šŸ’”INTRODUCING

ESG AI Innovation Challenge

Over an 8-week span, this challenge aims to revolutionize ESG monitoring, trade finance digitization, and fraud prevention using AI and NLP technologies.

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The Problem Statement

Why are we doing this?

We aim to improve how we assess company sustainability for financial solutions, expose misleading “green” claims, and make trade safer for our planet. Our project promises better decision-making, increased corporate honesty, and safer trade processes. Join us in shaping a future where businesses are transparent and responsible.

The Ultimate goal is to craft an AI-driven solution that brings transformative changes in three main areas:

The Ultimate goal is to craft an AI-driven solution that brings transformative changes in three (3) main areas:

ESG stands for Environmental, Social, and Governance factors, often used to measure sustainability. Our goal from this project is to evaluate both big and small companies on these criteria, giving a broader view of their sustainable practices. The current focus on large public companies overlooks smaller entities, limiting a holistic understanding of sustainability impacts. Key takes here for us are the lack of data and scoring.

Some companies claim to be green without truly being sustainable, a practice called greenwashing. Our goal is to create a solution that will detect and highlight these misleading claims, promoting genuine transparency.

ESG for smaller buyers, sellers, factories farms, and SMEs and digital identity
ESG criteria or the lack of this monitoring are important considerations for businesses of all sizes, including smaller buyers, sellers, factories, farms, and SMEs (small to medium-sized enterprises). However, implementing ESG practices and monitoring them can be a challenge for smaller entities due to limited resources. Digital identity can play a pivotal role in this context

ESG criteria are increasingly central to the investment decisions of global asset managers. They consider the sustainability and ethical impact of investments. Yet, many globally traded assets lack comprehensive ESG integration, which poses significant risks and challenges, especially for achieving global targets such as net zero on goods, In short, you want to know if the bananas you purchased are organic and did not involve child labor, this is paramount to retail as well as the financiers funding the trade if we are to have a more conscious planet and save it from climate change.

AI has high energy consumption primarily because modern deep learning models have billions of parameters that require intensive computations, especially when processed using power-hungry GPUs and TPUs. Training these models involves multiple iterations over vast datasets, often for extended periods. Additionally, practices like redundancy, fine-tuning, and hyperparameter searches multiply the energy demands, making the overall consumption notably high.

The Problem Statement

Why are we doing this?

We aim to improve how we assess company sustainability for financial solutions, expose misleading “green” claims, and make trade safer for our planet. Our project promises better decision-making, increased corporate honesty, and safer trade processes. Join us in shaping a future where businesses are transparent and responsible.

The Ultimate goal is to craft an AI-driven solution that brings transformative changes in three main areas:

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project output

Deliverables

The goal is to foster a transparent ecosystem, empower stakeholders with comprehensive insights, and promote genuine sustainability and secure financial practices.

Data Repository

A comprehensive database integrating the initially suggested data sources with at least three new ones identified during the exploration phase.

ESG Scoring Algorithm

A working AI model capable of processing diverse datasets and assigning ESG scores to entities, assets, and logistics with preliminary results on at least 100 entities and give a score in each designated category of 0-100

Greenwashing Detection Model

A deployable AI model trained to distinguish genuine sustainability claims from deceptive ones with a demonstrated accuracy of 85% or higher.

Digital Identity Scoring Solution

A strong model for AI learning digital identity on the SME 100 entries.

An AI blockchain Hybrid

A useable low-energy AI model for use with the rest of the solutions herein

Text Insights Report

A detailed report containing actionable insights derived from at least 10,000 textual data points using NLP techniques.

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Project Goal

Develop a tech-driven approach to elevate ESG monitoring, digitalize trade finance, and enhance fraud prevention. Our focus is to create tools and methodologies that will foster a more efficient and transparent business ecosystem.

Identify and integrate at least three new data sources beyond the initially suggested ones.

Implement AI to process diverse datasets, and create algorithms that can assign ESG scores to at least 100 entities based on supply chains and manufacturing processes. Current standardizations currently consist of evolving SASB, UN SDG and ICC frameworks (we aren’t here to create standards, only focus on data scoring. We are building to include, not exclude.

Develop an AI model that achieves at least 85% accuracy in distinguishing genuine sustainability claims from deceptive ones.

Utilise enhanced data points public and paid, and nonconventional screening including historical footprints and ESG screenings to create and evolve the digital identity

Using hybrid blockchain aims to reduce AI’s energy consumption by optimizing resource allocation and minimizing redundancies. This combined approach ensures efficient AI computations and provides transparent, verifiable energy use records, promoting sustainable AI practices.

Use NLP to derive actionable insights from text data, aiming to process and categorize a minimum of 10,000 textual data points.

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Scope

  • Identify and assess potential new data sources.
  • Collect, clean, and integrate data into a unified repository.

AI Model Development for ESG Scoring on assets, SMES medium buyers, and logistics for net zero:

  • Process data, including satellite imagery.
  • Train and test an algorithm to assign ESG scores based on counterparties, sources, supply chains, and manufacturing processes.
  • Curate a dataset of genuine versus deceptive sustainability claims.
  • Train, test, and refine the AI model to achieve the desired accuracy.
  • Identify and assess potential new data sources.
  • Collect, clean, and integrate data into a unified repository
  • Seek blockchain hybrid for low carbon footprint, low energy consuming AI
  • Process and categorize the text data.
  • Extract and document valuable insights on financial and sustainability aspects.

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Advisory Board, Partners & Endorsements

The goal is to foster a transparent ecosystem, empower stakeholders with comprehensive insights, and promote genuine sustainability and secure financial practices.

Andre Casterman

Casterman Advisory

Lee Tarone

CEO, Voy Finance

Dave Hopkins

COO, Voy Finance

Steven Matthew

Advisor

Andrea Frosinini

Advisor