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@article{196951,
        author = {Harshit Tyagi and Harsh Kumar Thakur and Aditya Gupta and Keshav Dixit and SHASHI KANT MOURYA},
        title = {Detecting Systemic Risk in Informal Finance: An Explainable AI and Agent-Based Modeling Approach for Chit Funds},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {6387-6392},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196951},
        abstract = {This paper presents ChitEase, an AI-driven behavioural simulation framework designed to model, detect, and predict emergent financial risks within chit-fund ecosystems. The system integrates Agent-Based Modeling (ABM) with Machine Learning (ML) techniques such as Random Forest, XG Boost, and Light GBM to simulate behavioural interactions among participants and predict collective risk outcomes. Unlike deterministic economic models, ChitEase simulates emergent risk arising from agent behaviours particularly trust, greed, and digital literacy which evolve dynamically through peer contagion effects. The synthetic dataset, containing 200,000 agents, is validated against industry-standard AI fraud-detection benchmarks, achieving comparable accuracy (0.95–0.99) and ROC-AUC (0.96–1.00). The study is empirically verifiable, as all simulations and models can be independently reproduced, cross-validated, and tested for statistical consistency. This work represents the first step toward a unified AI Risk Intelligence Framework for transparent, explainable, and compliant chit-fund management in India.},
        keywords = {Agent-Based Modelling (ABM), Financial Risk Simulation, Explainable AI (XAI), Random Forest, XG Boost, Light GBM, Chit Funds, Fraud Detection, Behavioural Modelling, Empirical Verification, SaaS Analytics.},
        month = {April},
        }