This course covers Alternative Data Usage Risk, which involves assessing the risks associated with using alternative or non-traditional data sources within the Agri & Rural Commercial Credit credit workflow. It focuses on evaluating the reliability, authenticity, consistency, and decision-making implications of alternative data such as mobile transaction records, satellite imagery, utility payments, digital behavior, informal market information, social indicators, and other non-conventional borrower assessment inputs commonly used in rural and agricultural lending environments. The course emphasizes understanding how dependence on alternative data may introduce risks related to misrepresentation, incomplete verification, inaccurate borrower profiling, biased assessments, and weak credit judgement if not independently validated. It evaluates key dimensions such as misrepresentation risk, sector risk assessment, collateral evaluation, and sustainability of rural and agri-enterprise lending, with each requiring independent validation and documented rationale before any credit action is finalized. It is distinct from broader portfolio diversification strategy, as it focuses specifically on structured identification, assessment, escalation management, and breach response related to data reliability, fraud exposure, and decision-quality risks arising from alternative data usage in agri and rural credit portfolios, while portfolio diversification strategy addresses wider portfolio allocation frameworks, concentration management, strategic sector balancing, and enterprise-level risk optimization with separate evidence standards, ownership, and approval authority. Within Fraud, Misrepresentation & Data Quality, the senior credit leader sets portfolio limits, governs exception criteria, and drives strategic alignment across the Agri & Rural Commercial Credit function, shaping escalation scope and portfolio-level priorities.