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所在平台: Udemy |
课程主页: https://www.udemy.com/course/customer-spending-forecasting-insurance-policy-case-size/
课程评论：没有评论
课程名称：AI驱动的保险客户消费预测 课程概述：本课程专注于在保险和银行行业中解锁预测分析的潜力，通过对客户消费预测进行深入学习，特别是保险政策案例金额预测。课程将为您提供预测政策案例金额（即客户可能为保险政策支付的预期金额）的工具和技术，基于客户的人口统计和财务数据。这些技能将帮助您推动收入增长、提升客户定位，并在竞争激烈的保险领域中个性化客户提供。 课程开始时将介绍银行保险模式下的商业背景，银行与保险公司协作提供量身定制的保险产品。您将学习如何在不同角色（如数据架构师、数据分析师、数据科学家和数据工程师）中驾驭商业问题，提供一体化的解决方案。您将获得与客户相关的关键信息的实践经验，从人口统计信息到财务洞察，这些信息构成了预测模型的基础。 课程将带您全面了解数据集成管道，从各种来源（例如核心银行和卡管理系统）访问和获取数据，到在数据仓库（DWH）和数据集市环境中集中数据。我们将深入探讨端到端的数据流，涵盖ETL（提取、转换、加载）过程以及使用Apache NiFi和Kafka等技术的实时数据流。 随后，您将使用Python、Jupyter Notebook、XGBoost和人工神经网络（ANN）构建和部署机器学习模型。这些模型将根据诸如信用卡限额、活期存款余额和消费行为等财务指标进行训练，以准确预测保险政策案例金额。此外，您还将学习使用BI工具（如Power BI和Tableau）有效地可视化和报告洞察。 本课程非常适合数据爱好者、希望在快速发展的预测分析领域提升技能的准数据科学家和银行专业人士。加入我们，共同利用数据的力量，改变未来的保险和银行策略！
Unlock the power of predictive analytics in the insurance and banking industries with our comprehensive course on Customer Spending Forecasting: Insurance Policy Case Size Prediction. This course equips you with the tools and techniques to predict the policy case size-the expected amount a customer may pay for an insurance policy-based on demographic and financial data. With this skill set, you'll be able to drive revenue growth, enhance customer targeting, and personalize offers in a competitive insurance landscape.The course begins by setting the business context within the bancassurance model, where banks and insurance companies collaborate to provide tailored insurance offerings. You'll learn to navigate the business problem and work within various roles, such as Data Architect, Data Analyst, Data Scientist, and Data Engineer, to deliver a cohesive solution. Gain hands-on experience with essential customer information, from demographic details to financial insights, that form the backbone of the model.Our course walks you through the entire data integration pipeline, from accessing and ingesting data from diverse sources (like core banking, and card management systems) to centralizing it in a Data Warehouse (DWH) and Data Mart environment. You'll dive into end-to-end data flow, covering ETL (Extract, Transform, Load) processes and real-time streaming with technologies like Apache NiFi and Kafka.As we proceed, you'll build and deploy machine learning models using Python, Jupyter Notebook, XGBoost, and Artificial Neural Networks (ANN). These models are trained on financial indicators like credit card limits, CASA balances, and spending behavior to predict insurance policy case sizes accurately. With BI tools such as Power BI and Tableau, you'll also learn to visualize and report insights effectively.This course is perfect for data enthusiasts, aspiring data scientists, and banking professionals looking to upskill in the rapidly growing field of predictive analytics. Join us to harness the power of data in transforming insurance and banking strategies for the future!
