AI ESG Monitoring and Fraud Prevention in Trade Finance Challenge

Introduction

In today's world, where sustainability and responsible business practices are paramount, the "AI ESG Monitoring and Fraud Prevention in Trade Finance Challenge" emerges as a crucial initiative. The goal is to revolutionize ESG monitoring, trade finance digitization, and fraud prevention using Artificial Intelligence (AI) and Natural Language Processing (NLP) technologies. This project addresses the pressing need for more inclusive and accurate assessments of Environmental, Social, and Governance (ESG) factors, especially for small and medium-sized enterprises (SMEs), and tackles the issue of misleading "green" claims (greenwashing).

Problem Statement

This project aims to enhance the assessment of SME sustainability, uncover misleading "green" claims, and ensure safer trade practices. This project promises to demonstrate better decision-making, increase corporate honesty, and safer trade processes. The ultimate goal is to craft an AI-driven solution that brings transformative changes in three main areas:

1. ESG Monitoring: Provide a holistic evaluation of companies by measuring their ESG factors and addressing the lack of data for smaller entities.
2. Greenwashing Detection: Develop a model with 85% accuracy to identify deceptive sustainability claims.
3. Digital Identity Scoring: Create a robust AI model for learning and scoring the digital identity of SMEs.

Approach & Methodology

1. Data Exploration: Identify and integrate new data sources from public internet sites, GDELT news archive, company websites, LinkedIn, and sustainability reports.
2. Data Collection & Processing: Utilize automated pipelines to download and temporarily store data, including news article, website & LinkedIn stats, and processed sustainability reports.
3. Extract Financial & Sustainability Metrics: Apply advanced AI and NLP techniques to generate text insights, digital scores, and ESG scores from the data collected. Develop a fine-tuned classification NLP model to distinguish genuine sustainability claims from deceptive ones (greenwashing).
4. Data Storage: Store processed insights in a permenant database.
5. User-Friendly UI: Develop a dashboard to display the ESG Scores, Greenwashing Probability, Digital Identity scores, NLP derive actionable insights, and sentiment trends over time of each company.

Conclusion

This project is set to bring transformative changes in ESG monitoring, greenwashing detection, and digital identity scoring, ultimately leading to better decision-making, increased corporate transparency, and safer trade processes.