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。这项工作是对与DecisionWorks合作的Engage Grant结果的扩展，其目的是开发一个 ** 会员个人资料驱动的模型，用于预测未来的会员行为，如收入，成本和利润。由于最初的Engage Grant，通过引入 ** 有效的递归神经网络（RNN）架构，数据集特征选择以及使用定义的RNN对 ** 整个模型进行两阶段设计，在实现这一目标方面取得了巨大进展。该Engage Plus Grant的重点是通过为提供数据的相应信用社进行试点实验来评估 ** 该模型在真实的生活中的有效性;** 将我们基于RNN的模型与行业目前使用的其他现有模型进行比较;并将 ** 提出的解决方案集成到DW的系统中。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. This work is an**extension of the results from an Engage Grant with DecisionWorks, where the aim was to develop a**member-profile driven model for predicting future member behaviour, such as revenue, cost and margin. As a**result of the original Engage Grant tremendous progress was achieved toward this goal through introducing an**effective recurrent neural network (RNN) architecture, dataset feature selection, and a two-phase design of the**whole model using the defined RNN. The focus of this Engage Plus Grant is to evaluate the effectiveness of**this model in real life by performing a pilot experiment for the respective credit union that provided the data;**compare our RNN-based model with other existing models currently used by the industry; and integrate the**proposed solution into the DW's systems. 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.