The focus on responsible investment and Environmental, Social, and Governance (ESG) reporting has significantly increased in the last few years. Investors now incorporate ESG performance and engagement into their long-term investment decisions and portfolio management strategies. This helps them recognize ESG considerations’ significant impact on investment outcomes and sustainability
Measuring ESG performance is challenging for fund managers because traditional data sources are often fragmented and time-consuming. Due to this, fund managers face many challenges in accessing accurate, credible, and consistent ESG data. The client wanted to empower fund managers with comprehensive ESG data and scoring capabilities.
Let’s explore how we partnered with a European fintech firm and helped them to develop an AI/ML-powered SaaS-based ESG compliance platform. To make every step completion and decision-making real-time, data-driven in order better portfolio risk management and regulatory compliance.
Project Overview
A European Fintech organization was on the lookout for a reliable development partner to build an AI and SaaS-based ESG investment compliance application. The client’s goal was to support fund managers with a reliable platform to automate the end-to-end process of ESG data extraction, AI-powered scoring, portfolio monitoring, analytics, and reporting. This application will support them in measuring sustainability performance and managing ESG risks more effectively.
As a trusted SaaS development partner, Rishabh Software helped the client develop an AI/ML-powered SaaS solution for ESG investment compliance using AWS lambda functions, implemented in Node.js to provide a robust and scalable platform for ESG reporting. We embedded intelligent automation and machine learning capabilities to process unstructured ESG data, improve scoring accuracy, and offer predictive analytics that support real-time risk identification.
Challenges
- Primary POC lacked enterprise-grade features and scalability
- Manual data entry led fund managers to errors and inefficiencies, risking data integrity and revenue potential.
- Siloed and unstructured ESG data management across multiple systems restricted transparency and impacted data integrity.
- Non-adherence to ESG risks endangered financial stability, operations, and reputation.
- Lack of intelligent analytics to uncover hidden ESG risk patterns from historic and real-time datasets hindered proactive decision-making
Solution
To address the above challenges, we developed a web application with advanced analytics and an intuitive UI featuring separate portals for administrators and clients. This feature-rich admin portal allowed the admin to efficiently manage the application, user accounts, and other tasks. Also, the client portal enabled fund managers to register and make informed decisions seamlessly with the help of ESG scores.
The AI-powered, SaaS-based ESG investment compliance web application was built using a serverless architecture and a hybrid model, leveraging the following components:
Key Components of The Solution
Serverless Architecture
We used AWS Lambda functions in Node.js to develop the serverless application to handle requests and responses seamlessly. We leveraged DynamoDB to store and manage data. With our expertise, we reduced the need for server maintenance and improved scalability. In addition, this architecture facilitated real-time data ingestion pipelines and supported ML model deployment at scale.
Automated ESG Score Calculation
We developed Python-based scoring logic that uses predefined ESG factor weightages and AI-assisted rule sets to calculate scores dynamically. This was enhanced using a machine learning model trained on historical ESG performance data and policy benchmarks, which improved scoring accuracy and ensured alignment with evolving standards. Intelligent automation helped client business to reduce the manual effort that goes into calculating ESG scores and helped the end user generate the portfolio company’s ESG footprint in just a few steps.
Hybrid Development Model
Our expert team executed this project by combining the elements of cloud and on-premise development approaches. It helped the client leverage the benefits of both – the cloud for scalability and cost-effectiveness and the on-premise for more significant control over data security and customization. The hybrid model allowed our development team to build an efficient, scalable, and feature-rich investment compliance platform. Additionally, it also enabled the secure training and fine-tuning of ML models using sensitive ESG datasets while deploying APIs for real-time inference in the cloud.
AI-driven Automation
We developed a POC to implement AI-driven automation within the developed portal and questionnaire. Our solution helped the client automate the investment compliance process for corporate ESG data. The AI modules used natural language processing (NLP) to interpret ESG policy documents and risk disclosures and populated compliance forms automatically, significantly minimizing manual review.
The solution collectively formed a comprehensive and user-friendly application that enabled investment managers to access and utilize ESG data effectively. This ESG management solution also helped reduce manual effort and improve scalability.
Benefits
- 85% improvement in fund manager’s satisfaction rates
- 80% reduction in manual efforts
- 70% ESG-related risks reduction
- 90% reduction in the complexity of ESG reporting
- 85% increase in overall operational efficiency
- 95% accuracy achieved in AI-based ESG document classification and scoring
- 60% faster identification of ESG outliers through predictive anomaly detection models
Customer Profile
A leading Europe-based FinTech organization that provides Investment compliance solutions.
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