Swarm learning based credit scoring for P2P lending in block chain

Conventional loan avenues generally focus more on the formal sector than the unbanked sector. A peer to-peer (p2p) lending platform built on blockchain can help bridge the gap between potential lenders and borrowers in need of money in a secure and decentralized environment. The Ethereum blockchain allows for the creation of smart contracts to perform actions in the network by setting logic rules and conditions thereby removing the need for middlemen and can be inclusive of the unbanked sector. The p2p platform introduces swarm learning for credit scoring, which is a novel methodology that utilizes smart contracts to train decentralized machine learning models. Each training round happens on the local device with the user data, which then exchanges the training parameters and weights to the machine learning model maintained in the smart contract. This allows for preserving the privacy of the user data by ensuring the data never leaves the device but only the inference does. Upon analyzing the user’s behavior, a statistical credit score is assessed for validating the chances of the user to default his/her loan repayment. The performance of the proposed model that has been trained using the swarm learning technique is close to the model that had been trained in a centralized environment while overcoming the drawbacks of federated learning by incorporating blockchain and swarm methodology.