ESG Materiality Assessments Powered by AI

At the first touch point, our ESG solutions advisory and consulting services team focused on identifying the firm’s key pain points. Detailed assessment helped KANINI understand the company’s existing process flows and ensure the new solution seamlessly aligned with their existing audit systems.

Our team of experts then moved on to conceptualize an AI-powered ESG audit solution for smart ESG topic modeling and expedite the ESG materiality and disclosure assessment process for the auditors.

With the client in agreement, we then developed a proof-of-concept to demonstrate how the proposed AI solution would completely transform the existing legacy ESG topic mapping process and save the audit firms time and cost.

Here’s how our custom-built AI solution delivered the best results in ESG topics recommendation and mapping –

• Collection of Data: The proposed AI solution efficiently collected ESG data from all external and internal ESG reports, along with other diverse sets of ESG documents such as CSR reports, corporate disclosures, and regulatory filings.
• Model Training for Topic Recognition: These collected reports trained the AI model across various ESG subjects, industry-specific standards & expectations, relationships between datasets, and other such details to enable the solution to identify the various ESG topics and analyze the patterns in the data for insights. It also provided key insights to auditors using past aggregated engagement data. ​
• Topic Mapping: The intelligent model which was trained across these vast number of ESG topics promptly identified the most relevant ESG topic based on the industry and the client. It then automatically mapped the ESG topics to the most relevant ESG standards and frameworks such as GRI or SASB.
• Topic Scoring: The materiality assessment capabilities of the AI solution recommended the most suitable ESG topics to the auditors by assigning scores to each ESG topic based on its significance and impact. The AI model’s knowledge of industry-specific ESG risks, regulatory requirements, relevance and significance of each topic, and other such parameters helped in determining correct scores. It provided multiple insights on topics mapping and scorings.