Objectives:

Upon completion of this course, participants will be able to:

• Analyze the core challenges of credit risk assessment and the **information asymmetry** inherent in lending to the unbanked.
• Evaluate and apply **alternative data sources** (e.g., mobile money transaction data, utility bills, digital footprint) for credit scoring.
• Design and implement **inclusive credit scoring models** (e.g., psychometric scoring, machine learning) while maintaining predictive power.
• Ensure full compliance with **Fair Lending and anti-discrimination laws** when using non-traditional data for underwriting.
• Understand the mechanics and risk management of non-traditional credit methodologies, such as **group lending** and character-based credit.
• Develop proportional loan product design, including appropriate collateral, repayment schedules, and interest rate setting for the unbanked.
• Manage the operational and fraud risks associated with digital credit and mobile lending to first-time borrowers.
• Understand the role and impact of **Credit Information Sharing (CIS)** and public credit registries in building the unbanked's credit history.

• Credit Underwriters, Analysts, and Managers in Microfinance Institutions and FinTech Lenders
• Data Scientists and AI/ML Engineers building Alternative Credit Scoring Models
• Risk Management and Compliance Officers specializing in Digital Credit
• Financial Inclusion and Product Development Specialists
• Regulators overseeing Digital Credit and Consumer Protection
• International Development Professionals focused on MSME and Retail Finance
• Bankers developing inclusive lending product lines

• Case Studies analyzing successful and failed digital credit deployments to the unbanked.
• Group Activities on designing a risk-adjusted interest rate structure for a micro-loan product.
• Discussions on the ethical and legal risks of using social media data for credit decisions.
• Individual Exercises on developing a scoring matrix using non-traditional data points.
• Role-playing a credit committee debate over a new alternative credit scoring model.
• Workshop on drafting compliant consent forms for alternative data collection.

Unit 1: The Challenge of Underwriting the Unbanked

• Defining the unbanked and the characteristics of a **thin-file/no-file** borrower.
• The limitations of traditional credit scoring (e.g., FICO) for inclusive lending.
• The risks: Adverse selection, moral hazard, and higher operational cost.
• The imperative: Safely and profitably expanding credit access to low-income markets.

• **Group Lending (Joint-Liability)** models and their risk management principles.
• Character-based lending and the role of local knowledge and community ties.
• The use of **collateral substitutes** and cash flow-based lending.
• Underwriting for Micro, Small, and Medium Enterprises (**MSMEs**).

Unit 2: Alternative Data and Inclusive Scoring

• Leveraging **Mobile Money Transaction Data (MMTD)** for cash flow analysis.
• Utilizing utility payment history, educational data, and geospatial information.
• The use of **Psychometric Scoring** and its predictive power and ethical considerations.
• Compliance with data privacy and obtaining informed consent for alternative data usage.

• Designing **Machine Learning (ML)** models for inclusive scoring while avoiding bias.
• Ensuring compliance with **Fair Lending** and anti-discrimination laws in alternative data use.
• Techniques for detecting and mitigating algorithmic bias in the underwriting process.
• The regulatory challenge of transparency and the "right to explanation" for credit denials.

Unit 3: Product Design and Risk Mitigation

• Designing flexible repayment schedules and short-term, low-value loan products.
• Setting appropriate interest rates and transparent fee structures (avoiding predatory lending).
• Strategies for effective, ethical debt collection and avoiding over-indebtedness.
• Integrating mandatory financial education with credit product disbursement.

Unit 4: Technology, Credit Bureaus, and Scaling

• The role of **Credit Information Sharing (CIS)** and the need for comprehensive data inclusion.
• Scaling underwriting through API integration and automated decision-making.
• Regulatory mandates for reporting positive and negative credit data on the unbanked.
• Managing the operational risks of fully automated, instant digital credit.