Emerging AI and Data Driven Supervisory Technology for Regulatory Compliance

Data Push: Push-based strategies are the default model. Automated the delivery on pre-determined specification, a forwarder is installed close to the source of the data, or built into the data generator/collector and pushes the events to an indexer. Data Pull: This approach provides significant flexibility by letting you create reports from multiple data sources and multiple data sets, and by letting you store and manage reports with an enterprise reporting server. Pull based cannot be reliable for real-time reports and information. Also, Pull base system most tolerate, its lack of real-time information cannot be best fit for supervisory Financial Institution as they demand real-time reporting with greater insights to financial health conditions of FIs. Supervisors can use machine learning tools to create a "risk score" for supervised entities. FINTRAC, the Financial Transactions and Reports Analysis Centre of Canada, has created one such score, evaluating the risk factors related to an institution's profile, compliance history, reporting behavior, and more.