This course covers Alternative Data Usage Risk, which involves assessing the risks associated with using alternative data sources within the Agri & Rural Commercial Credit credit workflow. It focuses on evaluating how non-traditional information sources such as mobile transaction data, satellite imagery, social indicators, utility payments, digital activity records, local references, agri-platform data, and informal market information may influence borrower assessment, credit decisioning, and exposure monitoring in rural lending environments. The course emphasizes structured validation of data reliability, relevance, authenticity, consistency, and ethical usage to ensure that alternative data supports sustainable and defensible credit decisions without increasing the risk of misrepresentation or incorrect borrower evaluation. 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 quality, alternative information reliability, borrower verification challenges, and exposure assessment integrity within 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 credit analyst executes the assessment, completes documentation, and flags exceptions for manager review within Agri & Rural Commercial Credit credit files, shaping escalation scope and operational priorities.