Amanah – BNPL Buy Now Pay Later

Amanah BNPL offers a Shariah-compliant, interest-free digital installment solution that empowers consumers to shop ethically and responsibly. The use of advanced technologies like Blockchain, AI, and ML ensures security, personalization, fraud detection, and real-time decision-making in the credit and transaction process. By integrating Blockchain, AI, and ML, Amanah BNPL can offer a secure, scalable, ethical, and highly intelligent alternative to conventional credit systems. This fusion not only increases customer trust and operational efficiency but also aligns with Islamic finance principles and the digital future of commerce

BNPL Blockchain Applications

Decentralized Credit Ledger Securely store users' BNPL history and transactions on a tamper-proof, transparent ledger.

Improves trust with merchants and regulators, especially in Islamic finance.

Smart Contracts for BNPL Terms Automatically execute and enforce payment schedules (e.g., 3-month, 6-month, 12 months plans) based on predefined Policy.

Reduces administrative costs and disputes.

Identity Verification & KYC/AML Blockchain-based identity management ensures secure onboarding and verification without compromising privacy

BNPL Artificial Intelligence Applications

Credit Risk Assessment AI evaluates customer spending behavior, income data, and purchase patterns in real-time to determine BNPL eligibility.

Personalized Offers & Limits Dynamic credit limits based on user profiles and transaction trends.

Fraud Detection & Anomaly Tracking AI algorithms detect suspicious activity or synthetic identities quickly, minimizing financial loss

BNPL Machine Learning Applications

Behavioral Analytics & Repayment Prediction ML models analyze past repayment data to predict future repayment capacity and risk.

Dynamic Risk-Based Pricing (for merchants) Suggest merchant-specific fees based on repayment behavior and fraud risk without charging customer’s interest.

Continuous Learning Models ML improves accuracy over time by learning from customer feedback and repayment patterns.