The proliferation of machine learning (ML) in various fields is benefiting both consumers and businesses in the anti-money laundering software market. Stakeholders in the value chain increase their profits with the help of ML, as the new technology helps to recognize undiscovered patterns. Hence companies in the market for Anti-money laundering software develop solutions that are integrated with MI and cloud computing. For example, ACTICO, an end-to-end automation solutions company, developed an ML module based on the expertise of compliance managers and knowledge gained from data.

Companies in the anti-money laundering software market are increasingly researching the development of software with ML for integration into other functional modules. They are stepping up their efforts to develop solutions that meet government standards. Such advanced software systems are gaining visibility in banks and insurance companies as there is a growing need for software systems that can identify customers and transactions that pose a potential compliance risk.

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The need to eliminate false positives in transaction monitoring solutions is increasing the demand for software

The anti money laundering software market is expected to grow at a CAGR of ~13% during the forecast period. Since end users have high expectations of software systems, companies develop solutions with reliable detection of true suspicious cases. However, the rapid growth of the wire industry is challenging for money laundering software companies in the market that find it difficult to efficiently monitor and detect suspicious transactions. As a result, companies are intensifying research and development to develop software that eliminates the problem of false positives in surveillance and verification programs.

Transaction monitoring is one of the main drivers behind the robust growth of the anti-money laundering software market. Therefore, the transaction monitoring system product type segment is estimated to lead the anti-money laundering software market, and the global market is estimated to be worth ~$ 4.9 billion by 2027. Therefore, companies optimize their effectiveness in the secondary assessment of the transaction monitoring process.

Cloud-based delivery model helps with the immediate analysis of data sources

Cloud-based services are intended to conquer the market for software to combat money laundering and at the same time gain importance in the banking world. This is evident as the cloud-based delivery model is expected to take a huge leap in the market landscape. Therefore, compliance officers are increasingly using cloud-based services to improve the functionality of anti-money laundering software. In addition, cloud services help end users analyze data in different formats and from different applications. Since most of the data comes from outside the company, cloud-based services help harmonize the data and enable the compliance team to assess suspicious activity.

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On the other hand, AI is another trend that is seeing growth in the anti-money laundering software market. Because of the benefits of AI, end users are leveraging cloud-based and other advanced services that are rapidly changing. For this reason, companies are increasingly trying to introduce relevant data to analyze information from inside and outside the company.

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Optimized workflows set the pace for innovations in software

End users seek a streamlined approach to innovation in anti-money laundering software. They call for small pilot programs because money laundering compliance involves several moving parts. Compliance managers therefore identify areas where technological innovations could help develop a pilot project to test different theories. In order to be successful in pilot projects, companies in the anti-money laundering software market are increasingly sensitizing to balancing the results of the pilot project with earlier control strategies in the company’s history.

Banks, which are among the major end users of anti-money laundering software, fight financial crimes with low-cost and ML-based software solutions. This is evident as the retail banking market is projected to grow exponentially for anti-money laundering software. There is a growing demand for software systems that prevent financial crime. Increasing research and development has led to innovations in ML-based analytics solutions and RPA (Robot Process Automation) workflows.

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