The client based in New York is a premier FinTech company combining aggregation, funding and a participation marketplace on a single platform. Founded in 2005, the company has provided more than $1.6 billion in financing to small businesses in a variety of industries across the United States.
What were their challenges?
Since they launched the company experienced 72% growth year on year in gross originations. As a result, the data that needed to be captured also grew tremendously, adding to complexities in analyzing and filtering risky loans from the overall pool of applicants.
Client g Client growth also added pressure on the existing process of loan approvals, which was predominantly manual in nature. The underwriting process needed to be streamlined and automated to exploit all the data, regardless of source, and perform real time analysis to predict loan success score prior to approval.
What was the proposed solution?
Saturn and its team of advanced analytics experts proposed SPSS Advanced Analytics as the solution to integrate all data streams and build a robust platform to predict risk for potential loans.
SPSS pSPSS provides univariate and multivariate modeling techniques to help users reach the most accurate conclusions when working with data describing complex relationships. These sophisticated analytical techniques are taken from machine learning, artificial intelligence and statistics and are frequently applied to gain deeper insights from data used in disciplines such as financial services, medical research, manufacturing, pharmaceuticals and market research.
SatuSaturn’s team of Advanced Analytics experts along with client personnel implemented a real time scoring system that generates a potential score along with relevant details for a new loan as well as a renewal. This involves looking at historical as well as current data from a wide variety of data sources, which come in a variety of structured, semi structured and unstructured formats.