Designing And Implementing Enterprise Early Warning Systems - For Enterprise Risk Management and Strategy Execution Management Functions

Learn how to use data science-based approaches and tools to model and assess risks and implement predictive models to detect anomalous behavior in portfolio risk measures. impact of strategic initiatives, report on strategy and strategic objectives, and implement predictive models to detect anomalous performance events and metrics. Learn about emerging practices in predictive analytics within the risk management and strategy management space. Learn how to transform data into powerful risk-based anomaly detection insights.

 

Organizations, especially Insurance and Financial institutions manage numerous portfolios whose risk must be managed continuously while at the enterprise levels, Risk Management teams struggle to effectively map the universe of risks associated with their organization’s policy, strategy, and operations. The large amounts of data that must be processed in these scenarios render this a considerable effort. The data science-based approach offers a system that autonomously detects anomalies in the risk measures of financial portfolios and helps orchestrate early warning systems to automatically track and identify risk.

This course shows corporate executives, Risk Managers, and team members in the risk management and audit functions design, implement and deploy data science-based early warning systems for risk management.

This course features 5-days in-person training and 3 live Zoom based bootcamp-style project sessions designed to improve mastery in small teams through real-life project-based live online training and handholding sessions

 

Course Objectives:

The course introduces participants to data science and data science-based approaches to modeling risk in any domain.

Through a series of well-structured learning modules, this Couse aims to build capability in participants to adopt and apply data science and machine learning-based approaches to implement robust risk management systems in their organizations and deploy an early warning system for enterprise, strategy, and strategic initiatives risk management as well as operational/transactional risk domains.

What You Will Learn:

•Move beyond the spreadsheet with a foundational understanding of data science tools, processes, and models and apply them to uncover insight from data

•Leverage data science to transform your risk management and reporting system while avoiding common mistakes associated with interpreting datasets, performance metrics, and risk profile/pattern visualizations

•Create a data-driven framework for your organization’s risk management system across all types and levels of risk

•understand the different outlier detection techniques and review evaluation criteria,

•Learn advanced techniques in anomaly detection and use these to implement an early warning system for proactive, future-oriented risk management.

If you are in the Risk Management function, Internal Audit, Quality Assurance Financial Crime or Economic Intelligence Unit, Process improvement, or Transaction monitoring and would like to become a data science-based practitioner of predictive analytics – or if you already are and would like to hone your knowledge across methods and best practices – this workshop is for you.

Who Should Attend:

For corporate executives and team leads  who work with data in the following functions;

•Enterprise Risk Management | strategy Management | Human Capital Management |  Performance Management |  Strategic Programs/Projects Management | Internal Audits & Quality Assurance | Financial Crime Analysts| Transaction Monitoring Professionals | Business process management