Credit risk prediction with Azure Machine Learning

September 15 2017

Credit risk analysis is important to financial institutions that provide loans to businesses and individuals. Credit loans and finances have risk of being defaulted or delinquent. To understand risk levels of credit users, credit providers normally collect vast amount of information on borrowers. Statistical predictive analytic techniques can be used to analyze and determine risk levels involved on credits, and approve or reject credit applications accordingly.

About Stefano Tempesta

Stefano Tempesta is CTO at SXiQ, a Microsoft Regional Director, MVP on Azure, AI and Business Applications, and co-founder of 365 Community, a non-profit organization whose mission is to empower communities around the world with better ICT processes for customer relationship management (CRM) and customer service, using modern technology.
A technology manager with 20 years of international experience, entrepreneur CTO and advisor for start-ups in Australia, Singapore and Switzerland, Stefano is an author, a public speaker, a blogger, an event organizer and an ambassador of beautiful software. His interests extend to microservice architectures, blockchain, IoT and A.I. related technologies.

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