DUBLIN–(BUSINESS WIRE)–The report “Anti-Money Laundering Software Market Forecast to 2028 – COVID-19 Impact and Global Analysis by Component, Implementation, Product, End User” has been added to ResearchAndMarkets.com’s offer.
The market size of anti-money laundering software is expected to grow from US$2,116.3 million in 2021 to US$6,162.8 million in 2028; The market share of anti-money laundering software is estimated to grow at a CAGR of 16.6% between 2022 and 2028.
Anti-Money Laundering Software (AML) is used to comply with financial institutions’ legal requirements for the prevention and reporting of money laundering activities. Increasing online transactions and growing concerns about fraudulent transactions have led to the adoption of these software solutions.
Furthermore, supportive government regulations, increasing cryptocurrency adoption and increasing developments in the FinTech sector are driving the growth of the money laundering software market to a significant extent. However, increasing complexity significantly hinders the growth of the market.
The COVID-19 pandemic has accelerated the development of digital technologies. Due to global political restrictions, everyone relied on digital platforms to meet their daily needs. The most common application is for digital payments.
Digital wallets, also known as eWallets, are becoming increasingly popular. This transition has increased the risk of illegal money transactions. The FATF has warned banks about illegal money transfers. As a result, the demand for anti-money laundering software has soared, and this factor has significantly influenced the growth of the anti-money laundering software market.
Various product launch strategies implemented by companies are driving the anti-money laundering software market. In September 2020, NASDAQ, Inc. for example, AI-based technology to help commercial and retail banks automate AML investigations. The newly launched technology could make it faster and cheaper for banks and other financial institutions to check the alerts, weakening money laundering cases generated by banking transaction tracking systems.
In June 2020, FIS partnered with FICO, a credit rating company, to introduce new anti-money laundering software in response to the escalating flow of dirty money during the COVID-19 pandemic. The platform uses machine learning and AI technologies to detect suspicious transactions, alert financial institutions and support bank investigators with detailed, transparent information.
Banks and various other financial institutions monitor every transaction carried out by their customers on a daily basis. The transaction monitoring system helps them perform the monitoring tasks in real time. In addition, by merging the transaction monitoring information with analysis of the customers’ historical information and account profile, the software can provide financial institutions with a complete analysis of a customer’s profile, risk levels and forecasted future activities; it can also generate reports and create alerts for suspicious activity. The transactions monitored with such software solutions include cash deposits and withdrawals, bank transfers and ACH activities.
AML transaction monitoring solutions may also include sanctions screening, blacklist screening and customer profiling functions. Banks have responded to these trends by investing heavily in manpower, manual checks (“checkers check the checkers”) and systems that meet the needs of a particular point in time.
In the US, for example, the money laundering (AML) workforce has increased up to tenfold in the past five years at major banks. Banks have typically taken a piecemeal approach, directing staff to areas with the weakest controls. This has resulted in compliance programs being developed for individual countries, product lines and customer segments – with all the duplication that suggests. Banks have also hired thousands of researchers to manually review high-risk transactions and accounts identified through inefficient exception-based rules.
Recently, the financial ecosystem has been transformed by the rapid advancements in machine learning, data science and their ability to produce algorithms for predictive data analytics. In recent times, machine learning has shown promise for the banking industry, especially in detecting hidden patterns and suspicious money laundering activities.
Machine learning facilitates identifying money laundering typologies, strange and suspicious transactions, customer behavior changes, transactions of customers belonging to the same geographic location, age, groups and other identities, and helps reduce false positives.
It also helps analyze comparable transactions for focal entities and correlate alerts flagged as suspicious in regulatory reports. The advanced capabilities of machine learning and data science in AML solutions are expected to increase the market share of anti-money laundering software over the forecast period.
In addition, as money launderers continue to explore newer ways to use banks for illegal activities, the timely detection of money laundering activities is the most challenging aspect of implementing an efficient AML. Numerous companies are launching innovative technologies that can detect, track and prevent money laundering.
For example, in March 2020 Infotech Limited introduced AMLOCK Analytics, an advanced AML solution that enables banks and financial institutions to recognize complex AML patterns. Powered by AI and machine language, the solution helps enterprises meet the critical challenge of processing a high false positive and providing a complete picture of how to investigate an alert.
Managing the compliance teams and thousands of people working remotely has been a critical responsibility for compliance officers during the COVID-19 pandemic. During this crisis, the protection of financial institutions extends beyond physical borders. Therefore, an external and digital infrastructure is necessary to meet the security and compliance requirements.
On the other hand, artificial intelligence (AI) can help organizations deal with various problems arising from the rise of digitalization. It can reduce the need for human intervention, especially in the fight against money laundering. While AI will never fully replace humans, it could help reduce the need for human approval.
Key Market Dynamics
Market factors
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FinTech’s increasing focus on implementing automated anti-money laundering systems
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Rising demand for advanced transaction monitoring solutions
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Increasing focus on mitigating risks related to digital payment methods
Market Restrictions
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The structure and technology of anti-money laundering software is becoming increasingly complex
Market opportunities
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Increasing Cryptocurrency Adoption
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Increasing adoption of advanced analytics
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Implementing Government Regulations to Implement AML Solutions
Future trends
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Sharing information between banks and other financial institutions
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Increased use of artificial intelligence
Company Profiles
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Accenture
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Aci Worldwide
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Ascent Technology Consulting
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BAE systems
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Eastnets Holding Ltd.
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Opentext Corporation
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Oracle Corporation
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Nice Ltd.
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Sas Institute
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Nasdaq Inc
For more information on this report, please visit https://www.researchandmarkets.com/r/47xrya
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