Anti-Money Laundering Software Market Forecast to 2028



Anti-money laundering software market size is expected to grow from US$2,116. 3 million in 2021 at US$6,162. 8 million by 2028; The anti-money laundering software market share is expected to grow at a CAGR of 16.

New York, July 4 07, 2022 (GLOBE NEWSWIRE) — announces the publication 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.
6% from 2022 to 2028.

Anti-Money Laundering (AML) software is deployed to meet the legal requirements of financial institutions to prevent and report money laundering activities. The increase in online transactions and growing concerns about fraudulent transactions have driven the adoption of these software solutions.

In addition, favorable government regulations, growing adoption of cryptocurrency, and growing developments in the FinTech sector are driving the growth of the anti-money laundering software market to a large extent. However, the growing complexity is significantly hampering the growth of the market.

The COVID-19 pandemic has accelerated the development of digital technologies. Due to political restrictions globally, everyone relied on digital platforms to meet their daily needs.

The most common application is for digital payments. Digital wallets, often referred to as e-wallets, are becoming increasingly popular. As a result of this transition, the likelihood of illegal monetary transactions has increased. The FATF has warned banks against illegal monetary transactions. As a result, the demand for anti-money laundering software has increased, and this factor has had a significant impact on the growth of the anti-money laundering software market.

Various product launch strategies implemented by companies are propelling the Anti-Money Laundering Software market. For example, in September 2020, NASDAQ, Inc. launched AI-based technology to help commercial and retail banks automate AML investigations. Recently launched technology can allow banks and other financial institutions to more quickly and cost-effectively investigate alerts, weakening money laundering cases generated by banking transaction monitoring systems. In June 2020, FIS collaborated with FICO, a credit reporting company, to introduce new anti-money laundering software in response to the escalating flow of dirty money amid 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 and transparent intelligence.

Banks and various other financial institutions monitor every transaction made by their customer on a daily basis. The transaction monitoring system helps them perform the monitoring tasks in real time.

Additionally, by merging transaction monitoring information with analysis of historical information and customer account profile, the software can offer financial institutions a comprehensive analysis of a customer’s profile, risk levels and planned future activity; it can also generate reports and create suspicious activity alerts. Transactions monitored using these software solutions include cash deposits and withdrawals, wire transfers, and ACH activity.

AML transaction monitoring solutions can also include sanctions screening, blacklist screening and customer profiling features. Banks have responded to these trends by investing heavily in manpower, manual checks (“auditors checking auditors”), and systems to meet ad hoc needs. .

For example, in the United States, anti-money laundering (AML) compliance personnel have increased tenfold at major banks in the past five years. Banks have generally used a piecemeal approach, redirecting staff to areas where controls are weakest. This has resulted in compliance programs designed for individual countries, product lines and customer segments, with all the duplication that suggests. Banks have also hired thousands of investigators to manually review high-risk transactions and accounts identified by ineffective exception-based rules.

Lately, the financial ecosystem has been transformed by rapid developments in machine learning, data science and their ability to produce algorithms for predictive data analysis. In recent times, machine learning has shown great promise for the banking system, especially in the area of ​​detecting hidden patterns and suspicious money laundering activities.

Machine learning facilitates the identification of money laundering typologies, strange and suspicious transactions, behavioral transitions among customers, transactions of customers belonging to the same geography, age, groups and other identities, and helps to reduce false positives. entities and correlate alerts flagged as suspicious in regulatory reports.

The advanced capabilities provided by machine learning and data science in AML solutions are expected to drive the anti-money laundering software market share over the forecast period.

Additionally, as money launderers continue to explore new ways to use banks for illicit activity, early detection of laundering activity is the most challenging aspect of implementing an AML. efficient. Many companies are launching innovative technologies capable of detecting, tracking and preventing money laundering.

For example, in March 2020, Infotech Limited launched 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 organizations address the critical challenge of managing a high number of false positives and providing a complete view of an alert’s investigation.

Managing 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, a remote and digital infrastructure is needed to meet security and compliance requirements. Artificial intelligence (AI), on the other hand, can help organizations deal with various issues related to the rise of digitalization.

This can reduce the need for human intervention, especially in anti-money laundering circumstances. Although AI can never fully replace humans, it can help reduce the need for human approval.

The anti-money laundering software market is segmented into component, deployment, product, and end user. The Anti-Money Laundering Software Market analysis by component, the market is segmented into software and services.

In terms of deployment, the global anti-money laundering software market is categorized into on-premises and cloud-based. Anti-Money Laundering Software Market Analysis By Product, Global Anti-Money Laundering Software Market is categorized into Transaction Monitoring, Compliance Management, Currency Transaction Reporting, and Currency Management customer identity.

In terms of end-user, the anti-money laundering software market is segmented into healthcare, retail, BFSI, IT & telecommunications, government, and others. The global anti-money laundering software market is segmented into five major regions: North America, Europe, APAC, MEA and SAM.

The overall Anti-Money Laundering Software market size has been derived using primary and secondary sources. Extensive secondary research was conducted using internal and external sources to obtain qualitative and quantitative information related to the market.

The process also serves to get an overview and forecast for the Anti-Money Laundering Software market with respect to all segments. Additionally, several primary interviews were conducted with industry participants to validate the data and gain more analytical insights.

Industry experts involved in this process include vice presidents, business development managers, business intelligence managers, national sales managers and external consultants, such as valuation experts, research analysts and key opinion leaders, specializing in the anti-money laundering software market.
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