The anti-money laundering market size is projected to be valued at US$ 3.18 billion in 2023 and is expected to rise to US$ 16.37 billion by 2033. The sales of anti-money laundering (AML) are projected to expand at a significant CAGR of 17.8% during the forecast period.
One of the main factors influencing the adoption of anti-money laundering solutions across organizations is the rise in money laundering cases around the world. For instance, the United Nations Office on Drugs and Crime (UNODC) estimates that between 2 to 5 % of the world's gross domestic product (GDP) has been laundered globally.
Anti-money laundering strategies are actively being used by financial institutions and governments worldwide to combat financial crimes.
Market acceptance has been aided by the development of powerful anti-money laundering software that can precisely trace suspicious transactions and identify any possible crimes.
In addition, technological developments made by numerous international Fin-tech firms to improve financial security and monitoring have increased product demand in recent years.
The use of electronic wallets, or eWallets, is growing, and this change has increased the chance of illegal financial activities. Banks have been warned by the FATF about unauthorized financial transactions.
Solutions for AML transaction monitoring have features for customer profiling, sanctions screening, and blacklist screening. To counteract these trends, banks have made significant investments in personnel, manual checks, and point-in-time system development.
The most difficult part of creating an effective AML program is the timely detection of the laundering operations since money launderers keep coming up with new ways to use banks for illegal purposes. Many businesses are introducing cutting-edge technologies that can identify, monitor, and stop money laundering.
On the other side, artificial intelligence (AI) can assist businesses in resolving a range of problems brought on by the growth in digitalization.
In situations involving anti-money laundering, it can lessen the need for human intervention. Even though AI can never fully replace people, it can greatly minimize the need for human authorization.
| Attribute | Details |
|---|---|
| Anti-money Laundering (AML) Market Estimated Size (2023) | US$ 3.18 billion |
| Anti-money Laundering (AML) Market CAGR (2023 to 2033) | 17.8% |
| Anti-money Laundering (AML) Market Forecasted Size (2033) | US$ 16.37 billion |
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The growing use of big data analytics by businesses enables them to conduct analyses that can identify trends and draw important conclusions from big data sets. It is predicted that using big data analytics may increase demand for anti-money laundering software. For instance, Coinfirm and Sanction Scanner collaborated in November 2022 to offer compliance expertise and benefits in the blockchain with traditional finance ecosystems. Through this agreement, clients are likely to be able to track and inspect transactions handled by their businesses for both fiat currency and cryptocurrency.
Big Data analytics is the best tool for AML compliance because it can be tailored to enhance and automate numerous AML compliance processes. Big Data analytics also aids in risk reduction, fraud pattern detection, and the detection of data quality concerns that result in false positives.
Financial organizations can study the details of existing regulatory laws with the help of big data analytics tools. It aids in the analysis of client onboarding, transactional data, and the completion of regulatory authority obligations. Real-time risk or problem detection also aids in avoiding potential rule violations in front of supervisory authorities.
Demand for anti-money laundering (AML) is anticipated to rise at a phenomenal CAGR of 17.8% from 2023 to 2033.
Growing Emphasis on Real-time Monitoring and Regulatory Technology is Providing the Market with Lucrative Prospects
To obtain a comprehensive overview of a customer's activities in real-time, real-time transaction monitoring examines client transactions and behavior by looking at both past and present customer data and interactions. Organizations can identify financial crimes early on or before they happen, thanks to the transaction monitoring feature of anti-money laundering software. Financial institutions can identify possible instances of financial crime and take action to intervene and stop it from happening at that same moment by implementing real-time transaction monitoring in anti-money laundering.
RegTech (Regulatory Technology) provides banks and other financial institutions with access to sophisticated tools and procedures that automate data gathering, processing, and visualization. RegTech helps banks onboard consumers quickly, efficiently, and with less engagement from the customer. In the upcoming years, there is likely to be a boom in demand for anti-money laundering software due to the advent of AI RegTech solutions in the financial sector.
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Several financial institutions frequently utilize AML solutions. There are a few external forces that have an impact on financial services. In order to identify the most significant issues relating to client identity or transactions, the solutions mix AI, machine learning, and big data. This enables authorities to take the necessary action. Several sizable corporations provide banking institutions with AML solutions. Through the analysis of process modifications and the identification of any enhancements that may be required, these solutions are made to solve transaction monitoring, compliance management, and other issues to maintain smooth financial operations. During the forecast period, these variables are expected to assist the segment's revenue growth.
BFSI companies are among the most frequent users of AML solutions, which use affordable ML and AI methods to combat illegal activity. The risk of fraudulent financial operations has increased as a result of digitalization, automation, and online transactions. In 2020, there were 1.9 billion users of internet banking services globally. Additionally, it is anticipated that during the projection period, demand for AML solutions and services is estimated to increase due to the growing requirement to detect and prevent illegal activities. Additionally, throughout the forecast period, revenue growth in the market is anticipated to be fueled by increased governance and internal control to comply with domestic and international statutory AML rules.
As a result of numerous institutions in the region, anti-money laundering solutions are also anticipated to see considerable adoption in the future years. Additionally, it is projected that inorganic tactics to incorporate artificial intelligence among anti-money laundering providers may evolve, which is likely to accelerate the growth of the anti-money laundering sector in the area. Specific anti-money laundering regulations set by the United States Congress under the Anti-Money Laundering Act of 2020 stimulate national preference for anti-money laundering, which is anticipated to boost regional market growth.
As a result of numerous institutions in the region, anti-money laundering solutions are also anticipated to see considerable adoption in the future years. Additionally, it is projected that inorganic tactics to incorporate artificial intelligence among anti-money laundering providers may evolve, which is likely to accelerate the growth of the anti-money laundering sector in the area. Specific anti-money laundering regulations set by the United States Congress under the Anti-money Laundering Act of 2020 stimulate national preference for anti-money laundering, which is anticipated to boost the regional market growth.
Asia Pacific market's prominent revenue growth rate is linked to the region's expanding adoption of digital payment platforms in nations like China and India. The industry is anticipated to grow in revenue as a result of increased government support and regulations, such as those set forth by the intergovernmental Financial Action Task Force (FATF) to tighten anti-money laundering procedures. For instance, the Prevention of Money Laundering Act, 2002 (PMLA) in India forbids money laundering and is anticipated to spur development in market income in the near future.
To accelerate market growth, businesses in the region are significantly incorporating technologies like AI, ML, and automation in their AML solutions. For instance, in March 2020, 3i Infotech Limited unveiled AMLOCK Analytics, a cutting-edge AML system outfitted with machine language and artificial intelligence (AI) (ML). AMLOCK Analytics creates analyses and forecasts based on institution-specific historical data using a variety of statistical techniques and machine learning algorithms.
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Due to the presence of numerous solution providers worldwide, including SAS Institute, NICE Ltd., Experian, BAE Systems, FICO, Refinitiv, and many others, the anti-money laundering (AML) market's competitive environment is moderately fragmented. Additionally, several small and medium-sized businesses are entering the market and generating money, which is anticipated to support them in developing novel solutions. To increase their market position, the current market participants are also developing strategic alliances and collaborations.
| Attribute | Details |
|---|---|
| Growth Rate | CAGR of 17.8% from 2023 to 2033 |
| Base Year of Estimation | 2022 |
| Historical Data | 2017 to 2022 |
| Forecast Period | 2023 to 2033 |
| Quantitative Units | Revenue in USD billion and Volume in Units and F-CAGR from 2023 to 2033 |
| Report Coverage | Revenue Forecast, Volume Forecast, Company Ranking, Competitive Landscape, growth factors, Trends, and Pricing Analysis |
| Key Segments Covered | Component, Deployment Model, Organization Size, Application, Region |
| Regions Covered | North America; Latin America; Europe; East Asia; South Asia; The Middle East & Africa; Oceania |
| Key Countries Profiled | Unite States, Canada, Brazil, Mexico, Germany, Italy, France, United Kingdom, Spain, Russia, China, Japan, India, GCC Countries, Australia |
| Key Companies Profiled | Oracle (The United States); ACI Worldwide (The United States); Eastnets (The United States); AML Partners. (The United States); Alessa (The United States); Acuant, Inc. (The United States); Feedzai (Portugal); SAS Institute Inc. (The United States); Ondato (The United Kingdom) Sanction Scanner (The United Kingdom) |
| Customization & Pricing | Available upon Request |
Adoption of anti-money laundering solutions across organizations drives sales.
Europe is anticipated to be the market with the highest potential.
The market is forecast to register a CAGR of 17.8% through 2033.
The market generated a revenue of US$ 3.18 billion in 2023.
Growing emphasis on real-time monitoring provides a key opportunity.
1. Executive Summary | Anti-money Laundering Market
1.1. Global Market Outlook
1.2. Demand-side Trends
1.3. Supply-side Trends
1.4. Technology Roadmap Analysis
1.5. Analysis and Recommendations
2. Market Overview
2.1. Market Coverage / Taxonomy
2.2. Market Definition / Scope / Limitations
3. Market Background
3.1. Market Dynamics
3.1.1. Drivers
3.1.2. Restraints
3.1.3. Opportunity
3.1.4. Trends
3.2. Scenario Forecast
3.2.1. Demand in Optimistic Scenario
3.2.2. Demand in Likely Scenario
3.2.3. Demand in Conservative Scenario
3.3. Opportunity Map Analysis
3.4. Investment Feasibility Matrix
3.5. PESTLE and Porter’s Analysis
3.6. Regulatory Landscape
3.6.1. By Key Regions
3.6.2. By Key Countries
3.7. Regional Parent Market Outlook
4. Global Market Analysis 2018 to 2022 and Forecast, 2023 to 2033
4.1. Historical Market Size Value (US$ Million) Analysis, 2018 to 2022
4.2. Current and Future Market Size Value (US$ Million) Projections, 2023 to 2033
4.2.1. Y-o-Y Growth Trend Analysis
4.2.2. Absolute $ Opportunity Analysis
5. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Component
5.1. Introduction / Key Findings
5.2. Historical Market Size Value (US$ Million) Analysis By Component, 2018 to 2022
5.3. Current and Future Market Size Value (US$ Million) Analysis and Forecast By Component, 2023 to 2033
5.3.1. Solutions
5.3.1.1. KYC/CDD & Sanction Screening
5.3.1.2. Transaction Monitoring
5.3.1.3. Case Management & Reporting
5.3.2. Services
5.3.2.1. Professional Services
5.3.2.2. Managed Services
5.4. Y-o-Y Growth Trend Analysis By Component, 2018 to 2022
5.5. Absolute $ Opportunity Analysis By Component, 2023 to 2033
6. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Organization Size
6.1. Introduction / Key Findings
6.2. Historical Market Size Value (US$ Million) Analysis By Organization Size, 2018 to 2022
6.3. Current and Future Market Size Value (US$ Million) Analysis and Forecast By Organization Size, 2023 to 2033
6.3.1. Small & Medium Enterprises
6.3.2. Large Enterprises
6.4. Y-o-Y Growth Trend Analysis By Organization Size, 2018 to 2022
6.5. Absolute $ Opportunity Analysis By Organization Size, 2023 to 2033
7. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Deployment Mode
7.1. Introduction / Key Findings
7.2. Historical Market Size Value (US$ Million) Analysis By Deployment Mode , 2018 to 2022
7.3. Current and Future Market Size Value (US$ Million) Analysis and Forecast By Deployment Mode , 2023 to 2033
7.3.1. Cloud
7.3.2. On-Premise
7.4. Y-o-Y Growth Trend Analysis By Deployment Mode , 2018 to 2022
7.5. Absolute $ Opportunity Analysis By Deployment Mode , 2023 to 2033
8. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By End Use
8.1. Introduction / Key Findings
8.2. Historical Market Size Value (US$ Million) Analysis By End Use, 2018 to 2022
8.3. Current and Future Market Size Value (US$ Million) Analysis and Forecast By End Use, 2023 to 2033
8.3.1. Banks & Financial Institutions
8.3.2. Insurance Providers
8.3.3. Gaming & Gambling
8.4. Y-o-Y Growth Trend Analysis By End Use, 2018 to 2022
8.5. Absolute $ Opportunity Analysis By End Use, 2023 to 2033
9. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Region
9.1. Introduction
9.2. Historical Market Size Value (US$ Million) Analysis By Region, 2018 to 2022
9.3. Current Market Size Value (US$ Million) Analysis and Forecast By Region, 2023 to 2033
9.3.1. North America
9.3.2. Latin America
9.3.3. Europe
9.3.4. South Asia
9.3.5. East Asia
9.3.6. Oceania
9.3.7. MEA
9.4. Market Attractiveness Analysis By Region
10. North America Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country
10.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022
10.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033
10.2.1. By Country
10.2.1.1. USA
10.2.1.2. Canada
10.2.2. By Component
10.2.3. By Organization Size
10.2.4. By Deployment Mode
10.2.5. By End Use
10.3. Market Attractiveness Analysis
10.3.1. By Country
10.3.2. By Component
10.3.3. By Organization Size
10.3.4. By Deployment Mode
10.3.5. By End Use
10.4. Key Takeaways
11. Latin America Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country
11.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022
11.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033
11.2.1. By Country
11.2.1.1. Brazil
11.2.1.2. Mexico
11.2.1.3. Rest of Latin America
11.2.2. By Component
11.2.3. By Organization Size
11.2.4. By Deployment Mode
11.2.5. By End Use
11.3. Market Attractiveness Analysis
11.3.1. By Country
11.3.2. By Component
11.3.3. By Organization Size
11.3.4. By Deployment Mode
11.3.5. By End Use
11.4. Key Takeaways
12. Europe Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country
12.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022
12.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033
12.2.1. By Country
12.2.1.1. Germany
12.2.1.2. United kingdom
12.2.1.3. France
12.2.1.4. Spain
12.2.1.5. Italy
12.2.1.6. Rest of Europe
12.2.2. By Component
12.2.3. By Organization Size
12.2.4. By Deployment Mode
12.2.5. By End Use
12.3. Market Attractiveness Analysis
12.3.1. By Country
12.3.2. By Component
12.3.3. By Organization Size
12.3.4. By Deployment Mode
12.3.5. By End Use
12.4. Key Takeaways
13. South Asia Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country
13.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022
13.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033
13.2.1. By Country
13.2.1.1. India
13.2.1.2. Malaysia
13.2.1.3. Singapore
13.2.1.4. Thailand
13.2.1.5. Rest of South Asia
13.2.2. By Component
13.2.3. By Organization Size
13.2.4. By Deployment Mode
13.2.5. By End Use
13.3. Market Attractiveness Analysis
13.3.1. By Country
13.3.2. By Component
13.3.3. By Organization Size
13.3.4. By Deployment Mode
13.3.5. By End Use
13.4. Key Takeaways
14. East Asia Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country
14.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022
14.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033
14.2.1. By Country
14.2.1.1. China
14.2.1.2. Japan
14.2.1.3. South Korea
14.2.2. By Component
14.2.3. By Organization Size
14.2.4. By Deployment Mode
14.2.5. By End Use
14.3. Market Attractiveness Analysis
14.3.1. By Country
14.3.2. By Component
14.3.3. By Organization Size
14.3.4. By Deployment Mode
14.3.5. By End Use
14.4. Key Takeaways
15. Oceania Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country
15.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022
15.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033
15.2.1. By Country
15.2.1.1. Australia
15.2.1.2. New Zealand
15.2.2. By Component
15.2.3. By Organization Size
15.2.4. By Deployment Mode
15.2.5. By End Use
15.3. Market Attractiveness Analysis
15.3.1. By Country
15.3.2. By Component
15.3.3. By Organization Size
15.3.4. By Deployment Mode
15.3.5. By End Use
15.4. Key Takeaways
16. MEA Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country
16.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022
16.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033
16.2.1. By Country
16.2.1.1. GCC Countries
16.2.1.2. South Africa
16.2.1.3. Israel
16.2.1.4. Rest of MEA
16.2.2. By Component
16.2.3. By Organization Size
16.2.4. By Deployment Mode
16.2.5. By End Use
16.3. Market Attractiveness Analysis
16.3.1. By Country
16.3.2. By Component
16.3.3. By Organization Size
16.3.4. By Deployment Mode
16.3.5. By End Use
16.4. Key Takeaways
17. Key Countries Market Analysis
17.1. USA
17.1.1. Pricing Analysis
17.1.2. Market Share Analysis, 2022
17.1.2.1. By Component
17.1.2.2. By Organization Size
17.1.2.3. By Deployment Mode
17.1.2.4. By End Use
17.2. Canada
17.2.1. Pricing Analysis
17.2.2. Market Share Analysis, 2022
17.2.2.1. By Component
17.2.2.2. By Organization Size
17.2.2.3. By Deployment Mode
17.2.2.4. By End Use
17.3. Brazil
17.3.1. Pricing Analysis
17.3.2. Market Share Analysis, 2022
17.3.2.1. By Component
17.3.2.2. By Organization Size
17.3.2.3. By Deployment Mode
17.3.2.4. By End Use
17.4. Mexico
17.4.1. Pricing Analysis
17.4.2. Market Share Analysis, 2022
17.4.2.1. By Component
17.4.2.2. By Organization Size
17.4.2.3. By Deployment Mode
17.4.2.4. By End Use
17.5. Germany
17.5.1. Pricing Analysis
17.5.2. Market Share Analysis, 2022
17.5.2.1. By Component
17.5.2.2. By Organization Size
17.5.2.3. By Deployment Mode
17.5.2.4. By End Use
17.6. United kingdom
17.6.1. Pricing Analysis
17.6.2. Market Share Analysis, 2022
17.6.2.1. By Component
17.6.2.2. By Organization Size
17.6.2.3. By Deployment Mode
17.6.2.4. By End Use
17.7. France
17.7.1. Pricing Analysis
17.7.2. Market Share Analysis, 2022
17.7.2.1. By Component
17.7.2.2. By Organization Size
17.7.2.3. By Deployment Mode
17.7.2.4. By End Use
17.8. Spain
17.8.1. Pricing Analysis
17.8.2. Market Share Analysis, 2022
17.8.2.1. By Component
17.8.2.2. By Organization Size
17.8.2.3. By Deployment Mode
17.8.2.4. By End Use
17.9. Italy
17.9.1. Pricing Analysis
17.9.2. Market Share Analysis, 2022
17.9.2.1. By Component
17.9.2.2. By Organization Size
17.9.2.3. By Deployment Mode
17.9.2.4. By End Use
17.10. India
17.10.1. Pricing Analysis
17.10.2. Market Share Analysis, 2022
17.10.2.1. By Component
17.10.2.2. By Organization Size
17.10.2.3. By Deployment Mode
17.10.2.4. By End Use
17.11. Malaysia
17.11.1. Pricing Analysis
17.11.2. Market Share Analysis, 2022
17.11.2.1. By Component
17.11.2.2. By Organization Size
17.11.2.3. By Deployment Mode
17.11.2.4. By End Use
17.12. Singapore
17.12.1. Pricing Analysis
17.12.2. Market Share Analysis, 2022
17.12.2.1. By Component
17.12.2.2. By Organization Size
17.12.2.3. By Deployment Mode
17.12.2.4. By End Use
17.13. Thailand
17.13.1. Pricing Analysis
17.13.2. Market Share Analysis, 2022
17.13.2.1. By Component
17.13.2.2. By Organization Size
17.13.2.3. By Deployment Mode
17.13.2.4. By End Use
17.14. China
17.14.1. Pricing Analysis
17.14.2. Market Share Analysis, 2022
17.14.2.1. By Component
17.14.2.2. By Organization Size
17.14.2.3. By Deployment Mode
17.14.2.4. By End Use
17.15. Japan
17.15.1. Pricing Analysis
17.15.2. Market Share Analysis, 2022
17.15.2.1. By Component
17.15.2.2. By Organization Size
17.15.2.3. By Deployment Mode
17.15.2.4. By End Use
17.16. South Korea
17.16.1. Pricing Analysis
17.16.2. Market Share Analysis, 2022
17.16.2.1. By Component
17.16.2.2. By Organization Size
17.16.2.3. By Deployment Mode
17.16.2.4. By End Use
17.17. Australia
17.17.1. Pricing Analysis
17.17.2. Market Share Analysis, 2022
17.17.2.1. By Component
17.17.2.2. By Organization Size
17.17.2.3. By Deployment Mode
17.17.2.4. By End Use
17.18. New Zealand
17.18.1. Pricing Analysis
17.18.2. Market Share Analysis, 2022
17.18.2.1. By Component
17.18.2.2. By Organization Size
17.18.2.3. By Deployment Mode
17.18.2.4. By End Use
17.19. GCC Countries
17.19.1. Pricing Analysis
17.19.2. Market Share Analysis, 2022
17.19.2.1. By Component
17.19.2.2. By Organization Size
17.19.2.3. By Deployment Mode
17.19.2.4. By End Use
17.20. South Africa
17.20.1. Pricing Analysis
17.20.2. Market Share Analysis, 2022
17.20.2.1. By Component
17.20.2.2. By Organization Size
17.20.2.3. By Deployment Mode
17.20.2.4. By End Use
17.21. Israel
17.21.1. Pricing Analysis
17.21.2. Market Share Analysis, 2022
17.21.2.1. By Component
17.21.2.2. By Organization Size
17.21.2.3. By Deployment Mode
17.21.2.4. By End Use
18. Market Structure Analysis
18.1. Competition Dashboard
18.2. Competition Benchmarking
18.3. Market Share Analysis of Top Players
18.3.1. By Regional
18.3.2. By Component
18.3.3. By Organization Size
18.3.4. By Deployment Mode
18.3.5. By End Use
19. Competition Analysis
19.1. Competition Deep Dive
19.1.1. ACI Worldwide Inc.
19.1.1.1. Overview
19.1.1.2. Product Portfolio
19.1.1.3. Profitability by Market Segments
19.1.1.4. Sales Footprint
19.1.1.5. Strategy Overview
19.1.1.5.1. Marketing Strategy
19.1.2. BAE Systems plc
19.1.2.1. Overview
19.1.2.2. Product Portfolio
19.1.2.3. Profitability by Market Segments
19.1.2.4. Sales Footprint
19.1.2.5. Strategy Overview
19.1.2.5.1. Marketing Strategy
19.1.3. Nice Systems Ltd.
19.1.3.1. Overview
19.1.3.2. Product Portfolio
19.1.3.3. Profitability by Market Segments
19.1.3.4. Sales Footprint
19.1.3.5. Strategy Overview
19.1.3.5.1. Marketing Strategy
19.1.4. Fair Isaac Corporation (FICO)
19.1.4.1. Overview
19.1.4.2. Product Portfolio
19.1.4.3. Profitability by Market Segments
19.1.4.4. Sales Footprint
19.1.4.5. Strategy Overview
19.1.4.5.1. Marketing Strategy
19.1.5. SAS Institute Inc.
19.1.5.1. Overview
19.1.5.2. Product Portfolio
19.1.5.3. Profitability by Market Segments
19.1.5.4. Sales Footprint
19.1.5.5. Strategy Overview
19.1.5.5.1. Marketing Strategy
19.1.6. Fiserv Inc.
19.1.6.1. Overview
19.1.6.2. Product Portfolio
19.1.6.3. Profitability by Market Segments
19.1.6.4. Sales Footprint
19.1.6.5. Strategy Overview
19.1.6.5.1. Marketing Strategy
19.1.7. Dixtior
19.1.7.1. Overview
19.1.7.2. Product Portfolio
19.1.7.3. Profitability by Market Segments
19.1.7.4. Sales Footprint
19.1.7.5. Strategy Overview
19.1.7.5.1. Marketing Strategy
19.1.8. LexisNexis Risk Solutions
19.1.8.1. Overview
19.1.8.2. Product Portfolio
19.1.8.3. Profitability by Market Segments
19.1.8.4. Sales Footprint
19.1.8.5. Strategy Overview
19.1.8.5.1. Marketing Strategy
19.1.9. TransUnion LLC
19.1.9.1. Overview
19.1.9.2. Product Portfolio
19.1.9.3. Profitability by Market Segments
19.1.9.4. Sales Footprint
19.1.9.5. Strategy Overview
19.1.9.5.1. Marketing Strategy
19.1.10. Wolter’s Kluwer Limited
19.1.10.1. Overview
19.1.10.2. Product Portfolio
19.1.10.3. Profitability by Market Segments
19.1.10.4. Sales Footprint
19.1.10.5. Strategy Overview
19.1.10.5.1. Marketing Strategy
19.1.11. Temenos AG
19.1.11.1. Overview
19.1.11.2. Product Portfolio
19.1.11.3. Profitability by Market Segments
19.1.11.4. Sales Footprint
19.1.11.5. Strategy Overview
19.1.11.5.1. Marketing Strategy
19.1.12. Nelito Systems Ltd.
19.1.12.1. Overview
19.1.12.2. Product Portfolio
19.1.12.3. Profitability by Market Segments
19.1.12.4. Sales Footprint
19.1.12.5. Strategy Overview
19.1.12.5.1. Marketing Strategy
19.1.13. Tata Consultancy Services Ltd.
19.1.13.1. Overview
19.1.13.2. Product Portfolio
19.1.13.3. Profitability by Market Segments
19.1.13.4. Sales Footprint
19.1.13.5. Strategy Overview
19.1.13.5.1. Marketing Strategy
19.1.14. Featurespace Limited
19.1.14.1. Overview
19.1.14.2. Product Portfolio
19.1.14.3. Profitability by Market Segments
19.1.14.4. Sales Footprint
19.1.14.5. Strategy Overview
19.1.14.5.1. Marketing Strategy
19.1.15. Feedzai Inc.
19.1.15.1. Overview
19.1.15.2. Product Portfolio
19.1.15.3. Profitability by Market Segments
19.1.15.4. Sales Footprint
19.1.15.5. Strategy Overview
19.1.15.5.1. Marketing Strategy
19.1.16. Finacus Solutions Private Limited
19.1.16.1. Overview
19.1.16.2. Product Portfolio
19.1.16.3. Profitability by Market Segments
19.1.16.4. Sales Footprint
19.1.16.5. Strategy Overview
19.1.16.5.1. Marketing Strategy
19.1.17. CaseWare RCM
19.1.17.1. Overview
19.1.17.2. Product Portfolio
19.1.17.3. Profitability by Market Segments
19.1.17.4. Sales Footprint
19.1.17.5. Strategy Overview
19.1.17.5.1. Marketing Strategy
19.1.18. Comarch SA
19.1.18.1. Overview
19.1.18.2. Product Portfolio
19.1.18.3. Profitability by Market Segments
19.1.18.4. Sales Footprint
19.1.18.5. Strategy Overview
19.1.18.5.1. Marketing Strategy
20. Assumptions & Acronyms Used
21. Research Methodology
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