UTU unveils Creditworthiness API to improve accuracy of credit assessments for lenders

By Pavithra R

UTU, a decentralized trust infrastructure provider building new models of digital trust via artificial intelligence and blockchain has announced the launch of its Creditworthiness API to improve the accuracy of credit assessments for financial institutions and decentralized finance (DeFi) platforms globally.

Benefits for banks, microfinance institutions and decentralized peer-to-peer lending platforms include increased loan volumes and decreased default rates. Additionally, businesses can also reach customers that lack credit history in new markets.

By leveraging machine learning and artificial intelligence, the UTU Creditworthiness API allows institutions to monitor loans and determine the creditworthiness of both individuals and businesses by analyzing a variety of user-permissioned data points. These include historical financial information and new repayments that provide the necessary interplay to train models of high accuracy and precision, empowering lenders to make decisions with a high level of confidence.

“Financial transactions are all about trust. When customers trust service providers, their loyalty and patronage grows stronger. Banks and lenders in general can only deliver confidently when they trust that the borrower will be able to pay back. We can’t wait to scale the Creditworthiness API to serve more people and bridge the gap between traditional finance, digital banking and DeFi,” said Jason Eisen, CEO and Co-founder of UTU.

Lenders can upload historical and other data, allowing the machine learning model to discover and learn from latent patterns in loan repayment that can then be applied to decisions on new applications. The firm’s model continuously analyzes new repayment data to increase accuracy and performance, adapting to evolving borrowing patterns in its prediction. The Creditworthiness API identifies trustworthy borrower profiles and those that are at risk of default through this dynamic model. It leverages on- and off-chain data and social graphs for contextual relevance, providing qualified borrowers with the opportunity to access funding with less collateral.

Additionally, the Creditworthiness API will work along with UTU’s M-PESA Parser API to allow lenders in Africa to easily extract data from their customers’ M-PESA statements. Lenders can view this information on a dashboard and incorporate it into the credit scoring model.

UTU is also building the first socially-powered credit assessment feature through which businesses can leverage social network connections to add more data to credit assessments. This extra layer of trust is expected to differentiate the Creditworthiness API from typical credit scoring systems.