AI Applications in Accounting and Finance

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AI Applications in Accounting and Finance

Instructeur : Sean Cao

Inclus avec

4 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Débutant

Expérience recommandée

2 semaines à compléter

à 10 heures par semaine

Planning flexible

Apprenez à votre propre rythme

4 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Débutant

Expérience recommandée

2 semaines à compléter

à 10 heures par semaine

Planning flexible

Apprenez à votre propre rythme

Compétences que vous acquerrez

Catégorie : Emerging Technologies
Catégorie : Data-Driven Decision-Making
Catégorie : Text Mining
Catégorie : Accounting
Catégorie : Machine Learning
Catégorie : Financial Analysis
Catégorie : Artificial Intelligence
Catégorie : Finance
Catégorie : Unstructured Data
Catégorie : Image Analysis
Catégorie : Annual Reports
Catégorie : Trend Analysis
Catégorie : Financial Data
Catégorie : Financial Statement Analysis
Catégorie : Natural Language Processing

Détails à connaître

Certificat partageable

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Récemment mis à jour !

septembre 2025

Évaluations

3 devoirs

Enseigné en Anglais

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Il y a 4 modules dans ce cours

In this course, you will learn:

How to work with unstructured financial data like earnings call transcripts, press releases, and ESG disclosures. How to apply machine learning and AI tools to real-world financial documents, images, and social media signals. How to evaluate and use emerging technologies with minimal coding requirements—no tech background needed. Detailed description: Designed for accounting and finance students and professionals, this course removes the common barriers to learning AI by integrating practical, job-relevant applications directly into the business context you already understand. Using real business data and use cases, you will learn how AI is used in accounting to solve accounting problems and complete accounting tasks. Each module includes engaging video lessons, guided exercises, and accessible tutorials that help you grasp key concepts. By the end, you will not only understand how AI works in finance, you will be able to use it confidently. Whether you are preparing for a career in corporate finance, accounting, consulting, or fintech, this course will give you a competitive edge.

Inclus

20 vidéos14 lectures1 devoir2 sujets de discussion

20 vidéosTotal 220 minutes

The Rising Use of Big Data for Decision-Making6 minutes
How to Leverage Data Science for Corporate Stakeholders: The Importance of Domain Knowledge, Part 110 minutes
How to Leverage Data Science for Corporate Stakeholders: The Importance of Domain Knowledge, Part 212 minutes
How to Leverage Data Science for Corporate Stakeholders: The Importance of Domain Knowledge, Part 37 minutes
Overview of Academic Research and Industry Adoption, Part 116 minutes
Overview of Academic Research and Industry Adoption, Part 29 minutes
An Overview of Structured and Unstructured Data, Part 110 minutes
An Overview of Unstructured and Structured Data, Part 28 minutes
An Overview of Unstructured and Structured Data, Part 39 minutes
Theory-Driven and Machine-Learning Approach of Data Analytics6 minutes
The Advantages of Applying Machine-Learning Approaches10 minutes
How Industry and Academics Adopt AI, Part 19 minutes
How Industry and Academics Adopt AI, Part 212 minutes
How Industry and Academics Adopt AI, Part 315 minutes
How Industry and Academics Adopt AI, Part 44 minutes
How Industry and Academics Adopt AI, Part 57 minutes
How to Generate and Eliminate Ideas23 minutes
How Do Writing Styles and Focal Questions Differ Between Finance and Accounting Scholars?12 minutes
Disruptive AI in Classical Finance Theory, Part 112 minutes
Disruptive AI in Classical Finance Theory, Part 213 minutes

14 lecturesTotal 70 minutes

Course Overview5 minutes
Content Overview10 minutes
Video Roadmap10 minutes
Meet Your Instructor2 minutes
Getting Help1 minute
Introduction to Module 1: Data Analytics in Finance and Accounting10 minutes
The Rising Use of Big Data for Decision-Making3 minutes
What Separates Us from Computer Science and Statistics Majors: The Importance of Domain Knowledge5 minutes
Tailoring Data Science to the Needs of Different Corporate Stakeholders3 minutes
Unstructured Data Analytics2 minutes
Structured Data Analytics3 minutes
Theory-Driven Approach Vs. Machine-Learning Approach4 minutes
The Advantages of Applying Machine-Learning Approaches2 minutes
Coming Up in "Idea Generation"10 minutes

1 devoirTotal 30 minutes

Module 1 Quiz30 minutes

2 sujets de discussionTotal 45 minutes

Meet Your Learning Group15 minutes
Application and Discussion: Approaches to Data Analytics30 minutes

Inclus

9 vidéos15 lectures1 devoir1 sujet de discussion

9 vidéosTotal 76 minutes

Data Structure of 10-K Filing4 minutes
Item 1 Business Description6 minutes
Item 1A Risk Disclosure4 minutes
Item 7: Management's Discussion and Analysis7 minutes
Textual Analysis: Keyword Search and LDA, Part 119 minutes
Textual Analysis: Keyword Search and LDA, Part 26 minutes
An Empirical Example of Analyzing 10-K Filings10 minutes
Guidance for Module 2 Project: How to Crawl Annual Reports10 minutes
Guidance for Module 2 Project: How to Parse Unstructured Data7 minutes

15 lecturesTotal 97 minutes

Introduction to Module 2: Analyzing Annual Reports10 minutes
Data Structure in Annual Reports and the 10-K Filing2 minutes
Other Items in Form 10-K: Part I of Form 10-K30 minutes
Other Items in Form 10-K: Part II of Form 10-K15 minutes
Other Items in Form 10-K: Part III and IV of Form 10-K4 minutes
Data Structure in Annual Reports1 minute
Coming Up in "Conventional Textual Analysis Approach"1 minute
Conventional Approach Review (Bag of Words)1 minute
Building a Keyword Dictionary5 minutes
Latent Dirichlet Allocation (LDA)2 minutes
Coming Up in "Empirical Examples: Analyzing Corporate Filings for Making Business Decisions"10 minutes
10-Ks and 10-Qs1 minute
Item 1 of 10-K2 minutes
Item 1A of 10-K3 minutes
Guidance for Module 2 Project: Sample Codes10 minutes

1 devoirTotal 30 minutes

Module 2 Quiz30 minutes

1 sujet de discussionTotal 60 minutes

Application and Discussion: How to Crawl Annual Reports and Parse Unstructured Data60 minutes

Inclus

17 vidéos21 lectures1 devoir1 sujet de discussion

17 vidéosTotal 226 minutes

Procedures for Applying Machine Learning Models, Part 14 minutes
Procedures for Applying Machine Learning Models, Part 213 minutes
Procedures for Applying Machine Learning Models, Part 35 minutes
Basic Concept and Foundation of ML14 minutes
Textual Analysis: Word Representation and Sentence Level Analysis using Google Bert, Part 116 minutes
Textual Analysis: Word Representation and Sentence Level Analysis using Google Bert, Part 29 minutes
From Supervised and Self-Supervised Learning to Distillation: An Overview of Technological Development from GPT to Deepseek, Part 16 minutes
From Supervised and Self-Supervised Learning to Distillation: An Overview of Technological Development from GPT to Deepseek, Part 214 minutes
From Supervised and Self-Supervised Learning to Distillation: An Overview of Technological Development from GPT to Deepseek, Part 311 minutes
Prompt Engineering for Large Language Models, Part 18 minutes
Prompt Engineering for Large Language Models, Part 214 minutes
Prompt Engineering for Large Language Models, Part 312 minutes
LLM Look-Ahead Bias and Potential Solutions, Part 110 minutes
LLM Look-Ahead Bias and Potential Solutions, Part 219 minutes
How AI Differs from Traditional Technology Breakthroughs10 minutes
Using AI as a Research Assistant, Part 119 minutes
Using AI as a Research Assistant, Part 234 minutes

21 lecturesTotal 82 minutes

Introduction to Module 3: Emerging AI Technology in Textual Analysis10 minutes
Data Cleaning, Parsing, and Feature Selection2 minutes
Machine Learning Model Selection2 minutes
Hyperparameter Tuning3 minutes
Model Evaluation5 minutes
Fundamental Concepts of Pre-Training in Machine Learning5 minutes
Textual Representation3 minutes
Advantages of Phrase-level Word-Embedding1 minute
Reinforcement Learning2 minutes
Pre-Trained Sentence-Level Word-Embedding2 minutes
Bidirectional Encoder Representation from Transformers2 minutes
Generative Pre-trained Transformers1 minute
Differences between GPT and BERT2 minutes
Application of GPT5 minutes
Prompt Engineering2 minutes
Hard Prompt-Tuning5 minutes
Soft Prompt-Tuning3 minutes
Prompt Engineering Vs. Model Fine-Tuning3 minutes
Machine Philosophy4 minutes
Competitive Advantages of Man and Machine5 minutes
Using AI as a Research Assistant15 minutes

1 devoirTotal 30 minutes

Module 3 Quiz30 minutes

1 sujet de discussionTotal 60 minutes

Application and Discussion: Evaluating Machine Learning Models60 minutes

Inclus

1 plugin

Instructeur

Sean Cao

University of Maryland, College Park

1 Cours1 421 apprenants

Offert par

University of Maryland, College Park

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