Insurers today become more socially engaged and necessitate to put purpose alongside profit as its motivating force, they need to learn how to deal with unexpected and changing priorities Companies are, hence, mandated to provide explanations to the market related to these non-financial factors as part of their “business as usual” analysis process to identify material risks and growth opportunities. Current systems and processes are unable to meet the frequent and accurate reporting requirements as expected by regulators. Organizations are hence relying on one-stop-shop ESG platform that might address all the application needs that relates with a company ESG performance. If the centralization of the ESG data is surely an important step forward to obtain an encompassing view around ESG performance, it surely reveals some major issues considering the salience and completeness of underlying data, most of which are non-standard and unstructured by nature. This is where machine learning may play a vital role.
Goals and Objectives
The impact of AI, ML, and NLP will not be in automated investment decisions, but rather in generating the inputs that allow the investment firms to monitor ESG performance with improved granularity and completeness. There is wide array of potential application streams, let’s list some of the most relevant: ESG performance descriptive exploration paths that might walk the multiple stakeholders through the complete narrative behind what ESG performance looks like within and across all the multiple facets that compose this multidimensional indicator; Imputing ESG data and ratings for currently unmeasured companies by detecting patterns within the full set of ESG metrics; Using NLP algorithms to infer crucial ESG information such as market sentiment/ public opinion around specific ESG domains or parsing and extracting relevant unstructured ESG data; Understanding of causal relationships and, hence, materiality between traditional financial metrics (e.g., return on investment, price movements, investment risk) and non-financial ones and provide relevant forward projections about each one of these metrics, such as future asset performance.
Cloud-enabled regulation as-a-service solutions
Cognitive capabilities (AI and machine learning, natural language processing)
Use Case Summary
Measuring every cent that contributes to financial performance is normalized. The same thing must happen for measuring ESG performance. As companies continue to integrate responsible business practices into their operations, strategy, and supply chain considerations, ESG management will become a key standard and market differentiator in the investment industry. Companies that fail to promptly react to this trend, will be inevitably left behind and gradually lose market capital attractiveness.