Anti-Money Laundering Software Market Forecast to 2028 –

NEW YORK, July 4, 2022 (GLOBE NEWSWIRE) — 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” –
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. Increasing online transactions and increasing concerns about fraudulent transactions have fueled the adoption of these software solutions.

Additionally, supportive government regulations, increasing adoption of cryptocurrencies, and increasing developments in the FinTech sector are also significantly conducive to the growth of the anti-money laundering software market. However, the increasing complexity is hampering the growth of the market to a significant extent.

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 digital payments. Digital wallets, often known as eWallets, are becoming increasingly popular. As a result of this transition, the likelihood of illegal money transactions has increased. The FATF has warned banks against illegal money transactions. As a result, the demand for anti-money laundering software has increased sharply and this factor has significantly impacted the growth of anti-money laundering software market.

Various product launch strategies implemented by companies are driving the anti-money laundering software market. For example, in September 2020, NASDAQ, Inc. introduced AI-based technology to help commercial and retail banks automate AML investigations. The newly introduced technology can allow banks and other financial institutions to check the alerts faster and cheaper, weakening the money laundering cases generated by bank transaction monitoring systems. In June 2020, FIS worked with FICO, a credit scoring company, to introduce new anti-money laundering software in response to the escalated 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 provide detailed, transparent information to bank investigators.

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

In addition, by merging transaction monitoring information with analysis of historical information and customer account profile, the software can provide financial institutions with a complete analysis of customer profile, risk level and predicted future activity; It can also generate reports and create alerts about suspicious activity. Transactions monitored with such software solutions include cash deposits and withdrawals, wire transfers, and ACH activity.

AML transaction monitoring solutions can also include sanctions screening, blacklisting, and customer profiling capabilities. Banks have responded to these trends by investing heavily in human resources, manual controls (“auditors review auditors”) and systems that address point-in-time requirements.

In the US, for example, anti-money laundering (AML) compliance staffing at major banks has increased tenfold over the past five years. Banks have typically taken a step-by-step approach, redirecting employees to areas with the weakest controls. This has led to compliance programs being developed for individual countries, product lines and customer segments – with all the duplications to suggest it. Banks have also hired thousands of investigators to manually review high-risk transactions and accounts identified by inefficient exception-based rules.

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

Machine learning makes it easier to identify money laundering typologies, strange and suspicious transactions, changes in customer behavior, transactions from customers belonging to the same region, age, groups and different identities, and helps reduce false positives. It also helps analyze similar transactions for focus entities and correlate alerts flagged as suspicious in regulatory reports.

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

As money launderers continue to look for new ways to use banks for illegal activities, timely detection of money laundering activities is the most challenging aspect of implementing efficient AML. Numerous companies are introducing 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 detect complex AML patterns. Powered by AI and machine language, the solution helps organizations address the critical challenge of dealing with high numbers of false alarms and providing a complete view of an alert’s verification.

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, protecting financial institutions goes beyond physical borders.

Therefore, remote and digital infrastructure is required to meet security and compliance requirements. Artificial intelligence (AI), on the other hand, can help organizations solve various problems arising from increasing digitization.

This can reduce the need for human intervention, particularly in anti-money laundering cases. Although AI can never fully replace humans, it can help reduce the need for human consent.

Anti-Money Laundering Software Market is segmented into component, deployment, product and end-user. Analyzing the Anti-Money Laundering Software market 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-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 anti-money laundering software market is segmented into five major regions: North America, Europe, APAC, MEA and SAM.

The total Anti-Money Laundering Software market size has been derived using both primary and secondary sources. In order to obtain qualitative and quantitative information on the market, extensive secondary research was conducted using internal and external sources.

The process also serves the purpose of obtaining an overview and forecast of the Anti-Money Laundering Software market in relation to all the segments. Also, several primary interviews with industry participants were conducted to validate the data and gain further analytical insights.

Industry professionals participating in this process include VPs, Business Development Managers, Market Intelligence Managers, National Sales Managers and outside consultants such as valuation experts, research analysts and key opinion leaders specializing in the anti-money laundering software market.
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