In the United States, although ESG disclosures haven’t yet been formally regulated, recent legislation passed by the House of Representatives requires public companies to submit their annual and quarterly ESG reports.
AI Drives Modern-Day ESG Audits
Use Case: How an Audit Firm Leveraged AI to Ensure ESG Readiness for Its Clients
Let’s take you through a use case to give you an overview of how the most complex Requirement Completion Scoring Process becomes seamless and accurate when powered by AI –
- The documents presented by clients were very extensive and in diverse formats
- Information mining was tedious and time-consuming
- The exact match of what the practitioner was expecting was often unavailable
- It was a challenge for practitioners to gather information in a consistent manner
- There was no way to create cross-references or set protocols to identify/rectify similar document inconsistencies in the future
- Practitioners had to note down valuable data presented in the form of diagrams (charts/ infographics/ tables etc.) manually and later correlate
- There were inconsistencies in scoring between two auditors of the same firm
- Comparing multiple document sets and drawing inferences from historical data was a challenge. This meant repeated auditing efforts to measure client compliance every time.
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7 Benefits of AI in ESG Audits
1. Automated Information Mining
- The tedious process of information mining is transformed into an automated, seamless, and far more accurate process.
- The practitioners no longer have to spend hours reviewing piles of documents to find what they were looking for.
- All they need to do is upload the bulk of CSR documents, and the ML algorithms present top matching paragraphs along with their “matching confidence scores” and the overall confidence score (aggregate of individual scores).
- The practitioners can then ‘Preview’ the document to cross-verify facts, approve/reject suggestions, and even key in their suggestions.
- The Human-AI Feedback Loop combines all these inputs, re-calculates the overall confidence score, and delivers accurate results.
2. Actionable Intelligence
- The AI-powered ESG Audit Platform empowers the practitioners with “actionable intelligence”, presenting matching phrases from the CSR documents and delivering a data-driven confidence score.
- Practitioners can add information from their subject matter expertise to ML suggestions – the platform uses it to improve algorithm performance in the future.
- Practitioners can review, accept, or change the completion score while reviewing disclosure status to drive a consistent, optimal Engagement Requirement process across all engagements.
3. Information Extraction from Images and Tables
- The intelligent system is also capable of extracting valuable data embedded within images – diagrams, infographics, and tables – across CSR documents.
- During the manual process, the practitioners have to spend a lot of time going over large documents, identifying information embedded in such images, and noting down the important points presented in the pictures (charts/graphs/tables, etc.).
- This information embedded in images contributes greatly to the decision-making process and improves audit quality.
4. Assisted Scoping
- The manual process results in inconsistent scoping by different practitioners for clients in the same industry. This is detrimental to the audit firm’s reputation.
- The automated system allows practitioners to upload historical CSR documents. The ML API parses these documents for all Standards, Topics, Disclosures, and Requirements and reduces the practitioner’s time/effort in going over a large volume of documents to find out topics, disclosures, and requirements during the ‘Scoping’ process.
- Practitioners are able to review scoping suggestions easily and approve selections and take further actions.
- The result is a Faster Turn Around Time of Engagement Scoping Process, and reduced repeated manual tasks for practitioners.
- The AI model ensures a consistent and automated scoping process across different practitioners.
- The modern system also allows the practitioner to change/update information manually if needed.
5. Entity Recognition for Historical Insights
- Auditors often want to understand the trend of CSR compliance of their clients from historical data presented to them. Manual analysis is a humongous task, while an AI-powered ESG Audit Platform very efficiently analyzes a bulk of documents to present past engagement statistics.
- The platform extracts relevant information using entity recognition, stores all the extracted data in an analytics repository (Synapse/SQL DB/Cosmos, etc.), and reports a trend of compliance and information over a period via a visualization tool.
- This gives the auditors far better actionable intelligence, enriches client interactions, and improves the overall quality of service delivery.
6. Prediction of Compliance Probability
- To complete the scoring process, auditors must measure client compliance for every requirement. The manual process for this is intensive, time-consuming, and must be repeated every time a client undergoes an audit.
- The AI system uses past engagement information and data to provide insights to auditors.
- Machine learning clusters and identifies ideal compliance measures for client types based on industry, asset types, etc., and this significantly improves the audit quality.
- The system further predicts the probability of compliance with individual requirements and provides a ranking order of requirements that the auditors need to focus on for the current audit.
- It also allows the practitioners to provide feedback on the validity of the prediction and uses that feedback loop to improve models.
7. Interactive Q&A Engine for Intelligent Searching
- Auditors often have questions and in the absence of an automated system, they have to go through all the CSR and other documents to find answers.
- The Q&A API allows the practitioner to ask questions in free flow text and provides contextually matching answers.
- The ready reckoner system enables practitioners to quickly search and retrieve contextual answers for questions asked.
- The system can be trained to analyze multiple types of documents.