Connect your existing transaction monitoring system to an advanced analytics environment in the cloud. Enhance your anti-money laundering (AML) program's overall efficiency and effectiveness with artificial intelligence (AI) and machine learning.
Modernize your existing AML solution by operationalizing AI and machine learning in the cloud. Lower compliance costs by reducing false positives, automating investigations and improving detection.
Boost the productivity of your AML analytical teams.
Empower data scientists, business analysts and other analytics professionals with highly accurate results from a single, collaborative environment that supports the entire machine learning pipeline. A variety of users can access and prepare data, perform exploratory analysis, build and compare multiple AML machine learning models, autotune hyperparameters and execute one-click model deployment.
Dramatically reduce false positives and eliminate unplanned model tuning efforts with ongoing optimization.
Always keep your AML models performing at their highest levels with performance benchmarking reports and alerts generated for easy tracking to indicate model decay. Ongoing monitoring identifies when it’s necessary to refine or retire a model. And model retraining integrates with the model pipeline processing environment for increased efficiency.
Explore multiple approaches quickly to find the optimal solution.
Easily build and train AML machine learning models with a user-friendly drag-and-drop interface. Users can explore and compare multiple models quickly. Find the optimal parameter settings fast for diverse machine learning algorithms – including decision trees, random forests, gradient boosting, neural networks, support vector machines and factorization machines – simply by selecting the option users want. Users can also combine unstructured and structured data in integrated machine learning programs for more valuable insights from new data types.
Ensure transparency with explainable AI and machine learning
Standard interpretability reports are available in all modeling nodes, including LIME, ICE, Kernel SHAP, PD heatmaps, etc., with explanations in simple language from embedded natural language generation capabilities.
Improve operational efficiency and gain a single view of the customer.
Examine alerts post-generation using predictive models to determine whether they are false positives. You can wrap this model around an existing AML platform to identify poor-quality alerts so investigators can spend more time on higher-value cases and significantly increase their investigation efficiency. Apply machine learning to account for inconsistencies, errors, abbreviations and incomplete records for resolving entities and creating a holistic view of risk through a single, global customer ID.
이 새로운 솔루션은 폭넓고 깊이 있게 모든 분석 과제를 해결할 수 있는 최첨단 오픈 아키텍처인 SAS Viya를 기반으로 실행됩니다. 단일 클라우드 환경인 SAS Viya는 확장 가능하고 안전할 뿐만 아니라 애자일 IT 환경에 없어서는 안 될 분석 관리 및 거버넌스를 통해 데이터 사이언티스트에서 비즈니스 분석가까지, 그리고 애플리케이션 개발자에서 기업 임원에 이르기까지 누구나 이용할 수 있습니다. 분석 분야를 선도하는 세계적 리더인 SAS와 함께 여러분이 기대해왔던 성능을 경험해보세요.
Explore More on SAS® Financial Crimes Analytics
Learn how AI, machine learning and robotic process automation can help you overcome challenges, improve results and make your AML/CFT programs more efficient and effective.
Check out these products related to SAS Financial Crimes Analytics.