Cashberry provides a digital Merchant Cash Advance platform for Italian micro-enterprises that struggle to obtain short-term credit from traditional banks. Its RAYS® platform integrates with banks, acquirers, and marketplaces, using machine learning on POS cash-flow data to automate credit assessment and repayment through a percentage of future receivables.
Funding
Funding not disclosed
Founders
Product
Problem
Micro-enterprises, which represent 95.2% of active Italian businesses, have short-term credit needs that the traditional banking system struggles to meet. These small businesses often lack the collateral, credit history, or administrative capacity required for conventional bank loans, leaving them without timely access to working capital.
Solution
Cashberry offers a fully digital Merchant Cash Advance (MCA) solution built around the payment flows of small businesses. The RAYS® platform integrates natively with banks, acquirers, marketplaces, and digital payment providers, enabling them to offer credit to their merchant clients without taking on credit risk or regulatory burden. Using machine learning models developed with the Fintech Lab of the University of Pavia, RAYS® analyzes recent POS cash-flow data to assess creditworthiness and automate the lending process. Merchants select an advance amount and a percentage of future receivables to be deducted automatically, with a clear forecast of how the credit will impact their future cash flow.
Target Audience
Primary customers are Italian banks, acquirers, marketplaces, and digital payment providers that serve micro-enterprises, as well as the small business owners themselves who need fast, accessible short-term credit.
Features
- Microservices-based architecture that allows full customization of platform content and workflows for each distribution partner
- Dedicated platform installation per partner, enabling a tailor-made approach to deployment and branding
- Open-source technologies and a proprietary API engine that ensure seamless integration with partners' existing systems
- Machine learning credit-scoring models that analyze POS transaction flows to automate credit evaluation and underwriting
- End-to-end digital user experience that lets merchants select an amount, choose a repayment percentage, and view projected cash-flow impact in a few steps
- Built-in controls and compliance management that shield distribution partners from regulatory and credit risk