Building an analytics framework for predicting customer default****

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This research aims to deliver expertise in data analytics and predictive modeling to help Scotiabank prepare a dataset that is amenable for analysis and to develop and implement predictive models for customer default. The project will help accomplish the following tasks; 1. Extract, organize and integrate data from the different systems identified by the bank, 2. Fix data quality issues (inconsistencies, missing values, duplication, etc.), 3. Transform the raw data into derived variables which can be useful model inputs and perform characteristics analysis, 4. Develop predictive models using machine learning algorithms. **With large volumes of data accumulated by companies every day, it is imperative that they find a more efficient and effective way to manage their data, and proactively assess risk to ideally help reduce and even prevent customer default. This research will help the company equip its platform with new capabilities to efficiently handle diverse data, and use it to predict customer default. Additionally, the project will provide a valuable learning and training environment for Highly Qualified Personnel (HQP) who will develop the skills and expertise to develop predictive modeling techniques for financial institutions.**********