Development of new methods for the joint modeling of longitudinal and survival data with applications in finance and insurance

开发纵向数据和生存数据联合建模的新方法及其在金融和保险中的应用

• 批准号: 557209-2020
• 负责人: Duchesne, ThierryT
• 金额: $ 3.51万
• 依托单位: Université Laval
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=759452
• 关键词: Development; new; methods; joint; modeling

A large proportion of Canadian households find it challenging to manage their finance and insurance products. As a matter of fact, according to Statistics Canada, Canadian household credit market debt as a proportion of disposable income has increased to more than 176 % during the first quarter of 2020. Making sure that Canadians receive offers of products better suited to their specific needs at more opportune times would go a long way in improving their financial health. The goal of this project is to develop analytical tools that can help a major Canadian provider of financial and insurance products to identify the best products to offer to their clients and the best times at which to offer these products using the massive historical information that is available in the company's databases. While some statistical and machine learning algorithms that already exist can be tweaked to perform similar tasks, no method that fully takes into account the temporal, hierarchical and network structure of the data are currently available. In this project, such methods will be developed and will be implemented in open software that will be made publicly available. Because historical financial databases do not reflect the diversity of the current client base of financial companies in major Canadian cities, efforts will be made to make sure that the methods to be developed will be as efficient to improve the financial health of minority groups as they will be for majority groups.

很大一部分加拿大家庭发现，管理他们的金融和保险产品具有挑战性。事实上，根据加拿大统计局的数据，2020年第一季度，加拿大家庭信贷市场债务占可支配收入的比例已增至176%以上。确保加拿大人在更合适的时间收到更适合他们特定需求的产品，将对改善他们的财务健康大有裨益。这个项目的目标是开发分析工具，帮助加拿大一家主要的金融和保险产品提供商利用公司数据库中的大量历史信息，确定向客户提供最佳产品以及提供这些产品的最佳时间。虽然已经存在的一些统计和机器学习算法可以调整以执行类似的任务，但目前还没有完全考虑到数据的时间、层次和网络结构的方法。在这个项目中，这些方法将被开发，并将在开放软件中实施，这些软件将公开可用。由于历史金融数据库没有反映加拿大主要城市金融公司当前客户基础的多样性，将努力确保将要开发的方法将像改善多数群体的财务健康一样有效地改善少数群体的财务健康。