KINTAI

Artificial intelligence model to categorise banking movements of SMEs and freelancers, detecting possible anomalies.

The KINTAI project builds the bases for an artificial intelligence and machine learning tool for calculating the level of risk and its monitoring, a basic requirement for granting a loan to a company.

KINTAI designs and implements models based on supervised learning that take into account the millions of bank transactions made by companies, and then efficiently group them into different categories identifying anomalies in cash flows and the customer portfolio.

This tool allows for the import, transformation and storage of any volume of data, making them accessible to the technologies that execute statistical analyses, the validation of the different models and other steps that make up the process of creating predictive models.

The end result of the project is a massive data storage and processing platform, as well as a series of tools validated in a productive environment.

Currently, the technology applied to risk analysis evaluates solvency at the specific moment in which clients apply for financing. In this sense, KINTAI’s innovation is based on quick and precise evaluation based on historical data, current status and future forecasts.

Eurecat participates in the KINTAI project through its Big Data & Data Science Unit, responsible for the development of the artificial intelligence model. The consortium is also made up of the company Kintai.