E-Wallet Enhances Financial Inclusion with AI-Powered Credit Scoring Using Remittance and Transaction Data
Client Background
A major e-wallet provider in ASEAN sought to introduce microcredit and overdraft facilities for users with irregular income streams, particularly migrant workers and gig economy participants. However, the lack of traditional credit history for many users posed a significant challenge in determining creditworthiness.
Challenges
The e-wallet provider needed a robust risk scoring solution to：
1
Leverage alternative data sources such as remittance history, top-up behavior, and transaction patterns.
Verify remittance behavior, including whether inflows and outflows followed a predictable cycle (e.g., salary remittances, family support, or irregular transfers).
2
Assess bill payment behavior, frequency, and type (e.g., recurring utility bills vs. discretionary spending).
Identify social and financial connections through transaction networks, including peer-to-peer transfers, refunds, and shared expenses among family and friends.
3
Enable instant micro-loan approvals for eligible users without requiring manual underwriting.
Balance accessibility with risk management to prevent high default rates.
Ensure compliance with evolving regulations on digital lending and consumer protection.Support scalability as the product expanded across multiple ASEAN markets.
The Mindigital Solution
Mindigital deployed a custom AI-powered risk scoring engine designed to leverage transaction-based behavioral insights for real-time lending decisions.
Key Features of the Solution
Transaction-Based Credit Score
Assessed spending patterns, cash inflows, and remittance activity to determine borrowing capacity
AI-Driven Behavioral Modeling
Identified high-trust users based on financial discipline, repayment behaviors, and social financial interactions.
Remittance Cycle & Stability Analysis
Verified whether remittance inflows and outflows followed a consistent schedule (e.g., monthly salary payments, weekend family support) or showed erratic, high-risk behavior.
Bill Payment & Spending Habit Assessment
Evaluated whether users regularly paid essential bills (e.g., rent, electricity, tuition) or primarily engaged in discretionary spending.
Social & Financial Network Profiling
Analyzed transaction links between family members and friends, detecting risk signals from shared financial responsibilities, peer-to-peer transfers, and refunds.
Seamless Lending Workflow
Fully integrated within the e-wallet app for frictionless loan application and disbursement.
Smart Risk Segmentation
Categorized users into dynamic risk bands to offer personalized credit limits.
Automated Collections and Repayment Reminders
Reduced delinquency rates through proactive engagement.
Regulatory-Ready Compliance Framework
Ensured adherence to lending policies in each operating market.