AI-Based Anti-Money Laundering (AML) Solutions Market

AI-Based Anti-Money Laundering (AML) Solutions Market Outlook

Fact.MR has released its AI-based anti-money laundering (AML) solutions market report, which has revealed the past performance of the industry as well as the future growth of technology enabled anti-money laundering solutions.

Money launderers continues to outsmart existing prevention financial and technological infrastructure, albeit the finance industry worldwide invests over US$ 75 Bn on AML solutions. Over a billion dollars were invested by U.S. and Canadian agencies alone in 2019 to upscale their anti-money laundering tools.

Advanced cognitive technologies integrating AI and machine learning enables the system to automatically detect true money laundering instances. Implemented new systems immix the power of big data analytics and ML in identifying breach designs, and alert systems for similar events, thus preventing money laundering activities.

These AI-based AML tools also generate signals to notice unfamiliar or suspicious transactions from a wide array of data, which is often unstructured or at times structured. This results in sizable minimization in false positives and false negatives, while banks can deploy miniscule number of compliance analysts to inspect the reduced cases before filing SARs.

Fact.MR has estimated that spending by banks and other financial institutions on AI-enabled AML technology will increase rapidly over the coming years.

Key Insights Covered in AI-based AML Solutions Industry Survey:

• Market Estimates and Forecasts (2016-2031)
• Key Drivers and Restraints Shaping Market Growth
• Segment-wise, Country-wise, and Region-wise Analysis
• Competition Mapping and Benchmarking
• Market Share Analysis
• COVID-19 Impact on Demand for AI-based AML Solutions and How to Navigate
• Recommendation on Key Winning Strategies

Revenue Analysis of AI-based AML Solutions from 2016 to 2020 Compared to Demand Outlook for 2021 to 2031

As per the AI-based anti-money laundering solutions industry research by Fact.MR – a market research and competitive intelligence provider, historically, from 2016 to 2020, market value of the industry increased drastically, wherein, countries such as the United States, France, Germany, United Kingdom, and France, India, and China held a significant share in the global market.

To strengthen financial systems against money laundering, terrorist financing, and other financial crimes, anti-money laundering (AML) compliance has been helping financial systems since 1970 following the enactment of the Bank Secretary Act (BSA). AML technology compliance has changed dramatically with the addition of artificial intelligence and regulatory layers in the financial jurisdiction.

Despite stringent regulatory reforms, incidence of money laundering and breaches is increasing, where substantial penalties have been reported. The U.S. Department of the Treasury roughly estimates that annually, around US$ 1.6 trillion of global money is involved in money laundering, representing 2.7% of global GDP.

Extensive revolution in technology has brought transformation in AML compliance services, which, in turn, has aided financial systems to address the challenges of money laundering. While increasing incidence of money laundering, terrorist financing, and other illicit financial transactions represent good opportunities for AI-based AML solution providers, growing layers of regulations and simultaneous technological restraints remain future milestones to be achieved by AML compliance providers.

Constant surge in anti-money laundering activities and the number of transactions has aggravated demand for AI-based AML software and solutions, which has increased the efficiency of companies and banks in the last few years. Artificial intelligence is decreasing the cost of AML compliance by doing all human work by itself.

American banks are spending nearly US$ 23.5 Bn annually, followed by European banks are around US$ 20 Bn, to identify AML transactions and their patterns. This spending by banks has been increased remarkably due to high fines being imposed on them by regulatory authorities; such as, American banks were fined US$ 23.6 Bn and European banks have paid fines of nearly US$ 1.2 Bn.

The aforementioned factors are increasing demand for AI in anti-money laundering solutions, which is expected to boost the global market during the forecasted era of ten years.

Trends

• The anti-money laundering software sales are anticipated to cross USD 1.77 billion by end of the year 2023. It is estimated that 2–5% of the world's GDP, or $800 billion–$2 trillion in current US dollars, is laundered internationally each year. However, it is challenging to assess the overall quantity of money that undergoes the laundering cycle due to the covert nature of the practice. The penetration of hidden ways is a driver for software-based laundering monitoring.

• Through Big Data searches for suspected abnormalities among millions of transactions, both fraud and money laundering can be found. Companies are now responsible for identifying fraudulent transactions. As a result, they have spent billions on highly advanced Big Data approaches. The technology is extensively useful for financial institutions like Banks to analyze their customers and their transactions.

• Businesses can fine-tune transaction monitoring algorithms to detect more suspicious activity and fewer false positives by combining data analytics and machine learning. Additionally, modern case management technologies make reporting and conducting investigations simpler than ever.

• The AML laws are always being improved and expanded by organizations like FinCEN, FATF, and OFAC to help the banking sector remain ahead of criminals. Additionally, not only banks are needed to comply; any company that allows consumers to move money, such as an online marketplace, a cryptocurrency, a fintech, or a gaming platform, must have an efficient AML program in place in order to avoid paying hefty fines.

• The business pays extra attention to avoid being in news for corrupt practices and with the AML solutions in place companies can manage to save their market image by ensuring clean practices.

• Big data analytics helps companies in the reduction of false positives, which occur when a normal consumer is mistakenly identified as a high-risk or fraudulent customer. Up to 90% of the time, the alerts generated by AML programs are false positives, which costs businesses a lot of time and money.

How Complex is AI-based AML Software?

Machine learning is a new technology as compared to other technologies that are not taught what to look for and how to detect tricky financial transactions that are related to financial crime.

AI-based AML software is prepared using supervised and unsupervised machine learning algorithms that are capable to learn by themselves by analyzing the given data without any error and provide almost accurate results. The AI-based anti-money laundering system is a straightforward and easy procedure to follow to implement in a financial organization.

What are the Use Cases of AI-based AML Solutions?

AI is a very vast technology that has tremendous proficiencies. AI is used in many areas of compliance such as risks, operations, etc. AI is one of the most crucial steps in the financial industry, which is being implemented in numerous processes such as transaction monitoring, transaction screening, risk scoring, financial crime pattern detection, etc.

Financial organizations are progressing by implementing artificial intelligence in AML solutions, which is improving the accuracy of detection by decreasing false positive indications. In turn, these improvements are protecting firms from huge fines and continuing to their high reputation in the market.

Which Region is Projected to Offer High Growth Opportunities for AI-based AML Solution Providers?

As reported by the U.S. Department of Treasury, extensive penetration of money laundering prevails, representing involvement of US$ 300 Bn generated by criminal enterprises.

USA has enacted multiple laws and regulations for money laundering at the federal and state level. The USA PATRIOT ACT was enacted after the 2001 terrorist attack, which assisted the already enacted Bank Secretary Act (BSA) to strengthen the AML compliance program. Primary causes for money laundering are attributed to easy accessibility of the financial system and trade-based money laundering.

Canada’s regime for anti-money laundering and anti-terrorist financing activities is controlled by the Financial Transactions and Reports Analysis Center of Canada (FINTRAC), which actively monitors activities of financial institutions, intermediaries, and participating member countries. This regional scenario presents higher implementation of AI-based AML compliance by the financial sector and legally associated other institutions, owing to the stringent regulatory framework.

The U.S AI-based anti-money laundering solutions market is projected to acquire more than 1/4 of the overall market share from 2021 to 2031.

How Will Asia Pacific Contribute to Revenue from AI-based Anti-Money Laundering Solutions?

The Asia Pacific region consists of multiple developing economies that are gradually growing to stable institutional, political, and economic capacities. Such a developing scenario also presents a high level of corruption, where money laundering has the demand and opportunity to flourish.

The region has a dispersed and less penetrated regulatory framework to monitor financial frauds and money laundering activities. According to the Global Fraud Survey 2016, concerning combating corruption, China has seen strong and sustained initiatives on the global stage.

While working for anti-corruption measures, inter-government cooperation, foreign multinationals, and Chinese companies are seeking a more transparent and ethical market over the risk of financial loss in a less transparent market. India has also initiated efforts towards AML following FATF recommendations such as GST, Aadhaar compulsion, and KYC reports that are gradually gathering data for AML compliances.

Increasing financial inclusions, international financial collaborations, growing online financial transactions, and rising number of financial institutions are likely to drive demand for AI-based AML solutions over the next ten years. The Asia Pacific AI-based transaction monitoring market will generate marvellous opportunities, as the market is acquiring the third-largest market share in the global market.

Category-wise Insights

Which Use Case of AI-based AML Solutions Accounts for the Largest Market Share?

Artificial intelligence-enabled antimony laundering software performs numerous tasks such as transaction monitoring, KYC (know your customer), crime pattern detection, risk scoring customers and accounts, and many more, where fraud, risk & compliance is acquiring the largest market share among all use cases.

Fraud, risk & compliance is dominating the global artificial intelligence-based anti-money laundering software and solutions market, and will continue to do so through 2031.

Which End User is the Largest Shareholder in the Global Market for AI-based AML Solutions?

Banks are the primary source of money and deal with millions of transactions every day. They get thousands of false positive indications of financial crime through AML solutions, which increases the cost and time to track every single transaction and confirm them as an anti-money laundering activity. Due to this, they suffer from high expenses as well as high fines.

However, banks are major consumers of AML solutions, but due to the above-mentioned problem, they are adopting artificial intelligence in AML solutions to reduce AML compliance audit cost, false-positive alerts, and the complexity of AML methods.

Banks hold nearly 50% market share, which is projected to increase at a double-digit CAGR over the decade.

The insurance industry is the second-largest platform from the perspective of transactions, where individuals or firms can park their large sums of income and can recover them easily. These large sums of funds attract money launders, underworld operators, and terrorist groups.

In this process, insurance firms require to check an individual’s and firm’s identity carrying out financial transactions. To avoid hefty penalties and increase transparency, these firms have started applying AI in AML processes. Insurance companies hold more than one-sixth market share among all the end users.   

Competitive Landscape

The AI-based anti-money laundering solutions industry is fragmented in nature, where market leaders are dominating due to their global reach and funds. There are numerous new players who are also acquiring a share in the global market and challenging the big players.

Moreover, companies are dedicatedly focusing on geographical expansion, product up-gradation, and much more. Some of the recent developments of key AI-based AML service providers are:

• On 11th February 2021, Nice Actimize launched WL-X, its breakthrough, next-generation Watch List (WL) screening solution leveraging the power of artificial intelligence for superior data management, advanced screening capabilities, and frictionless customer on-boarding.
• On 29th April 2021, NICE Actimize and Refinitiv partnered to offer the NICE Actimize’s SURVEIL-X Holistic Surveillance Suite to the Asia Pacific market.
• On 22nd June 2021, ComplAdvantage added Know Your Business (KYB) data to create the industry’s first unified and most comprehensive data graph of individuals and business entities. KYB is the process to verify the identity and understand the risk of business customers, either before or during their on-boarding to a service.
• On 9th June 2021, Pega Systems partnered with WithYouWithMe, a tech organisation that is making talent more accessible globally, to provide free training to WithYouWithMe students to train up Pega certified professionals, ready to start work on projects that require the specific Pega qualifications in process automation to help fulfil the digital skills gap.
• On 29th April 2020, Temenos AG launched eight propositions – using innovative Explainable AI (XAI) and cloud technologies – to help banks in their immediate response to the COVID-19 crisis.
• On 24th February 2021, Ayasdi launched Sensa, a pioneering solution for AML and financial crime detection to thwart money laundering activities.
• On 27th April 2021, AI-based big data analytics provider ThetaRay as launched SONAR Solutions, an AML cross-border payments solution as a cloud-based service.

Many recent developments related to companies offering AI-based AML solutions have been tracked by the team of Fact.MR, which are available in the available in the full report.

AI-based AML Solutions Market Report Scope

Attribute	Details
Forecast Period	2021-2031
Historical Data Available for	2016-2020
Market Analysis	US$ Mn for Value
Key Regions Covered	North America Latin America Europe East Asia South Asia Oceania MEA
Key Countries Covered	United States Canada Brazil Mexico Germany United Kingdom France Italy BENELUX China Japan South Korea India Malaysia Singapore Australia New Zealand Turkey South Africa GCC Countries
Key Market Segments Covered	End Use Use Case Region
Key Companies Profiled	ACI Worldwide, Inc. FICO (Fair, Isaac and Company) SAS Institute Inc. Brighterion, Inc. IBM Genpact Limited Fenergo Compliance AI BAE Systems Temenos AG Pegasystems Inc. ComplyAdvantage NICE Actimize DataVisor Inc. Featurespace Limited ThetaRay Ayasdi AI LLC Feeszai Inc. Jumio Inc. Tookitaki Holding Pte. Ltd
Pricing	Available upon Request

Market Segments Covered in AI-based AML Solutions Industry Research

• By End User

  • Banks
  • Insurance Companies
  • Asset Management
  • Money Service Businesses
  • Securities
  • Other FSIs

• By Use Case

  • Transaction Monitoring  
  • KYC (Know Your Customer)       
  • Fraud, Risk & Compliance           
    • Trade AML
    • Capital Markets AML
    • Correspondent Banking AML
    • Fraud
    • Credit Risk
  • Crime Pattern Detection 
  • Risk Scoring Customers and Accounts
  • Watch-List Screening       
  • Alert Management and Reporting       
  • Other Solutions