FinCop: A Smart Digital Platform for Cooperative Societies with AI-Based Trust Scoring

Author

Karthikeya Barnala, Bhavana Chappa, Sesidhar Kamodula, Venkatesh Algubelli, Mrs. Anusha Tanna

Abstract

 Cooperative societies provide important financial and community services to farmers, small workers, and rural households. However, paper-based records and disconnected digital systems can make member management, loan processing, grievance handling, and scheme access difficult to monitor. This paper presents FinCop, a proposed cloud-enabled digital platform that integrates cooperative member services with AI-assisted financial assessment.

The platform is designed to support digital registration, member and staff management, loan-application tracking, grievance handling, financial record management, and administrative dashboards. Its central component is a Cooperative Trust Score intended to consider contextual indicators such as income or crop yield, debt-to-income ratio, repayment history, asset ownership, and other relevant member information.

The proposed technical design combines a React and TypeScript frontend, Node.js and Express services, a central data layer, and machine-learning components for repayment-risk prediction and anomaly detection. XGBoost and explainability methods such as SHAP are proposed to support risk assessment and make individual predictions more interpretable, while Isolation Forest is considered for identifying unusual patterns. This paper describes the motivation, related research, system design, methodology, and evaluation plan. Quantitative performance is not claimed because validated experimental results are not provided in the supplied project material.

Keywords

Cooperative societies, digital governance, trust scoring, credit-risk prediction, explainable AI, XGBoost, anomaly detection, rural financial inclusion.

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References

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