DUBLIN--(BUSINESS WIRE)--The "Anti-Money Laundering Software Market Forecast to 2028 - COVID-19 Impact and Global Analysis By Component, Deployment, Product, End User" report has been added to ResearchAndMarkets.com's offering.
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 anti-money laundering software market share is estimated to grow at a CAGR of 16.6% from 2022 to 2028.
An anti-money laundering (AML) software is deployed to meet the legal requirements of financial institutions for preventing and reporting the activities of money laundering. Increasing online transactions and rising concerns regarding fraudulent transactions have steered the adoption of these software solutions.
Further, supportive government regulations, rising adoption of cryptocurrency, and increasing developments in the FinTech sector favor the anti-money laundering software market growth to a significant extent. However, increasing complexities impede the growth of the market to a considerable extent.
The COVID-19 pandemic accelerated the development of digital technologies. Because of political restrictions globally, everyone was relying on digital platforms to meet their everyday needs. The most common application is for digital payments.
Digital wallets, often known as eWallets, are becoming more popular. As a result of this transition, the likelihood of unlawful money transactions has grown. The FATF has cautioned banks about unlawful money transactions. As a result, demand for anti-money laundering software has surged, and this factor has significantly impacted the anti-money laundering software market growth.
Various product launch strategies implemented by companies are propelling the anti-money laundering software market. For instance, in September 2020, NASDAQ, Inc. launched AI-based technology to help commercial and retail banks automate AML investigations. The newly launched technology can make it swifter and cheaper for banks and other financial institutions to scrutinize through the alerts, which weakens money-laundering cases generated by bank transaction monitoring systems.
In June 2020, FIS collaborated with FICO, a credit scoring company, to introduce a new anti-money laundering software in response to the escalated flow of dirty cash 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, transparent intelligence.
Banks and various other financial institutions monitor each transaction performed by their customer on daily basis. The transaction monitoring system helps them perform the monitoring tasks on a real time basis. Furthermore, by coalescing the transaction monitoring information with analysis of the historical information and account profile of the customers, the software can offer financial institutions with a complete analysis of a customer's profile, risk levels, and predicted future activity; it can also generate reports and create alerts to suspicious activities. The transactions monitored using such software solutions include cash deposits and withdrawals, wire transfers, and ACH activity.
AML transaction monitoring solutions also may include sanctions screening, blacklist screening, and customer profiling features. Banks have responded to these trends by investing heavily in manpower, manual controls ("checkers checking the checkers"), and systems addressing point-in-time needs.
For example, in the US, anti-money laundering (AML) compliance staff have increased up to tenfold at major banks over the past five years. Banks have typically used a piecemeal approach, redirecting staff to areas with the weakest controls. This has resulted in compliance programs built 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 through inefficient, exception-based rules.
Lately, the financial ecosystem has been transformed by the swift developments in machine learning, data science, and their ability to produce algorithms for predictive data analytics. In recent times, machine learning has proved to be holding great promise for the banking system, particularly in the area of detecting hidden patterns and suspicious money-laundering activities.
Machine learning facilitates identifying money-laundering typologies, strange and suspicious transactions, behavioral transitions in customers, transactions of customers belonging to the same geography, age, groups, and other identities, and helps reduce false positives.
It also helps analyze similar transactions for focal entities and correlate alerts flagged as suspicious in regulatory reports. The advanced capabilities provided by the machine learning and data science in AML solutions are expected to drive the anti-money laundering software market share during the forecast period.
Furthermore, as money launders continue to explore newer ways to use banks for illicit activities, the timely detection of the laundering activities is the most challenging aspect in implementing an efficient AML. Numerous companies are launching innovative technologies that are capable of detecting, tracking, and preventing money laundering.
For instance, in March 2020, Infotech Limited introduced AMLOCK Analytics, an advanced AML solution that allows banks and financial institutions to recognize complex AML patterns. Powered by AI and machine language, the solution helps enterprises meet the critical challenge of handling a high false positive and deliver a complete view of scrutinizing an alert.
Managing the compliance teams and thousands of people working remotely has been a crucial responsibility for compliance officers during the COVID-19 Pandemic. During this crisis, the protection of financial institutions extends beyond physical boundaries. Hence, a remote and digital infrastructure is necessary to meet security and compliance demands.
Artificial intelligence (AI), on the other hand, can help organizations deal with various issues arisen from the rise in digitalization. It can reduce the need for human intervention, particularly in anti-money laundering circumstances. Although AI will never be able to completely replace humans, it can help reduce the need for human approval.
Key Market Dynamics
- Increasing Focus of FinTech on Implementing Automated Anti-Money Laundering Systems
- Rising Demand for Sophisticated Transaction Monitoring Solutions
- Increasing Focus on Limiting Risks Related to Digital Payment Methods
- Increasingly Complicating Structure and Technology of Anti-Money Laundering Software
- Rising Adoption of Cryptocurrency
- Surging Adoption of Advanced Analytics
- Implementation of Government Regulations to Deploy AML Solutions
- Information Sharing Among Banks and Other Financial Institutions
- Increased Use of Artificial Intelligence
- Aci Worldwide
- Ascent Technology Consulting
- BAE Systems
- Eastnets Holding Ltd.
- Opentext Corporation
- Oracle Corporation
- Nice Ltd.
- Sas Institute
- Nasdaq Inc
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