SAS Anti-Money Laundering Integrations

Effortlessly load your data into or extract it from Hadoop and data lakes, ensuring it is primed for generating reports, visualizations, or conducting advanced analytics—all within the data lakes environment. This streamlined approach allows you to manage, transform, and access data stored in Hadoop or data lakes through a user-friendly web interface, minimizing the need for extensive training. Designed specifically for big data management on Hadoop and data lakes, this solution is not simply a rehash of existing IT tools. It allows for the grouping of multiple directives to execute either concurrently or sequentially, enhancing workflow efficiency. Additionally, you can schedule and automate these directives via the public API provided. The platform also promotes collaboration and security by enabling the sharing of directives. Furthermore, these directives can be invoked from SAS Data Integration Studio, bridging the gap between technical and non-technical users. It comes equipped with built-in directives for various tasks, including casing, gender and pattern analysis, field extraction, match-merge, and cluster-survive operations. For improved performance, profiling processes are executed in parallel on the Hadoop cluster, allowing for the seamless handling of large datasets. This comprehensive solution transforms the way you interact with data, making it more accessible and manageable than ever.

Combine master data management solutions with those found in SAS 9.4, where SAS MDM operates as a web-based interface accessible via the SAS Data Management Console. This system delivers a cohesive and precise representation of organizational data by consolidating information from multiple sources into a singular master record. Additionally, SAS® Data Remediation and SAS® Task Manager synergistically enhance SAS MDM's capabilities, as well as those of other SAS products, including SAS® Data Management and SAS® Data Quality. Through SAS Data Remediation, users can address and rectify issues arising from business rules in both batch jobs and real-time processes within SAS MDM. Meanwhile, SAS Task Manager serves as a supportive tool that integrates seamlessly with SAS Workflow technologies, allowing users to manage workflows initiated by other SAS applications with ease. By enabling the initiation, cessation, and transition of workflows uploaded to the SAS Workflow server, this ecosystem empowers organizations to maintain efficient data management practices. Overall, the integration of these technologies creates a robust framework for handling master data effectively.

SAS Financial Crimes Analytics is a cloud-based solution designed to enhance Anti-Money Laundering (AML) initiatives by leveraging artificial intelligence and machine learning to operationalize compliance efforts. This innovative platform empowers financial institutions to more effectively identify and thwart financial crimes by minimizing false positives, automating the investigative process, and enhancing overall detection capabilities. With features that simplify data exploration and visualization, users can easily import, transform, and integrate data using an intuitive drag-and-drop interface. Additionally, it streamlines the swift operationalization of analytical models through automated methods, facilitating efficient deployment in both batch and real-time scenarios. The solution seamlessly integrates with existing transaction monitoring systems, negating the necessity to overhaul current AML frameworks. Furthermore, it employs advanced analytics tools, such as network and text analytics, to provide a comprehensive understanding of risk factors, ultimately allowing institutions to adapt to the evolving landscape of financial crime threats. This holistic approach not only improves compliance but also strengthens the institution’s overall risk management strategy.