Customer Profiling and Prediction of Revenue, Cost and Margin Based on Customer Behaviour

这项工作的重点是根据金融交易数据的过去历史预测未来的行为。大部分交易数据来自日常银行活动，如存款，取款，利息，贷款，抵押贷款和各种金融投资。越来越多的金融机构正在寻求提供更高的消费者价值，并通过使用交易历史作为预测未来客户行为的基础，在收入，成本和利润方面创造竞争优势。因此，该参与补助金的目的是开发一个会员资料驱动的模型，用于预测未来的会员行为，最终目标是预测未来的产品和服务使用情况（即客户行为）以及相关的收入、成本和利润。这笔赠款的结果将改善马尼托巴省和加拿大的经济，使加拿大金融机构在其他司法管辖区的竞争中具有优势，并在金融服务部门创造新的就业机会，用于预测会员/消费者行为的数据科学和商业智能。

The focus of this work is the prediction of future behaviour based on past history for financial transactional data. Much of the transactional data arises out of daily banking activities such as deposits, withdrawals, interest, loan, mortgages and various financial investments. Increasingly, financial institutions are seeking to provide enhanced consumer value as well as create competitive advantage by using transaction history as a foundation for predicting future client behaviour in terms of revenue, cost and margin. Thus, the aim of this Engage Grant is to develop a member-profile driven model for predicting future member behaviour, where the ultimate goal is the prediction of future product and service usage (i.e. customer behaviour) and associated revenue, cost and margin. The outcome of this grant will improve the Manitoban and Canadian economy by giving Canadian financial institutions an edge over their competition in other jurisdictions and create new job opportunities within the financial services sector for data science and business intelligence on predicting member/consumer behaviour.