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.
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