The Worldwide Anti-money Laundering Market Industry is Expected to Reach $5.8 Billion by 2027

Dublin, July 20, 2022 (GLOBE NEWSWIRE) -- The "Global Anti-money Laundering Market (AML) by Component, Solution (KYC/CDD & Sanctions Screening, Transaction Monitoring, and Case Management & Reporting), Organization Size, Deployment Mode (On-Premises, Cloud), End User & Region - Forecast to 2027" report has been added to's offering.

The global AML market size is expected to grow from an estimated value of USD 2.8 billion in 2022 to USD 5.8 billion by 2027, at a Compound Annual Growth Rate (CAGR) of 15.9% from 2022 to 2027.

Some of the factors that are driving the market growth includes monetary penalties, regulatory sanctions, and reputational loss due to non-compliance with regulations, growing focus towards digital payments, and necessity to create a 360-degree view of data in the financial landscape. However, lack of skilled AML professionals with in-depth knowledge and lack of awareness related to government regulations and deployment of AML solutions are expected to hinder the market growth.

By Solutions, Insurance to grow at the highest CAGR during the forecasted period

Insurance includes life insurance, health insurance, travel insurance, corporate insurance, and vehicle insurance. Insurance firms offer flexible policies and investment products and services. These products and services allow customers to deposit and withdraw a large amount of money in terms of cash.

These flexible offerings and the massive flow of funds in the Life Insurance business has made this industry vulnerable to money laundering and other illegal activities in the insurance industry. Authorities have imposed AML regulations on insurance covering the transaction monitoring and sanction screening obligation. In the US, the Bank Secrecy Act (BSA) has imposed regulations related to transaction monitoring on insurance firms to track cash flows.To keep a track of money coming in and out of the systems, government and financial bodies implement guidelines specific to the insurance industry, which are expected to fuel the adoption of AML solutions. Thus the Insurance sector is expected to grow at the highest CAGR during the forecasted period.

By Organization, Large enterprises to account for a larger market size during the forecasted period

By organization size, Enterprises are categorized into Large and Small and medium sized enterprises (SMEs). Enterprises with more than 1,000 employees are considered large enterprises. Large enterprises are the early adopters of AML solutions, as they use many business applications susceptible to fraudulent attacks.

As these enterprises are large, with different types of IT infrastructure, they face the difficult task of effectively managing the security of their applications. Unlike SMEs, large enterprises are well-equipped with technical skills, have higher investment capabilities, and are more exposed to incidents of fraud. This has led to higher awareness levels among large enterprises. Large enterprises are rich with resources and offer a number of options for money transactions to customers worldwide. Due to this, large banks, casinos, and insurance companies are more vulnerable than SMEs, due to which large enterprises account for a larger market share than SMEs.

By Region, North America to grow at highest market size during the forecasted period

Due to the presence of major vendors of AML solutions and services in the North America region and continuous innovations such as the implementation of AI and ML in AML solutions, North America is expected to hold the largest market size during the forecast period. There are two authorities present in the US: BSA and USA Patriot. These authorities create regulations and policies to combat financial crimes in the country. Such regulatory norms are the major foundations for dependence on AML solutions, which in turn, promote market growth.

Also, the increasing technological advancements and rising money laundering cases fuel the AML market in Canada. AML vendors in this country adopt advanced AI- and ML-based AML solutions to defend their systems against suspicious transactions. Hence, organizations in Canada are taking modern measures against money laundering and applying a risk-based approach to combat financial crimes, thus increasing the market growth.

Key Topics Covered:

1 Introduction

2 Research Methodology

3 Executive Summary

4 Premium Insights
4.1 Attractive Opportunities in the Anti-Money Laundering Market
4.2 Market, by Component
4.3 Anti-Money Laundering Solutions Market, by Type
4.4 Market, by Organization Size
4.5 Anti-Money Laundering Market Share of the Top Three End-users and Regions
4.6 Market Investment Scenario

5 Market Overview and Industry Trends
5.1 Introduction
5.2 Market Dynamics
5.2.1 Drivers Increased Monetary Penalties, Regulatory Sanctions, and Reputational Loss due to Non-Compliance with Regulations Growth in Focus Toward Digital Payments and Internet Banking Necessity to Create an Infrastructure with a 360-Degree View of Data in the Financial Landscape
5.2.2 Restraints Increased Technological Complexities and Sophistication of Attacks Budgetary Issues in Developing In-House Fraud Detection Solutions
5.2.3 Opportunities Higher Adoption of Advanced Analytics in Aml Integration of Ai, Ml, and Big Data Technologies in Developing Aml Solutions Adoption of Cloud-Based Aml Solutions to Combat Financial Crimes
5.2.4 Challenges Lack of Skilled Aml Professionals with In-Depth Knowledge Lack of Awareness Related to Government Regulations and Deployment of Aml Solutions
5.3 Use Cases
5.3.1 Use Case 1: Fico Helped 4Finance Increase Efficiency while Maintaining Regulatory Compliance
5.3.2 Use Case 2: Nice Actimize Helped a European Bank Improve Insight into Global Operations
5.3.3 Use Case 3: Sas Institute Helped a Bangkok Bank Stay Ahead of Emerging Risks and Changing Regulations
5.4 Tariff and Regulatory Landscape
5.4.1 Introduction
5.4.2 Financial Industry Regulatory Authority
5.4.3 Financial Action Task Force (Fatf)
5.4.4 International Monetary Fund
5.4.5 General Data Protection Regulation
5.4.6 Gramm-Leach-Bliley Act
5.4.7 Sarbanes-Oxley Act
5.4.8 Regulatory Bodies, Government Agencies, and Other Organizations
5.5 Ecosystem
5.6 Technology Analysis
5.6.1 Anti-Money Laundering and Ai/Ml
5.6.2 Anti-Money Laundering and Data Analytics
5.6.3 Anti-Money Laundering and the Internet of Things
5.6.4 Anti-Money Laundering and Real-Time Authentication
5.7 Patent Analysis
5.8 Value Chain
5.9 Average Pricing Analysis
5.10 Trends and Disruptions Impacting Customers
5.11 Porter's Five Forces Analysis
5.12 Key Stakeholders and Buying Criteria
5.13 Key Conferences & Events

6 Anti-Money Laundering Market, by Component
6.1 Introduction
6.1.1 Components: Market Drivers
6.2 Solutions
6.2.1 Solutions: Market Drivers
6.2.2 Know Your Customer/Customer due Diligence & Sanctions Screening
6.2.3 Transaction Monitoring
6.2.4 Case Management & Reporting
6.3 Services
6.3.1 Services: Market Drivers
6.3.2 Professional Services Consulting Services Training & Education Integration & Maintenance
6.3.3 Managed Services

7 Anti-Money Laundering Market, by Deployment Mode
7.1 Introduction
7.1.1 Deployment Mode: Market Drivers
7.2 Cloud
7.3 On-Premises

8 Anti-Money Laundering Market, by Organization Size
8.1 Introduction
8.1.1 Organization Size: Market Drivers
8.2 Small and Medium-Sized Enterprises
8.3 Large Enterprises

9 Anti-Money Laundering Market, by End-user
9.1 Introduction
9.1.1 End-user: Market Drivers
9.2 Banking & Financial Institutes
9.3 Insurance
9.4 Gaming & Gambling

10 Anti-Money Laundering Market, by Region

11 Competitive Landscape
11.1 Overview
11.2 Key Player Strategies
11.3 Revenue Analysis
11.4 Market Share Analysis
11.5 Ranking of Key Players in the Market
11.6 Company Evaluation Quadrant
11.6.1 Overview
11.6.2 Product Footprint
11.7 Company Evaluation Quadrant
11.7.1 Stars
11.7.2 Emerging Leaders
11.7.3 Pervasive Players
11.7.4 Participants
11.8 Startup/Sme Evaluation Quadrant
11.8.1 Progressive Companies
11.8.2 Responsive Companies
11.8.3 Dynamic Companies
11.8.4 Starting Blocks
11.8.5 Competitive Benchmarking for Startups/Smes
11.9 Competitive Scenario and Trends

12 Company Profiles
12.1 Introduction
12.2 Key Vendors
12.2.1 Nice Actimize
12.2.2 Sas Institute Inc.
12.2.3 Bae Systems
12.2.4 Gb Group plc.
12.2.5 Fico
12.2.6 Aci Worldwide
12.2.7 Oracle
12.2.8 Experian
12.2.9 Fiserv
12.2.10 Lexisnexis
12.2.11 Fis
12.2.12 Nelito Systems
12.2.13 Wolters Kluwer Financial Services, Inc
12.2.14 Comarch Sa (Comarch)
12.2.15 Allsec Technologies Ltd.
12.2.16 Dixtior
12.2.17 Temenos
12.2.18 Tcs
12.2.19 Complyadvantage
12.2.20 Featurespace
12.2.21 Feedzai
12.2.22 Napier Ai
12.2.23 Tier1 Financial Solutions
12.2.24 Finacus Solutions Pvt Ltd
12.2.25 Jumio
12.2.26 Friss
12.2.27 Transunion
12.3 Other Vendors
12.3.1 Idmerit
12.3.2 Imtf
12.3.3 Innovative Systems
12.3.4 Sedicii
12.3.5 Trulioo
12.3.6 Namescan
12.3.7 Datavisor Inc
12.3.8 Gurucul

13 Appendix

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