The anti-money laundering software market size is expected to grow from US$2,116. 3 million in 2021 to US$6,162. 8 million by 2028; The market share of anti-money laundering software is estimated to grow at a CAGR of 16.
New York, July 4, 2022 (GLOBE NEWSWIRE) — Reportlinker.com announces the release of the report “Anti-Money Laundering Software Market Forecast to 2028 – COVID-19 Impact and Global Analysis By Component, Deployment, Product, End User” – https://www.reportlinker.com/p06289881/?utm_source=GNW
6% from 2022 to 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 driven 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, the increasing complexity hinders the growth of the market significantly.
The COVID-19 pandemic accelerated the development of digital technologies. Due to political restrictions worldwide, 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 against illegal money transfers. As a result, the demand for anti-money laundering software has skyrocketed, 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 several other financial institutions track every transaction executed by their client 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 activity; it can also generate reports and create alerts for suspicious activity. The transactions monitored using such software solutions include cash deposits and withdrawals, wire transfers and ACH activities.
AML transaction monitoring solutions may also include sanctions screening, blacklist screening, and customer profiling. Banks have responded to these trends by investing heavily in manpower, manual checks (“checkers checking the checkers”) and systems that meet specific needs .
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 system, especially in the area of 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. 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 capable of detecting, tracking and preventing 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 companies meet the critical challenge of treating 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 security and compliance requirements. Artificial intelligence (AI), on the other hand, can help organizations deal with various problems arising from the rise of digitization.
It can reduce the need for human intervention, especially in the fight against money laundering. While AI will never completely replace humans, it could help reduce the need for human approval.
The anti-money laundering software market is segmented into component, implementation, product and end user. In the anti-money laundering software market analysis, the market is segmented into software and services.
In terms of implementation, the global anti-money laundering software market is divided into on-premise and cloud-based. currency transaction reporting and customer identity management.
In terms of end-users, the anti-money laundering software market is segmented into healthcare, retail, BFSI, IT & telecom, government, and others. The global money laundering software market is segmented into five major regions: North America, Europe, APAC, MEA, and SAM.
The total market size of anti-money laundering software has been inferred using both primary and secondary sources. Extensive secondary research has been conducted using internal and external sources to obtain qualitative and quantitative information related to the market.
The process also serves to gain an overview and forecast for the anti-money laundering software market across all segments. Multiple primary interviews were also conducted with industry participants to validate the data and gain more analytical insights.
The industry experts who participate in this process include VPs, business development managers, market intelligence managers, national sales managers and outside advisors such as valuation experts, research analysts and key opinion leaders specializing in the anti-money laundering software market.
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