Alternative Credit Scoring & Banking AI
A licensed Pakistani neobank targeting the 100M+ underbanked population struggled to serve customers with zero traditional credit history. Their manual underwriting took 14 days with a 73% rejection rate, while customer support was overwhelmed by 40,000+ monthly WhatsApp queries with an 11-hour response lag.
Implemented a multi-dimensional AI layer combining alternative credit scoring, real-time underwriting, and an Urdu-first LLM banking assistant.
Designed signals from mobile airtime patterns, utility regularity, and app behavioral data to build creditworthiness without traditional CIBIL scores.
Fine-tuned Llama 3 on local banking regulations and Urdu dialogue to create a fluent, compliant banking assistant for underbanked users.
Built a high-performance decision API using FastAPI and Kafka to integrate document verification (NADRA) and risk scoring into a 90-second flow.
Developed an internal platform for risk officers to monitor portfolio health, model drift, and default trends in real-time.
For returning customers, 4h for first-time
Approval increased from 27% to 61% for first-time
Reduction in defaults from 19% to 11%
Queries resolved by AI without human intervention
Advanced fine-tuning to handle phonetic Roman Urdu used widely in Pakistan, which traditional LLMs struggle with.
Ensemble models that derive stability and ability-to-pay from unconventional data points like ecommerce history.
Governance framework built to satisfy Pakistan's central bank AI transparency and security requirements.
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