Proliferation of machine learning (ML) in various are benefitting both consumers as well as companies in the anti-money laundering software market. Stakeholders in the value chain are increasing their profits with the help of ML, since the novel technique helps to recognize undetected patterns. Hence, companies in the market for anti-money laundering software are developing solutions that are integrated with MI and cloud computing. For instance, ACTICO- an end-to-end automation solutions company has developed an ML module which is built on the expertise of compliance managers and the knowledge gained from data.
Companies in the anti-money laundering software market are increasing research to develop software with ML to integrate with other functional modules. They are increasing efforts to develop solutions that comply with government norms. Such advanced software systems are gaining visibility in banks and insurance companies, as there is a growing need for software systems that are capable of detecting clients and transactions that are a potential compliance risk.
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Need to Eliminate False Alerts in Transactional Monitoring Solutions Drives Demand for Software
The anti-money laundering software market is anticipated to grow at a CAGR of ~13% during the forecast period. Since end users have high expectations from software systems, companies are developing solutions with robust detection of true suspicious cases. However, rapid growth of the wire industry is challenging for companies in the market for anti-money laundering software who find it difficult to efficiently monitor and detect suspicious transactions. Hence, companies are increasing R&D to develop software that eliminate the issue of false alerts across monitoring and screening programs.
Transaction monitoring is one of the key drivers contributing toward the robust growth of the anti-money laundering software market. As such, transaction monitoring system product type segment is estimated to lead the anti-money laundering software market and the global market is estimated to reach a value of ~US$ 4.9 Bn by 2027. Hence, companies are fine-tuning their efficacy in secondary scoring of the transaction monitoring process.
Cloud-based Deployment Model Helps in Immediate Analysis of Data Sources
Cloud-based services are projected to engulf the anti-money laundering software market whilst acquiring prominence in the banking world. This is evident since the cloud-based deployment model is estimated to take a huge leap in the market landscape. Thus, compliance officers are increasingly using cloud based services to improve functionality of anti-money laundering software. Moreover, cloud services are helping end users to analyze data in a variety of formats and from a variety of applications. Since most of the data comes from outside of the enterprise, cloud-based services help to harmonize the data and allow the compliance team to make judgments about suspicious activity.
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On the other hand, AI is another trend that is witnessing growth in the anti-money laundering software market. Due to the advantages of AI, end users are embracing cloud-based and other advanced services that are fast-changing business landscape. As such, companies are increasing efforts to introduce ‘skim’ relevant data to analyze information from internal and outside the enterprise.
Streamlined Workflows Set Pace for Innovations in Software
End users are aiming toward a streamlined approach toward innovations in the anti-money laundering software. They are demanding for small-scale pilot programs, since AML compliance has several moving parts. Thus, compliance managers are identifying areas where technological innovations could help design a pilot to test various theories. In order to achieve success in pilot projects, companies in the anti-money laundering software market are increasing awareness about weighing the results in the pilot against previous control strategies in the history of the enterprise.
Banks, being one of the key end users of anti-money laundering software are fighting financial crimes using cost-effective and ML-based software solutions. This is evident since retail banking is projected for exponential growth in the market for anti-money laundering software. There is a growing demand for software systems that prevent financial crimes. Increased R&D have led to innovations in ML-based analytical solutions and robotic process automation (RPA) workflows.
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