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Monitor and assess environmental and social risks and impacts with deep learning.
Optimization and 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.
MONITORING
Having the right frameworks, processes, and procedures to collect quality and relevant data is the first step to being able to use A.I. to monitor and assess ESG risks and opportunities for positive impact.
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Keys:
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1. Collecting and monitoring all data channels internal and external to a matrix organization.
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2. Create new sources of quality and timely data.
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REPORTING
Once data collection mechanisms and frameworks are in place and ESG risks and impacts can be assessed, timely reporting is critical.
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Keys:
3. Reporting and taxonomy that aligns with standards and regulations.
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4. Reporting to all relevant stakeholders, both internal and external to the organization.
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