Initial Situation: Companies are increasingly required and incentivized to engage in Environmental, Social, and Governance (ESG) practices. This involves assessing reports of ESG rating agencies for weaknesses, information requirements, and improvement potentials, and is especially of relevance for large financial institutes for demonstrating their contribution to sustainable business practices. However, assessing reports and deriving recommendations for action with which ratings can be improved is a manual and time-consuming activity, and leaves potential for leveraging technological means for more impactful and efficient engagement with these reports unused. To enable the development of data-driven (semi)automated tools for supporting this assessment, especially by means of Artificial Intelligence (AI), it is first necessary to understand how ESG experts assess reports and derive recommendations, and what requirements they have vis-à-vis a supporting tool.

Goal: The goal of this thesis is 1) to assess requirements that ESG experts in a large German financial institution have for a tool that supports their ESG management practice, and 2) to capture their current practice of engaging with ESG reports to derive potential for automating and supporting steps therein. Of particular relevance is also to understand how ESG experts integrate context information for a given report, and how such integration may be supported by automated, technical means. With semi-structured interviews and potentially other qualitative methods, this thesis identifies and thus provides a catalogue of requirements and procedure model based on which a technical solution for supporting ESG rating management can be implemented in the future. This will allow organizations to more impactfully engage with ESG rating reports.

Application: If you are interested, please send your current transcript of records together with a brief statement of motivation. Interviews will likely be held in German, so working knowledge of German is a requirement.