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@article{209001,
        author = {Kunal Urdhwareshe},
        title = {The Future of Income Assessment in Digital Lending},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {5},
        pages = {96-110},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=209001},
        abstract = {Income is the primary determinant of repayment capacity, yet India assesses it through documentary artefacts — salary slips, Form 16, income tax returns and manually reviewed bank statements — designed for a formal economy that a majority of Indian earners do not inhabit. This paper argues that income assessment is undergoing a structural transition from point-in-time verification, through analytical assessment, to continuous income intelligence: a probabilistic, explainable and continuously refreshed view of a borrower’s sustainable debt-service capacity. It examines the limitations of documentary underwriting; proposes a five-dimensional construct of income comprising level, stability, predictability, resilience and trajectory, consolidated into an Income Intelligence Score reported with explicit confidence; and assesses the enabling technologies of machine learning, explainable artificial intelligence and generative artificial intelligence. It evaluates India’s Account Aggregator framework, which has crossed 45 crore cumulative consents and 500 crore data fetches and received a formal self-regulatory anchor in 2026, alongside the Open Credit Enablement Network as the protocol layer for cash-flow-based credit. The paper analyses emerging data sources and their limitations, the regulatory perimeter established by the Reserve Bank of India (Digital Lending) Directions, 2025 and the Digital Personal Data Protection Rules, 2025, and the fairness risks inherent in income modelling. Ten case studies from India and comparable markets, three scenarios for 2030, and stakeholder-specific recommendations are presented. The central conclusion is that the binding constraint on inclusive credit growth in India is no longer algorithmic capability or data availability, but institutional willingness to rewrite credit policy around probabilistic income and to build the governance that makes such policy defensible.},
        keywords = {Account Aggregator, alternative data, artificial intelligence, cash-flow lending, credit risk, digital lending, financial inclusion, income intelligence, MSME finance, open finance, responsible AI.},
        month = {October},
        }