Energy and Utilities
Finding hidden patterns and relations in the Credit Bureau related data is a key to build new and advanced statistical indicators that cover various areas from creditworthiness assessment of subjects with no or thin credit history to anti-fraud scores, to collection. CRIF is using machine learning techniques to exploit the value of its Credit Bureau data and to provide innovative solutions to problems that were unanswered so far.
As an example, with a strong focus on financial inclusion CRIF has developed a machine-learning based indicator that is able to score a subject (individual, sole trader, SMEs) even if not present in the Credit Bureau database. Based on the information that are usually provided along with the application data, on technical inquiry information, on open data, on internally calculated indicators and on derived variables, the no hit score is able to return a statistical indicator of the likelihood a customer will default over the next year on a payment, despite the absence of credit bureau data.
With a strong mix of proprietary data, dedicated Hadoop-based big data infrastructure, data analysis skills, expertise in terms of compliance and data protection, CRIF is very well positioned to continue exploiting the value of its Credit Bureau data mixing machine learning techniques in top of the well-established standard statistical approaches.