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所在平台: Udemy |
课程主页: https://www.udemy.com/course/financial-modeling-genai/
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课程名称：金融建模与生成性AI在银行业中的应用导论 课程概述：生成性AI正在改变建模的游戏规则。随着生成性AI在多个行业中的革命性变化，金融建模也不例外。生成性AI能够对数据进行总结、转换和处理，验证假设、即时生成场景、应用公式和模板等，像ChatGPT这样的文本生成AI正在改变建模的格局。本课程将介绍如何将生成性AI融入您的金融建模流程，以适应这一新时代。 课程内容： - 学习生成性AI的基础知识，包括其能力、局限性、常见模型和使用的技术，以及它如何加速各种任务的完成。 - 学习金融建模的基础知识，包括一般建模过程的四个步骤（收集数据、建立假设/约束、构建模型、验证/使用模型）。 - 了解金融领域一些常见的建模应用案例，例如折现现金流分析进行估值、回归分析用于信用评分、时间序列和机器学习用于证券价格预测、保险风险定价的精算/灾害模型，以及通常输入和假设的相关内容。 - 学习主要的金融模型类型：数学模型（应用运算于给定输入）、统计模型（根据变量间的因果关系、相关性或其他关系计算结果）、仿真模型（随机模拟不同场景以评估输出的变化）、算法/计算模型（以编程方式执行的一系列步骤），以及这些模型在银行/贷款、交易、欺诈检测或保险等常见用例中的应用。 - 深入学习建模过程的步骤，包括在每个步骤中需要考虑的事项（收集和准备数据时、建立假设和约束时、构建模型时、验证或使用模型时）。 - 学习生成性AI如何加速或增强建模过程的四个主要步骤（在收集数据时提取或转换数据、在建立假设时进行双重检查和生成假设、在构建模型时应用公式或进行计算、在验证或使用模型时验证输出或生成各种场景）。 - 了解信用分析中使用的常见模型（回归模型、信用评分模型和机器学习模型），以及生成性AI如何增强这些模型。 - 学习欺诈预防中使用的常见模型（基于规则的系统、异常检测器和网络算法），以及生成性AI如何增强这些模型。 通过本课程，您将能够掌握如何利用生成性AI提升金融建模的效率，助力决策和分析。
GENERATIVE AI IS CHANGING THE MODELING GAMEGenerative AI has revolutionized several industries. And financial modeling is no different.With the capability to summarize data, transform them and process them, validate assumptions, generate scenarios, apply formulas and templates instantly, and more, text generative AIs such as ChatGPT are changing the modeling landscape.This course will cover how to incorporate generative AI into your financial modeling pipeline, improving it for this new era.LET ME TELL YOU.EVERYTHING.Some people - including me - love to know what they're getting in a package.And by this, I mean, EVERYTHING that is in the package.So, here is a list of everything that this course covers:You'll learn about the basics of generative AI, including its capabilities, limitations, common models and technology used, and how it accelerates various tasks;You'll learn about the basics of financial modeling, including the general modeling process with four steps (gathering data, establishing assumptions/constraints, building the model, and validating it/using it);You'll learn about some common modeling use cases in finance, such as the Discounted Cash Flows analysis for valuation, regression for credit scoring, time series and machine learning for security price prediction, and actuarial/catastrophe models for insurance risk pricing, as well as the usual inputs and assumptions in general;You'll learn about the main types of financial models: mathematical (where we apply operations to the inputs given), statistical (where we calculate results based on causality, correlation, or other relationships among variables), simulations (where we stochastically simulate various scenarios and gauge variations in outputs due to these), and algorithmic/computational (where we execute a set of steps, in a programmatic manner), as well as how these are used for common use cases such as banking/lending, trading, fraud detection or insurance;You'll learn about the steps of the modeling process in depth, including what to take into account at each step (when gathering and preparing data, when establishing assumptions and constraints, when building the model itself, and when validating or using the model);You'll learn about ways in which gen AI can accelerate or augment each of the four main steps of the modeling process (extracting or transforming data when gathering data, double-checking and generating assumptions when establishing assumptions, applying formulas or making calculations when building the model, and validating outputs or generating various scenarios when validating or using the model);You'll learn about the usual models used for credit analysis (regression models, credit scoring models, and machine learning models), and how Gen AI can augment them;You'll learn about the usual models used for fraud prevention (rules-based systems, anomaly detectors, and network algorithms), and how Gen AI can augment them;
