Designing appropriate credit risk model for big data via cloud computing

Business industries are either experiencing or expected to face the challenge of big data. Big data brings trouble to traditional data mining algorithms. Building a machine learning credit scoring algorithm under the big data scenario, offers ATB financial the framework which improve the efficiency and the running speed compared with traditional data mining algorithms, especially when the training data set is very large. Such an algorithm designed for big data will ultimately help ATB to have a better connection with customers due to the faster speed of algorithm decision. Moreover, the proposed algorithm in this proposal also offers ATB the way to improve the models’ real-time capability by considering data flowing.

Faculty Supervisor:

Bei Jiang

Student:

Partner:

ATB Financial

Discipline:

Mathematics

Sector:

Finance and Insurance

University:

University of Alberta

Program: