Predictive models using big data

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Manulife currently has vast amount of customer data including past customer behaviour, customer demographics, and channel based transactions data. They would like to build multiple data science experiments coming from all divisions within Manulife. Capturing and analyzing structured data associated with their policyholders and unstructured data from various sources-including social media-can help Manulife evaluate the risks of insuring a particular person and set the premium for the policy accordingly. In addition, Big Data and analytics also affects customer insights, claims management, and risk management. As customer preferences change, Manulife has to develop simpler and more transparent products. They can analyze Big Data to better predict customer behavior so they can improve customer retention and become more profitable. Manulife can use predictive analytics to address the increase in fraudulent claims and losses.We propose the use of classification algorithms, such as deep neural network, Naïve Bayes, SVM, etc., on the key attributes (or reduced features) identified earlier to automatically classify the root causes of a past customer behavior. The objectives of the projects are to extract data from different sources and formats within Manulife by using big data tools such as Hadoop, MongoDB, etc.