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<p>Access to financial resources is broadly acknowledged as essential for fostering inclusive growth and enabling market participation. In developing economies, access is frequently limited by regulatory frameworks, inadequate financial infrastructure, and socioeconomic factors at the household level. North East India is endowed with a plethora of natural resources; nonetheless, it has been intrinsically lagging in industrialization and equitable regional economic development. According to statistics periodically supplied from various secondary sources, Assam is not an exception regarding this progress and the attainment of the Financial Inclusion goal. The Bodoland (BTR) Territorial Region of Assam lags significantly in this regard. Consequently, it is imperative to formulate customer-centric strategies that address their financial requirements, thereby incorporating them into the financial inclusion strategy through the anticipation of their possession of savings bank accounts. Moreover, advanced forecasting techniques in artificial intelligence, specifically machine learning techniques are beneficial pertaining to possession of savings bank account. These methods have been adopted in this work to develop a model that aids policymakers, government entities, and financial institutions in tailoring products and services to meet the needs of the populace. Random Forest model has 88.89% accuracy in predicting ownership of savings bank account. This performance is highly dominated by banked population and class imbalance. Mediation analysis shows that savings are an important, but not the only way that family income affects bank account ownership. The results emphasize the potential of AI-driven predictive frameworks to assist policymakers, regulators, and financial service providers in crafting customer-focused strategies that strengthen financial markets, increase household engagement, and mitigate regional inequalities. This study recommended that the financial service providers must switch from business models at the investment level.</p>

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Predicting Bank Account Ownership and Financial Inclusion Employing Machine Learning and Mediation Analysis: Evidence from Kokrajhar District of BTR, Assam

  • 期刊论文

  • 发表日期：

    2026年7月2日

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  • Bapan Kalita,
  • Sanjay Karmakar

摘要

Access to financial resources is broadly acknowledged as essential for fostering inclusive growth and enabling market participation. In developing economies, access is frequently limited by regulatory frameworks, inadequate financial infrastructure, and socioeconomic factors at the household level. North East India is endowed with a plethora of natural resources; nonetheless, it has been intrinsically lagging in industrialization and equitable regional economic development. According to statistics periodically supplied from various secondary sources, Assam is not an exception regarding this progress and the attainment of the Financial Inclusion goal. The Bodoland (BTR) Territorial Region of Assam lags significantly in this regard. Consequently, it is imperative to formulate customer-centric strategies that address their financial requirements, thereby incorporating them into the financial inclusion strategy through the anticipation of their possession of savings bank accounts. Moreover, advanced forecasting techniques in artificial intelligence, specifically machine learning techniques are beneficial pertaining to possession of savings bank account. These methods have been adopted in this work to develop a model that aids policymakers, government entities, and financial institutions in tailoring products and services to meet the needs of the populace. Random Forest model has 88.89% accuracy in predicting ownership of savings bank account. This performance is highly dominated by banked population and class imbalance. Mediation analysis shows that savings are an important, but not the only way that family income affects bank account ownership. The results emphasize the potential of AI-driven predictive frameworks to assist policymakers, regulators, and financial service providers in crafting customer-focused strategies that strengthen financial markets, increase household engagement, and mitigate regional inequalities. This study recommended that the financial service providers must switch from business models at the investment level.