play a crucial role as the primary providers of funds and handle millions of daily transactions. With such a vast volume, they often encounter numerous false positive indications of financial crime through their anti-money laundering solutions. This not only raises costs but also consumes significant time and effort in tracking and verifying each transaction for potential money laundering activities. Consequently, banks face substantial expenses and the risk of hefty fines.
Recognizing the need for effective AML measures, banks have turned to artificial intelligence (AI) solutions. By incorporating AI into their AML systems, they aim to mitigate the expenses associated with AML compliance audits, minimize false positive alerts, and simplify the complexity of AML processes.
Competitive Landscape
Key market players are investing in research and development projects to enhance the capabilities of their AML solutions. They are also forming strategic partnerships and collaborations with other industry players, such as financial institutions, technology providers, or regulatory bodies. These partnerships allow for knowledge-sharing, access to new markets, and the development of comprehensive solutions that address the specific needs of different stakeholders.
- WL-X, a ground-breaking, next-generation Watch List (WL) screening system that uses artificial intelligence for enhanced data management, enhanced screening capabilities, and seamless customer onboarding, was introduced by Nice Actimize on February 11, 2021.
- ThetaRay, a supplier of AI-based big data analytics, introduced SONAR Solutions on April 27, 2021. This is an anti-money laundering cross-border payments solution as a cloud service.
Key Segments Covered in AI-Based AML Solutions Industry Research
-
By Use Case :
- Transaction Monitoring
- KYC
- Crime Pattern Detection
- Risk Scoring Customers & Accounts
- Watch-list Screening
- Alert Management & Reporting
- Fraud, Risk, & Compliance
-
By End User :
- Banks
- Insurance Companies
- Asset Management
- Money Service Businesses
- Securities
-
By Region :
- North America
- Latin America
- Europe
- East Asia
- South Asia & Oceania
- MEA
Table of Content
1. Executive Summary 1.1. Global Market Outlook 1.2. Summary of Statistics 1.3. Summary of Key Findings 1.4. Fact.MR Analysis and Recommendations 2. Global Market Overview 2.1. Market Introduction 2.2. Global Market Taxonomy 2.3. Global Market Definition 3. Market Background and Foundation Data Points 3.1. Global Economic Loss due to Money Laundering 3.2. Money Laundering Prevention Measure and Technologies 3.3. Notable Policy and Regulation for Anti-Money Laundering (AML) 3.4. Penalties to Financial Institutions for AML 3.5. Role of AI/ML in Anti-Money Laundering 3.6. Challenges in AI / ML implementation in Banking System 3.6.1. DQM and Profile Refresh 3.6.2. Limited Customer Data Accessibility to AI/ML Applications 3.6.3. Gap in Comprehensive Understanding of AI Technologies and Banking Infrastructure 3.6.4. Challenges in Investigation Process 3.6.4.1. Investigations take too long to complete 3.6.4.2. Incompatible/outmoded systems/tools 3.6.4.3. High number of false positive/ unsubstantiated alerts 3.6.4.4. Lack of data/insight around customers, accounts and entities 3.6.4.5. Too much information to sort through in time allotted 3.6.4.6. Increasing complexity of regulatory requirements 3.6.4.7. Lack of skilled resources 3.6.4.8. Management pressure to increase efficiency, cost-effectiveness 3.7. Success Case Studies 3.7.1. Bank Lowering False Positive Cases with AI Technologies 3.7.2. Banks Speeding AML Investigation using AI Technologies 3.7.3. Banks Speeding EDD Investigation 4. Global Market Value Analysis 2018-2022 and Forecast, 2023-2033 4.1. Historical Market Value (US$ Mn) Analysis, 2018-2022 4.2. Current and Future Market Value (US$ Mn) Projections, 2023-2033 4.2.1. Y-o-Y Growth Trend Analysis 4.2.2. Absolute $ Opportunity Analysis 5. Global Market Analysis 2018-2022 and Forecast 2023-2033, By Use Case 5.1. Introduction / Key Findings 5.2. Historical Market Value (US$ Mn) Analysis By Use Case, 2018-2022 5.3. Current and Future Market Value (US$ Mn) Analysis and Forecast By Use Case, 2023 – 2033 5.3.1. Transaction Monitoring 5.3.2. KYC (Know Your Customer) 5.3.3. Fraud, Risk & Compliance 5.3.3.1. Trade AML 5.3.3.2. Capital Markets AML 5.3.3.3. Correspondent Banking AML 5.3.3.4. Fraud 5.3.3.5. Credit Risk 5.3.4. Crime Pattern Detection 5.3.5. Risk Scoring Customers and Accounts 5.3.6. Watch-List Screening 5.3.7. Alert Management and Reporting 5.3.8. Other Solutions 5.4. Market Attractiveness Analysis By Use Case 6. Global Market Analysis 2018-2022 and Forecast 2023-2033, By End User 6.1. Introduction / Key Findings 6.2. Historical Market Value (US$ Mn) Analysis By End Use, 2018-2022 6.3. Current and Future Market Value (US$ Mn) Analysis and Forecast By End Use, 2023 – 2033 6.3.1. Bank 6.3.2. Insurance company 6.3.3. Asset management 6.3.4. Money Services Business 6.3.5. Securities 6.3.6. Other FSI 6.4. Market Attractiveness Analysis By End User 7. Global Market Analysis 2018-2022 and Forecast 2023-2033, By Region 7.1. Introduction 7.2. Historical Market Value (US$ Mn) Analysis By Region, 2018-2022 7.3. Current Market Value (US$ Mn) and Volume Units) Analysis and Forecast By Region, 2023 - 2033 7.3.1. North America 7.3.2. Latin America 7.3.3. Europe 7.3.4. East Asia 7.3.5. South Asia & Oceania 7.3.6. Middle East and Africa (MEA) 7.4. Market Attractiveness Analysis By Region 8. North America Market Analysis 2018-2022 and Forecast 2023-2033 8.1. Introduction 8.2. Historical Market Size (US$ Mn) Trend Analysis By Market Taxonomy, 2018-2022 8.3. Market Size (US$ Mn) Forecast By Market Taxonomy, 2023 - 2033 8.3.1. By Country 8.3.1.1. U.S. 8.3.1.2. Canada 8.3.2. By Use Case 8.3.3. By End-User 8.4. Market Attractiveness Analysis 8.4.1. By Country 8.4.2. By Use Case 8.4.3. By End-User 9. Latin America Market Analysis 2018-2022 and Forecast 2023-2033 9.1. Introduction 9.2. Historical Market Size (US$ Mn) Trend Analysis By Market Taxonomy, 2018-2022 9.3. Market Size (US$ Mn) Forecast By Market Taxonomy, 2023 - 2033 9.3.1. By Country 9.3.1.1. Brazil 9.3.1.2. Mexico 9.3.1.3. Rest of Latin America 9.3.2. By By Use Case 9.3.3. By End-User 9.4. Market Attractiveness Analysis 9.4.1. By Country 9.4.2. By Use Case 9.4.3. By End-User 10. Europe America Market Analysis 2018-2022 and Forecast 2023-2033 10.1. Introduction 10.2. Historical Market Size (US$ Mn) Trend Analysis By Market Taxonomy, 2018-2022 10.3. Market Size (US$ Mn) Forecast By Market Taxonomy, 2023 - 2033 10.3.1. By Country 10.3.1.1. Brazil 10.3.1.2. Mexico 10.3.1.3. Rest of Latin America 10.3.2. By Use Case 10.3.3. By End-User 10.4. Market Attractiveness Analysis 10.4.1. By Country 10.4.2. By Use Case 10.4.3. By End-User 11. East Asia America Market Analysis 2018-2022 and Forecast 2023-2033 11.1. Introduction 11.2. Historical Market Size (US$ Mn) Trend Analysis By Market Taxonomy, 2018-2022 11.3. Market Size (US$ Mn) Forecast By Market Taxonomy, 2023 - 2033 11.3.1. By Country 11.3.1.1. China 11.3.1.2. Japan 11.3.1.3. South Korea 11.3.2. By Use Case 11.3.3. By End-User 11.4. Market Attractiveness Analysis 11.4.1. By Country 11.4.2. By Use Case 11.4.3. By End-User 12. South Asia & Oceania America Market Analysis 2018-2022 and Forecast 2023-2033 12.1. Introduction 12.2. Historical Market Size (US$ Mn) Trend Analysis By Market Taxonomy, 2018-2022 12.3. Market Size (US$ Mn) Forecast By Market Taxonomy, 2023 - 2033 12.3.1. By Country 12.3.1.1. India 12.3.1.2. Thailand 12.3.1.3. Malaysia 12.3.1.4. Indonesia 12.3.1.5. Australia & New Zealand 12.3.1.6. Rest of South Asia & Oceania 12.3.2. By Use Case 12.3.3. By End-User 12.4. Market Attractiveness Analysis 12.4.1. By Country 12.4.2. By Use Case 12.4.3. By End-User 13. MEA America Market Analysis 2018-2022 and Forecast 2023-2033 13.1. Introduction 13.2. Historical Market Size (US$ Mn) Trend Analysis By Market Taxonomy, 2018-2022 13.3. Market Size (US$ Mn) Forecast By Market Taxonomy, 2023 - 2033 13.3.1. By Country 13.3.1.1. GCC Countries 13.3.1.2. Turkey 13.3.1.3. South Africa 13.3.1.4. Rest of MEA 13.3.2. By Use Case 13.3.3. By End-User 13.4. Market Attractiveness Analysis 13.4.1. By Country 13.4.2. By Use Case 13.4.3. By End-User 14. Key Countries 14.1. Introduction 14.1.1. Market Value Proportion Analysis, By Key Countries 14.1.2. Global Vs. Country Growth Comparison 14.2. US Market Analysis 14.2.1. Value Proportion Analysis by Market Taxonomy 14.2.2. Value Analysis and Forecast by Market Taxonomy, 2018-2033 14.2.2.1. By Use Case 14.2.2.2. By End-User 14.2.3. Competition Landscape and Player Concentration in the Country 14.3. Canada Market Analysis 14.3.1. Value Proportion Analysis by Market Taxonomy 14.3.2. Value Analysis and Forecast by Market Taxonomy, 2018-2033 14.3.2.1. By Use Case 14.3.2.2. By End-User 14.3.3. Competition Landscape and Player Concentration in the Country 14.4. Brazil Market Analysis 14.4.1. Value Proportion Analysis by Market Taxonomy 14.4.2. Value Analysis and Forecast by Market Taxonomy, 2018-2033 14.4.2.1. By Use Case 14.4.2.2. By End-User 14.4.3. Competition Landscape and Player Concentration in the Country 14.5. Mexico Market Analysis 14.5.1. Value Proportion Analysis by Market Taxonomy 14.5.2. Value Analysis and Forecast by Market Taxonomy, 2018-2033 14.5.2.1. By Use Case 14.5.2.2. By End-User 14.5.3. Competition Landscape and Player Concentration in the Country 14.6. Germany Market Analysis 14.6.1. Value Proportion Analysis by Market Taxonomy 14.6.2. Value Analysis and Forecast by Market Taxonomy, 2018-2033 14.6.2.1. By Use Case 14.6.2.2. By End-User 14.6.3. Competition Landscape and Player Concentration in the Country 14.7. France Market Analysis 14.7.1. Value Proportion Analysis by Market Taxonomy 14.7.2. Value Analysis and Forecast by Market Taxonomy, 2018-2033 14.7.2.1. By Use Case 14.7.2.2. By End-User 14.7.2.3. 14.7.2.4. By End-User 14.7.3. Competition Landscape and Player Concentration in the Country 14.8. United Kingdom Market Analysis 14.8.1. Value Proportion Analysis by Market Taxonomy 14.8.2. Value Analysis and Forecast by Market Taxonomy, 2018-2033 14.8.2.1. By Use Case 14.8.2.2. By End-User 14.8.3. Competition Landscape and Player Concentration in the Country 14.9. Italy Market Analysis 14.9.1. Value Proportion Analysis by Market Taxonomy 14.9.2. Value Analysis and Forecast by Market Taxonomy, 2018-2033 14.9.2.1. By Use Case 14.9.2.2. By End-User 14.9.3. Competition Landscape and Player Concentration in the Country 14.10. BENELUX Market Analysis 14.10.1. Value Proportion Analysis by Market Taxonomy 14.10.2. Value Analysis and Forecast by Market Taxonomy, 2018-2033 14.10.2.1. By Use Case 14.10.2.2. By End-User 14.10.3. Competition Landscape and Player Concentration in the Country 14.11. Nordic Countries Market Analysis 14.11.1. Value Proportion Analysis by Market Taxonomy 14.11.2. Value Analysis and Forecast by Market Taxonomy, 2018-2033 14.11.2.1. By Use Case 14.11.2.2. By End-User 14.11.3. Competition Landscape and Player Concentration in the Country 14.12. China Market Analysis 14.12.1. Value Proportion Analysis by Market Taxonomy 14.12.2. Value Analysis and Forecast by Market Taxonomy, 2018-2033 14.12.2.1. By Use Case 14.12.2.2. By End-User 14.12.3. Competition Landscape and Player Concentration in the Country 14.13. Japan Market Analysis 14.13.1. Value Proportion Analysis by Market Taxonomy 14.13.2. Value Analysis and Forecast by Market Taxonomy, 2018-2033 14.13.2.1. By Use Case 14.13.2.2. By End-User 14.13.3. Competition Landscape and Player Concentration in the Country 14.14. South Korea Market Analysis 14.14.1. Value Proportion Analysis by Market Taxonomy 14.14.2. Value Analysis and Forecast by Market Taxonomy, 2018-2033 14.14.2.1. By Use Case 14.14.2.2. By End-User 14.14.3. Competition Landscape and Player Concentration in the Country 14.15. India Market Analysis 14.15.1. Value Proportion Analysis by Market Taxonomy 14.15.2. Value Analysis and Forecast by Market Taxonomy, 2018-2033 14.15.2.1. By Use Case 14.15.2.2. By End-User 14.15.3. Competition Landscape and Player Concentration in the Country 14.16. Malaysia Market Analysis 14.16.1. Value Proportion Analysis by Market Taxonomy 14.16.2. Value Analysis and Forecast by Market Taxonomy, 2018-2033 14.16.2.1. By Use Case 14.16.2.2. By End-User 14.16.3. Competition Landscape and Player Concentration in the Country 14.17. Singapore Market Analysis 14.17.1. Value Proportion Analysis by Market Taxonomy 14.17.2. Value Analysis and Forecast by Market Taxonomy, 2018-2033 14.17.2.1. By Use Case 14.17.2.2. By End-User 14.17.3. Competition Landscape and Player Concentration in the Country 14.18. Australia Market Analysis 14.18.1. Value Proportion Analysis by Market Taxonomy 14.18.2. Value Analysis and Forecast by Market Taxonomy, 2018-2033 14.18.2.1. By Use Case 14.18.2.2. By End-User 14.18.3. Competition Landscape and Player Concentration in the Country 14.19. New Zealand Market Analysis 14.19.1. Value Proportion Analysis by Market Taxonomy 14.19.2. Value Analysis and Forecast by Market Taxonomy, 2018-2033 14.19.2.1. By Use Case 14.19.2.2. By End-User 14.19.3. Competition Landscape and Player Concentration in the Country 14.20. GCC Countries Market Analysis 14.20.1. Value Proportion Analysis by Market Taxonomy 14.20.2. Value Analysis and Forecast by Market Taxonomy, 2018-2033 14.20.2.1. By Use Case 14.20.2.2. By End-User 14.20.3. Competition Landscape and Player Concentration in the Country 14.21. Turkey Market Analysis 14.21.1. Value Proportion Analysis by Market Taxonomy 14.21.2. Value Analysis and Forecast by Market Taxonomy, 2018-2033 14.21.2.1. By Use Case 14.21.2.2. By End-User 14.21.3. Competition Landscape and Player Concentration in the Country 14.22. South Africa Market Analysis 14.22.1. Value Proportion Analysis by Market Taxonomy 14.22.2. Value Analysis and Forecast by Market Taxonomy, 2018-2033 14.22.2.1. By Use Case 14.22.2.2. By End-User 14.22.3. Competition Landscape and Player Concentration in the Country 15. AI Based AML Competition Landscape and Company Profile 15.1. Competition Dashboard 15.2. Competition Benchmarking 15.3. Competition Deep Dive 15.3.1. Feeszai Inc. 15.3.1.1. Company Overview 15.3.1.2. Product Overview 15.3.1.3. Key Financials 15.3.1.4. Key Developments 15.3.1.5. SWOT Analysis 15.3.2. Jumio Inc. 15.3.2.1. Company Overview 15.3.2.2. Product Overview 15.3.2.3. Key Financials 15.3.2.4. Key Developments 15.3.2.5. SWOT Analysis 15.3.3. ACI Worldwide, Inc. 15.3.3.1. Company Overview 15.3.3.2. Product Overview 15.3.3.3. Key Financials 15.3.3.4. Key Developments 15.3.3.5. SWOT Analysis 15.3.4. Brighterion, Inc. 15.3.4.1. Company Overview 15.3.4.2. Product Overview 15.3.4.3. Key Financials 15.3.4.4. Key Developments 15.3.4.5. SWOT Analysis 15.3.5. DataVisor Inc. 15.3.5.1. Company Overview 15.3.5.2. Product Overview 15.3.5.3. Key Financials 15.3.5.4. Key Developments 15.3.5.5. SWOT Analysis 15.3.6. Featurespace Limited 15.3.6.1. Company Overview 15.3.6.2. Product Overview 15.3.6.3. Key Financials 15.3.7. FICO (Fair, Isaac and Company) 15.3.7.1. Company Overview 15.3.7.2. Product Overview 15.3.7.3. Key Financials 15.3.7.4. Key Developments 15.3.7.5. SWOT Analysis 15.3.8. ThetaRay 15.3.8.1. Company Overview 15.3.8.2. Product Overview 15.3.8.3. Key Financials 15.3.8.4. Key Developments 15.3.8.5. SWOT Analysis 15.3.9. Ayasdi AI LLC 15.3.9.1. Company Overview 15.3.9.2. Product Overview 15.3.9.3. Key Financials 15.3.9.4. Key Developments 15.3.9.5. SWOT Analysis 15.3.10. Tookitaki Holding Pte. Ltd 15.3.10.1. Company Overview 15.3.10.2. Product Overview 15.3.10.3. Key Financials 15.3.10.4. Key Developments 15.3.10.5. SWOT Analysis 15.3.11. BAE Systems 15.3.11.1. Company Overview 15.3.11.2. Product Overview 15.3.11.3. Key Financials 15.3.11.4. Key Developments 15.3.11.5. SWOT Analysis 15.3.12. Temenos AG 15.3.12.1. Company Overview 15.3.12.2. Product Overview 15.3.12.3. Key Financials 15.3.12.4. Key Developments 15.3.12.5. SWOT Analysis 15.3.13. PEGASYSTEMS Inc. 15.3.13.1. Company Overview 15.3.13.2. Product Overview 15.3.13.3. Key Financials 15.3.13.4. Key Developments 15.3.13.5. SWOT Analysis 15.3.14. ComplyAdvantage 15.3.14.1. Company Overview 15.3.14.2. Product Overview 15.3.14.3. Key Financials 15.3.14.4. Key Developments 15.3.14.5. SWOT Analysis 15.3.15. NICE Actimize 15.3.15.1. Company Overview 15.3.15.2. Product Overview 15.3.15.3. Key Financials 15.3.15.4. Key Developments 15.3.15.5. SWOT Analysis 15.3.16. Other Prominent Players 16. Assumptions and Acronyms Used 17. Research Methodology
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List Of Table
Table 1: Global Market Value (US$ Mn) by Use Case, 2018-2022
Table 2: Global Market Value (US$ Mn) by Use Case, 2023-2033
Table 3: Global Market Value (US$ Mn) by End-User, 2018-2022
Table 4: Global Market Value (US$ Mn) by End-User, 2023-2033
Table 5: Global Market Value (US$ Mn) by Region, 2018-2022
Table 6: Global Market Value (US$ Mn) by Region, 2023-2033
Table 7: North America Market Value (US$ Mn) by Use Case, 2018-2022
Table 8: North America Market Value (US$ Mn) by Use Case, 2023-2033
Table 9: North America Market Value (US$ Mn) by End-User, 2018-2022
Table 10: North America Market Value (US$ Mn) by End-User, 2023-2033
Table 11: North America Market Value (US$ Mn) by Country, 2018-2022
Table 12: North America Market Value (US$ Mn) by Country, 2023-2033
Table 13: Latin America Market Value (US$ Mn) by Use Case, 2018-2022
Table 14: Latin America Market Value (US$ Mn) by Use Case, 2023-2033
Table 15: Latin America Market Value (US$ Mn) by End-User, 2018-2022
Table 16: Latin America Market Value (US$ Mn) by End-User, 2023-2033
Table 17: Latin America Market Value (US$ Mn) by Country, 2018-2022
Table 18: Latin America Market Value (US$ Mn) by Country, 2023-2033
Table 19: Europe Market Value (US$ Mn) by Use Case, 2018-2022
Table 20: Europe Market Value (US$ Mn) by Use Case, 2023-2033
Table 21: Europe Market Value (US$ Mn) by End-User, 2018-2022
Table 22: Europe Market Value (US$ Mn) by End-User, 2023-2033
Table 23: Europe Market Value (US$ Mn) by Country, 2018-2022
Table 24: Europe Market Value (US$ Mn) by Country, 2023-2033
Table 25: East Asia Market Value (US$ Mn) by Use Case, 2018-2022
Table 26: East Asia Market Value (US$ Mn) by Use Case, 2023-2033
Table 27: East Asia Market Value (US$ Mn) by End-User, 2018-2022
Table 28: East Asia Market Value (US$ Mn) by End-User, 2023-2033
Table 29: East Asia Market Value (US$ Mn) by Country, 2018-2022
Table 30: East Asia Market Value (US$ Mn) by Country, 2023-2033
Table 31: South Asia & Oceania Market Value (US$ Mn) by Use Case, 2018-2022
Table 32: South Asia & Oceania Market Value (US$ Mn) by Use Case, 2023-2033
Table 33: South Asia & Oceania Market Value (US$ Mn) by End-User, 2018-2022
Table 34: South Asia & Oceania Market Value (US$ Mn) by End-User, 2023-2033
Table 35: South Asia & Oceania Market Value (US$ Mn) by Country, 2018-2022
Table 36: South Asia & Oceania Market Value (US$ Mn) by Country, 2023-2033
Table 37: MEA Market Value (US$ Mn) by Use Case, 2018-2022
Table 38: MEA Market Value (US$ Mn) by Use Case, 2023-2033
Table 39: MEA Market Value (US$ Mn) by End-User, 2018-2022
Table 40: MEA Market Value (US$ Mn) by End-User, 2023-2033
Table 41: MEA Market Value (US$ Mn) by Country, 2018-2022
Table 42: MEA Market Value (US$ Mn) by Country, 2023-2033
Table 43: United States Market Value (US$ Mn) Analysis and Forecast, 2018-2022
Table 44: United States Market Value (US$ Mn) Analysis and Forecast, 2023-2033
Table 45: Brazil Market Value (US$ Mn) Analysis and Forecast, 2018-2022
Table 46: Brazil Market Value (US$ Mn) Analysis and Forecast, 2023-2033
Table 47: Russia Market Value (US$ Mn) Analysis and Forecast, 2018-2022
Table 48: Russia Market Value (US$ Mn) Analysis and Forecast, 2023-2033
Table 49: Germany Market Value (US$ Mn) Analysis and Forecast, 2018-2022
Table 50: Germany Market Value (US$ Mn) Analysis and Forecast, 2023-2033
Table 51: Japan Market Value (US$ Mn) Analysis and Forecast, 2018-2022
Table 52: Japan Market Value (US$ Mn) Analysis and Forecast, 2023-2033
Table 53: China Market Value (US$ Mn) Analysis and Forecast, 2018-2022
Table 54: China Market Value (US$ Mn) Analysis and Forecast, 2023-2033
Table 55: India Market Value (US$ Mn) Analysis and Forecast, 2018-2022
Table 56: India Market Value (US$ Mn) Analysis and Forecast, 2023-2033
Table 57: ANZ Market Value (US$ Mn) Analysis and Forecast, 2018-2022
Table 58: ANZ Market Value (US$ Mn) Analysis and Forecast, 2023-2033
Table 59: South Africa Market Value (US$ Mn) Analysis and Forecast, 2018-2022
Table 60: South Africa Market Value (US$ Mn) Analysis and Forecast, 2023-2033
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List Of Figures
Figure 1: Global Market Value (US$ Mn) Analysis, 2018, 2023 and 2033
Figure 2: Global Market Value (US$ Mn) Scenario Forecast, 2023 & 2033
Figure 3: Global Market Value (US$ Mn), Forecast and Analysis, 2018-2022
Figure 4: Global Market Value (US$ Mn), Forecast and Analysis, 2023-2033
Figure 5: Global Market Value Y-o-Y Growth and Forecast, 2018-2033
Figure 6: Global Market Incremental $ Opportunity, 2022-2033
Figure 7: Global Market Share Analysis By Use Case– 2023 & 2033
Figure 8: Global Market Y-o-Y Growth Projections by Use Case, 2022-2033
Figure 9: Global Market Attractiveness Analysis By Use Case, 2023-2033
Figure 10: Global Market Value (US$ Mn), Forecast by Transaction Monitoring, 2018-2033
Figure 11: Global Market Incremental $ Opportunity Transaction Monitoring, 2022-2033
Figure 12: Global Market Value (US$ Mn), Forecast by KYC (Know Your Customer) , 2018-2033
Figure 13: Global Market Incremental $ Opportunity KYC (Know Your Customer) , 2022-2033
Figure 14: Global Market Value (US$ Mn), Forecast by Fraud, Risk & Compliance, 2018-2033
Figure 15: Global Market Incremental $ Opportunity Fraud, Risk & Compliance, 2022-2033
Figure 16: Global Market Value (US$ Mn), Forecast by Crime Pattern Detection, 2018-2033
Figure 17: Global Market Incremental $ Opportunity Crime Pattern Detection, 2022-2033
Figure 18: Global Market Value (US$ Mn), Forecast by Risk Scoring Customers and Accounts, 2018-2033
Figure 19: Global Market Incremental $ Opportunity Risk Scoring Customers and Accounts, 2022-2033
Figure 20: Global Market Value (US$ Mn), Forecast by Watch-List Screening, 2018-2033
Figure 21: Global Market Incremental $ Opportunity Watch-List Screening, 2022-2033
Figure 22: Global Market Value (US$ Mn), Forecast by Alert Management and Reporting, 2018-2033
Figure 23: Global Market Incremental $ Opportunity Alert Management and Reporting, 2022-2033
Figure 24: Global Market Value (US$ Mn), Forecast by Other Solutions, 2018-2033
Figure 25: Global Market Incremental $ Opportunity Other Solutions, 2022-2033
Figure 26: Global Market Share Analysis by End-User
Figure 27: Global Market Y-o-Y Growth Projections by End-User, 2022-2033
Figure 28: Global Market Attractiveness Analysis by End-User, 2023-2033
Figure 29: Global Market Value (US$ Mn), Forecast by Banks, 2018-2033
Figure 30: Global Market Incremental $ Opportunity by Bank, 2022-2033
Figure 31: Global Market Value (US$ Mn), Forecast by Insurance Company, 2018-2033
Figure 32: Global Market Incremental $ Opportunity by Insurance Company, 2022-2033
Figure 33: Global Market Value (US$ Mn), Forecast by Money Services Business, 2018-2033
Figure 34: Global Market Incremental $ Opportunity by Money Services Business, 2022-2033
Figure 35: Global Market Value (US$ Mn), Forecast by Securities, 2018-2033
Figure 36: Global Market Incremental $ Opportunity by Securities, 2022-2033
Figure 37: Global Market Share Analysis by Region– 2023 & 2033
Figure 38: Global Market Y-o-Y Growth Projections by Region, 2022-2033
Figure 39: Global Market Attractiveness Analysis by Region, 2023-2033
Figure 40: North America Market Value (US$ Mn), Forecast and Analysis, 2018-2022
Figure 41: North America Market Value (US$ Mn), Forecast and Analysis, 2022-2033
Figure 42: North America Market Value Y-o-Y Growth and Forecast, 2018-2033
Figure 43: North America Market Incremental $ Opportunity, 2022-2033
Figure 44: North America Market Share Forecast and BPS Change by Use Case, 2023 and 2033
Figure 45: North America Market Share Forecast and BPS Change by End-User, 2023 and 2033
Figure 46: North America Market Share, Forecast and BPS Change by Country, 2023 and 2033
Figure 47: North America Market Y-o-Y Growth Comparison by Country, 2023-2033
Figure 48: North America Market Attractiveness Analysis by Country, 2023-2033
Figure 49: Latin America Market Value (US$ Mn), Forecast and Analysis, 2018-2022
Figure 50: Latin America Market Value (US$ Mn), Forecast and Analysis, 2022-2033
Figure 51: Latin America Market Value Y-o-Y Growth and Forecast, 2018-2033
Figure 52: Latin America Market Incremental $ Opportunity, 2022-2033
Figure 53: Latin America Market Share Forecast and BPS Change by Use Case, 2023 and 2033
Figure 54: Latin America Market Share Forecast and BPS Change by End-User Type, 2023 and 2033
Figure 55: Latin America Market Share, Forecast and BPS Change by Country, 2023 and 2033
Figure 56: Latin America Market Y-o-Y Growth Comparison by Country, 2018-2033
Figure 57: Latin America Market Attractiveness Analysis by Country, 2023-2033
Figure 58: Europe Market Value (US$ Mn), Forecast and Analysis, 2018-2022
Figure 59: Europe Market Value (US$ Mn), Forecast and Analysis, 2023-2033
Figure 60: Europe Market Value Y-o-Y Growth and Forecast, 2018-2033
Figure 61: Europe Market Incremental $ Opportunity, 2022-2033
Figure 62: Europe Market Share Forecast and BPS Change by Use Case, 2023 and 2033
Figure 63: Europe Market Share Forecast and BPS Change by End-User, 2023 and 2033
Figure 64: Europe Market Share, Forecast and BPS Change by Country, 2023 and 2033
Figure 65: Europe Market Y-o-Y Growth Comparison by Country, 2018-2033
Figure 66: Europe Market Attractiveness Analysis by Country, 2023 and 2033
Figure 67: East Asia Market Value (US$ Mn), Forecast and Analysis, 2018-2022
Figure 68: East Asia Market Value (US$ Mn), Forecast and Analysis, 2023-2033
Figure 69: East Asia Market Value Y-o-Y Growth and Forecast, 2018-2033
Figure 70: East Asia Market Incremental $ Opportunity, 2019-2033
Figure 71: East Asia Market Share Forecast and BPS Change by Use Case, 2023 and 2033
Figure 72: East Asia Market Share Forecast and BPS Change by End-User, 2023 and 2033
Figure 73: East Asia Market Share, Forecast and BPS Change by Country, 2023 and 2033
Figure 74: East Asia Market Y-o-Y Growth Comparison by Country, 2018-2033
Figure 75: East Asia Market Attractiveness Analysis by Country, 2023- 2033
Figure 76: South Asia & Oceania Market Value (US$ Mn), Forecast and Analysis, 2018-2022
Figure 77: South Asia & Oceania Market Value (US$ Mn), Forecast and Analysis, 2022-2033
Figure 78: South Asia & Oceania Market Value Y-o-Y Growth and Forecast, 2018-2033
Figure 79: South Asia & Oceania Market Incremental $ Opportunity, 2022-2033
Figure 80: South Asia & Oceania Market Share Forecast and BPS Change by Use Case, 2023 and 2033
Figure 81: South Asia & Oceania Market Share Forecast and BPS Change by End-User, 2023 and 2033
Figure 82: South Asia & Oceania Market Share, Forecast and BPS Change by Country, 2023 and 2033
Figure 83: South Asia & Oceania Market Y-o-Y Growth Comparison by Country, 2018-2033
Figure 84: South Asia & Oceania Market Attractiveness Analysis by Country, 2023-2033
Figure 85: MEA Market Value (US$ Mn), Forecast and Analysis, 2018-2022
Figure 86: MEA Market Value (US$ Mn), Forecast and Analysis, 2022-2033
Figure 87: MEA Market Value Y-o-Y Growth and Forecast, 2018-2033
Figure 88: MEA Market Incremental $ Opportunity, 2022-2033
Figure 89: MEA Market Share Forecast and BPS Change by Use Case, 2023 and 2033
Figure 90: MEA Market Share Forecast and BPS Change by End-User, 2023 and 2033
Figure 91: MEA Market Share, Forecast and BPS Change by Country, 2023 and 2033
Figure 92: MEA Market Y-o-Y Growth Comparison by Country, 2018-2033
Figure 93: MEA Market Attractiveness Analysis by Country, 2023-2033
Figure 94: United States Market Value Share, by Use Case, (2023E and 2033F)
Figure 95: United States Market Value Share, By End-User (2023E and 2033F)
Figure 96: Brazil Market Value Share, by Use Case (2023E and 2033F)
Figure 97: Brazil Market Value Share, By End-User (2023E and 2033F)
Figure 98: Russia Market Value Share, by Use Case (2023E and 2033F)
Figure 99: Russia Market Value Share, By End-User (2023E and 2033F)
Figure 100: Germany Market Value Share, by Use Case (2023E and 2033F)
Figure 101: Germany Market Value Share, By End-User (2023E and 2033F)
Figure 102: China Market Value Share, by Use Case (2023E and 2033F)
Figure 103: China Market Value Share, By End-User (2023E and 2033F)
Figure 104: India Market Value Share, by Use Case (2023E and 2033F)
Figure 105: India Market Value Share, By End-User (2023E and 2033F)
Figure 106: ANZ Market Value Share, by Use Case (2023E and 2033F)
Figure 107: ANZ Market Value Share, By End-User (2023E and 2033F)
Figure 108: Turkey Market Value Share, by Use Case (2023E and 2033F)
Figure 109: Turkey Market Value Share, By End-User (2023E and 2033F)
Know thy Competitors
Competitive landscape highlights only certain players
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- FAQs -
What is the current size of the AI-based anti-money laundering solutions market?
The AI-based anti-money laundering solutions market is valued at US$ 1.94 billion in 2023.
What is the predicted valuation of the market for 2033?
By 2033, the market for AI-based AML solutions is projected to reach US$ 8.49 billion.
What is the estimated growth rate of the market for the decade?
Demand for AI-based AML solutions is predicted to increase at a CAGR of 15.9% from 2023 to 2033.
Which country is anticipated to exhibit significant market expansion?
The German market is predicted to expand at a CAGR of 16.6% through 2033.
At what rate is the Chinese market predicted to expand?
The market in China is forecasted to advance at a CAGR of 16.9% from 2023 to 2033.
AI-Based Anti-Money Laundering (AML) Solutions Market