MUMBAI, India, Sept. 22 -- Intellectual Property India has published a patent application (202631108917 A) filed by Vidyasagar University on September 10, 2026, for A System And Method For Machine Learning-Enabled Assessment And Flexible Management Of Microfinancial Inclusion.
Inventor includes Prof. Debasish Biswas.
The application for the patent was published on September 18, 2026, under issue no. 38/2026.
Abstract: ABSTRACT OF THE INVENTION The present invention discloses a system and method for machine learning-enabled assessment and flexible management of microfinancial inclusion. Traditional microfinance approaches rely on rigid eligibility criteria, limited formal data, and standardized products, resulting in both exclusion of creditworthy but undocumented clients and elevated portfolio risk when livelihood realities are ignored. The invention addresses these shortcomings through an integrated technological platform. The system ingests heterogeneous data streams—including KYC and demographic records, alternative credit signals (utility payments, mobile and UPI transactions), Self-Help Group performance histories, geospatial and climatic indicators, and self-reported livelihood information— under privacy-preserving protocols. An ensemble of machine-learning models generates multi-dimensional scores that capture not only conventional creditworthiness but also financial capability, vulnerability, and developmental potential. Explainable AI techniques produce feature attributions and counterfactual insights, while a dedicated fairness module continuously monitors predictions for disparate impact across gender, region, and other protected attributes. A constrained product- configuration engine translates these assessments into personalized yet policy-compliant microfinance offerings. Clients are presented with flexible options regarding loan size, tenure, repayment frequency (including seasonal or step-up schedules), grace periods, and complementary services. Human-in-the-loop oversight is retained for high-stakes or borderline decisions, with full audit logging. Post-disbursement, a continuous monitoring layer tracks repayment behavior, cash-flow proxies, and exogenous shocks to compute predictive early-warning scores. Differentiated adaptive interventions—ranging from automated payment holidays to structured restructuring or intensified support—are triggered proactively. Client outcome data (income trajectories, asset accumulation, empowerment indicators) are captured and fed back into model retraining, creating a closed learning loop that aligns institutional performance with genuine inclusion impact. The architecture is modular and interoperable, supporting deployment by banks, NBFC-MFIs, SHG federations, and fintechs, with offline capability for low-connectivity environments. By unifying multi-dimensional assessment, flexible productization, predictive adaptive management, explainability, fairness governance, and impact feedback, the invention enables responsible expansion of microfinancial services, improved portfolio quality, and measurable developmental outcomes. The method comprises sequential and iterative steps of data acquisition, scoring, product generation, decisioning, monitoring, intervention, and model updating. The system and method thereby constitute a significant advancement over existing credit-scoring engines and digital lending platforms that address only isolated aspects of the microfinancial inclusion challenge.
Disclaimer: Curated by HT Syndication.
