Monitor and assess ESG risks and impacts with deep learning.
Mathematical optimization and computational statistics is at the heart of machine learning. Fueled by better data and supported by increasingly powerful hardware, machine learning is well suited to tackle challenging and complex problems, support decision-making, and provide deep monitoring with high-dimensionality.
Having the right frameworks, processes, and procedures to collect quality and relevant data is the first step to being able to monitor and assess ESG risks and opportunities for positive impact.
Collecting and monitoring all data channels: internal to a matrix organization and external.
Low Latency Data
Once data collection mechanisms and frameworks are in place and ESG risks and impacts can be assessed, timely reporting is critical.
Reporting standards and taxonomy that aligns with recommendations and regulations.
Reporting to all relevant stakeholders, both internal and external to the organization.