NICE Actimize Financial Crime and Compliance

NICE Actimize solutions are built on a common open and extensible platform that provides functionality, stability, and robustness across the total NICE Actimize portfolio. A key driver of the platform allows for the combination of advanced rules-based analytics and machine learning AI models wherever they can be leveraged to improve detection and gain efficiencies in the compliance process. In that respect, the NICE Actimize platform applies various techniques where appropriate to highlight specific known scenarios that should be recognized through a robust set of out-of-the-box rules, using ML models that learn to classify communications appropriately by intent and eliminate non-relevant interactions from review, applying NLP to extract meaningful insights and predictive AI models to highlight changes in behavior of employees.

The platform enables building sophisticated statistical measures for historical behavior profiling learned in Batch or R/T, as well as running any kind of ML algorithm or business logic (some examples include XGBoost, Logistic Regression, Decision Trees, Clustering as well as Deep Learning NN models). Financial events flow through the system and then a set of expert as well as data-driven features (risk signals) are calculated against them, the results and insights derived from the calculations are captured in a data repository that can be a standard DB or a big data lake (depending on customer size and preference).

The data is also streamed to the NICE Actimize analytics lab, X-Sight AI, hosted on the cloud, allowing analysts and data scientists to analyze the data and create new features and ML models in a dedicated data science environment named “X-Sight Studio”. X-Sight Studio is also offered by NICE Actimize to our customers’ data scientists for data analysis, data visualization and model development and monitoring. Our customers use X-Sight Studio to develop homegrown models and features using the calculation produced by the NICE Actimize system, as well as based on additional data sources of their choice. These models are then integrated to the NICE Actimize runtime environment where the financial events are being risk rated for fraudulent activity, money laundering and compliance breaches with the AI models produced.

Each of the NICE Actimize solutions is equipped with a set of Out-of-the-Box analytical models and features which are built using our Analytics development environment and which are being evaluated throughout the NICE Actimize detection flow. Some of the most common problems that NICE Actimize solves with AI models are customer segmentation problems, alert prioritization problems, risk rating transactions for Fraud, identifying anomalous patterns in trading behavior and identifying risky behavior from electronic and voice communications.

The NICE Actimize’s philosophy towards analytics includes a hybrid model of an execution environment (that can be running on premise or on cloud) flexibly connected to an analytics development environment that’s running on the cloud.

Data continuously flows from the execution environment to the cloud environment, which allows NICE Actimize to leverage the power and speed of the cloud to bring cutting edge innovation to our customers. This approach enables the NICE Actimize systems to utilize the most innovative AI techniques and algorithms available at any given moment in time when models are delivered.

To execute on our strategy of continuous innovation and delivery, the NICE Actimize analytics organization consists of a few well-connected groups, each with its own role in creating high quality analytics.

Each solution has a team includes of domain experts, developers, analysts, data scientists and ML practitioners that create and develop features, models, and modeling techniques appropriate for the specific solution and business problem. Our solution experts leverage the billions of financial events which flow into our cloud environment every day from across our customer base to create new solution features, models, and algorithms. This strategic data asset, which keeps expanding, allows our development teams to focus on features that bring out significant value in preventing, detecting and stopping financial crime and test out algorithms and approaches on real data in a secure and safe fashion.