Discover how AI can reduce repetitive accounting work (reconciliations, data processing, spreadsheet analysis) so accountants focus on judgment, controls, audit readiness, and strategic insights—without replacing the profession.

AI for Accountants: Future-Proof Your Close, Controls & Career

How AI Will Transform Accountants and Future-Proof Your Accounting Career

In this episode of Blood, Sweat, and Balance Sheets, FloQast CEO Mike Whitmire sits down with Dan Callaghan, FloQast's Chief Accounting Officer, to discuss his 30+ year career journey and share his bold vision for the future of accounting in the age of AI.

What You'll Learn:
Why AI is accounting's "tractor moment" and what that means for your career
How Dan went from KPMG auditor to CAO of multiple pre-IPO tech companies
The truth about AI replacing accountants (spoiler: it won't)
Why every accountant needs to upskill NOW to stay competitive
Inside look at the FloQast Certified Accountant (FCA) program
How AI will free accountants from spreadsheet drudgery to become strategic business partners

Key Takeaways: Dan argues that AI isn't eliminating accounting jobs -- it's eliminating the manual grind of data entry, reconciliation, and spreadsheet analysis. Just like tractors revolutionized farming without replacing farmers, AI will transform accountants from "counters" into strategic advisors and business partners.

This is your opportunity to shape your own future in the profession. The accountants who embrace AI and accounting transformation platforms today will be the ones sitting at the board table tomorrow.

About the Guest: Dan Callaghan is the Chief Accounting Officer at FloQast with 30+ years of experience, including helping take three companies public from the controllership role.

🔗 Learn more about the FloQast Certified Accountant program: https://www.floqast.com/floqast-certified-accountant

#Accounting #AI #FutureOfWork #AccountingTech #CFO #FloQast #CareerAdvice #accounting , #accountant , #accountingsoftware , #accountingsolutions #cpa , #cfo , #financialaccounting , #accountingtips , #audit , #finance , #technology , #companyculture , #ai , #work, #workplaceculture,

How AI Will Transform Accountants: Future-Proofing Your Career | FloQast CAO Dan Callaghan

Learn how to use generative AI responsibly in the classroom. Discover practical steps to strengthen critical thinking, teach AI literacy, personalize support, and assess reasoning.

Using Generative AI in Education: Build Reasoning, Not Shortcuts

AI in Education: How Generative AI Can Build Critical Thinking and Future-Ready Skills

Ready to become a certified watsonx AI Assistant Engineer? Register now and use code IBMTechYT20 for 20% off of your exam → https://ibm.biz/Bdb5ip

Learn more about Artificial Intelligence (AI) here → https://ibm.biz/Bdb5i8

AI is changing everything, including the way we learn 🎓🤖. Jeff Crume explores how AI & Education are transforming classrooms with generative AI, fostering critical thinking, personalized learning, and future-ready skills. Discover the opportunities and challenges of integrating AI in modern education!

AI news moves fast. Sign up for a monthly newsletter for AI updates from IBM → https://ibm.biz/Bdb5ig

#ai #education #generativeai

AI & Education: Generative AI & the Future of Critical Thinking

Learn AI ethics step by step: what it means, why bias happens in machine learning, how data shapes outcomes, and how to think about alignment, risk, and responsible AI use.

AI Ethics for Machine Learning: Bias, Alignment, and Safe Deployment

AI Ethics and Machine Learning: A Step by Step Guide to Bias, Alignment, Risk and Responsible Use

Should AI systems have personhood? Is AI biased? Go from 0 to 1 in AI ethics in 90 minutes!

In this video, you will learn about the foundations of AI, its applications, and the ethical considerations that shape this transformative field. 

✏️ Course created by Seth Goldin - https://twitter.com/seth_goldin

❤️ Try interactive AI courses we love, right in your browser: https://scrimba.com/freeCodeCamp-AI (Made possible by a grant from our friends at Scrimba)

⭐️ Course Contents ⭐️
✨ (00:00) Opening Remarks
📝 (02:08) Intro to AI Ethics
🤖 (10:52) Basics of AI/ML
🧠 (18:37) Neural Networks
🐶 (25:56) Datasets
⬛ (28:54) Limitations of AI & Black Boxes
⚖️ (38:48) Intro to Ethics
💭 (45:38) Thought Experiments
☣️ (52:06) The Singularity and AI Risk
🫀 (01:02:16) AI in Healthcare
✏️ (01:08:45) AI in Education
📈 (01:13:29) AI in Finance
🫂 (01:18:37) AI in Employment
🌏 (01:27:00) AI and Policy
🌱 (01:37:49) Future of Ethical AI

This course also showcases segments from guest speakers Sneha Revanur (https://twitter.com/sneharevanur) from Encode Justice, Okezue Bell (https://twitter.com/okezuebell) from Fidutam, and Emilia Javorsky (https://futureoflife.org/person/emilia-javorsky-md-mph/) from the Future of Life Institute. 

Mostly unaffiliated links to dive deeper into topics discussed in this course:

🔗Deep Reinforcement Learning Visualized: https://www.youtube.com/watch?v=2tamH76Tjvw
🔗Deep Learning Crash Course: https://www.youtube.com/watch?v=VyWAvY2CF9c
🔗The Rise of the Machines - Why Automation is Different this Time: https://youtube.com/watch?v=WSKi8HfcxEk 
🔗Do Robots Deserve Rights? What if Machines Become Conscious?: https://www.youtube.com/watch?v=DHyUYg8X31c 
🔗​​The big debate about the future of work, explained: https://www.youtube.com/watch?v=TUmyygCMMGA 
🔗A Collection of Lectures on the Ethical implications of Data and Artificial Intelligence from Different Perspectives: https://aiethicscourse.org/ 
🔗Predictive Policing: https://www.youtube.com/watch?v=A-PrLbawdgg
🔗Elections and Disinformation: https://www.goodreads.com/en/book/show/28186015
🔗AI Safety – Full Course on Machine Learning & Ethics (Center for AI Safety): https://www.youtube.com/watch?v=agEPmYdbQLs
🔗How AI Could Save (Not Destroy) Education | Sal Khan: https://www.youtube.com/watch?v=hJP5GqnTrNo 
🔗ProPublica Study on Recidivism: https://www.propublica.org/article/how-we-analyzed-the-compas-recidivism-algorithm
🔗Course on AI Safety: https://aisafetyfundamentals.com/ 

YouTube videos with brief clips utilized in this course:  

📼The Trolley Problem | The Good Place: https://www.youtube.com/watch?v=DtRhrfhP5b4 
📼AlphaFold: The making of a scientific breakthrough: https://www.youtube.com/watch?v=gg7WjuFs8F4 
📼Stock Market Crash - Flash Crash May 6, 2010: https://www.youtube.com/watch?v=E1xqSZy9_4I 
📼Taylor Swift – Starboy (AI) and Barack Obama Deepfake supplied by Encode Justice


🎉 Thanks to our Champion and Sponsor supporters:
👾 davthecoder
👾 jedi-or-sith
👾 南宮千影
👾 Agustín Kussrow
👾 Nattira Maneerat
👾 Heather Wcislo
👾 Serhiy Kalinets
👾 Justin Hual
👾 Otis Morgan 
👾 Oscar Rahnama

--

Learn to code for free and get a developer job: https://www.freecodecamp.org

Read hundreds of articles on programming: https://freecodecamp.org/news

The Ethics of AI & Machine Learning [Full Course]

Learn the differences between AI, machine learning, deep learning, and generative AI—what each term means, how they relate, and why generative AI is driving today’s breakthroughs.

AI vs Machine Learning vs Deep Learning vs Generative AI (Simple Guide)

AI vs Machine Learning vs Deep Learning vs Generative AI

Want to learn more about Agentic AI + Data? Register here →  https://ibm.biz/BdeGLe

Want to play with the technology yourself? Explore our interactive demo → https://ibm.biz/BdKSer

Join Jeff Crume as he dives into the distinctions between Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and Foundation Models and how these technologies have evolved over time. He also explores the latest advancements in Generative AI, including large language models, chatbots, and deepfakes - and clarifies common misconceptions, simplifies complex concepts, and discusses the impact these technologies have on various fields.

AI news moves fast. Sign up for a monthly newsletter for AI updates from IBM → https://ibm.biz/BdKSei

AI, Machine Learning, Deep Learning and Generative AI Explained

Learn how the fraud triangle explains why fraud happens in auditing. Understand pressure, opportunity and rationalisation—plus key risk indicators for fraudulent financial reporting and asset misappropriation.

Fraud Triangle in Auditing: Pressure, Opportunity & Rationalisation

Fraud Triangle Explained: Pressure, Opportunity and Rationalisation in Auditing

In this session, we explain the fraud triangle.
Start your free trial: 👉 https://farhatlectures.com/

0:00 Introduction
The Fraud Triangle (0:15): The video introduces the concept of the fraud triangle, highlighting the three elements that are present when fraud occurs: incentive/pressure, opportunity, and rationalization.

Real-World Example (5:37): The presenter uses the example of Andy Fastow from Enron to illustrate how the three elements of the fraud triangle come together in a real-world scenario.

Breaking the Fraud Triangle (17:25): The video explains that while it's difficult to eliminate pressure or rationalization, eliminating the opportunity for fraud through strong internal controls is key to preventing it.





Understanding the Fraud Triangle
The Fraud Triangle is a theoretical framework designed to explain the reasoning behind an individual’s decision to commit fraud. Developed by criminologist Donald Cressey, the model outlines three elements that, when combined, increase the likelihood of fraudulent behavior. These factors are: Pressure (or Motivation), Opportunity, and Rationalization. Understanding these components helps organizations identify and mitigate the risk of fraud.

1. Pressure (Motivation)
Definition: Pressure or motivation refers to the personal need felt by the individual committing the fraud. This need is often financial but can also be driven by other pressures, such as the desire for social status, personal financial problems, or workplace challenges.
Examples:
Financial stress from debt, medical expenses, or lifestyle expectations.
Unrealistic performance targets or compensation structures that directly tie to financial rewards.
2. Opportunity
Definition: Opportunity is the element of the Fraud Triangle that provides the means for fraud to be committed. It typically stems from weaknesses in the internal controls or processes that an individual believes they can exploit without being caught.
Examples:
Inadequate segregation of duties, where one person controls multiple aspects of financial transactions or data management.
Lack of oversight or ineffective monitoring systems that fail to catch inconsistencies or irregular activities.
3. Rationalization
Definition: Rationalization is the internal justification that fraudsters use to convince themselves that their fraudulent action is acceptable. This factor is about aligning the act of fraud with the individual’s self-image without feeling significant guilt or self-reproach.
Examples:
Believing the fraud is a temporary loan that they’ll pay back once personal financial conditions improve.
Feeling underappreciated or underpaid, thinking, "I deserve more," or "The company won’t miss it."
Justifying the act by blaming poor management or claiming the system is unfair.
Application in Preventive Measures
Pressure: Companies can reduce pressure by setting realistic performance goals, maintaining open lines of communication about employee hardships, and offering support programs for personal or financial problems.
Opportunity: Strengthening internal controls is crucial. This includes implementing robust checks and balances, regular audits, effective oversight, and clear segregation of duties.
Rationalization: Fostering an ethical corporate culture and promoting an environment where employees feel valued and compensated fairly can mitigate rationalization. Education about the consequences of fraud and visible enforcement of policies also plays a crucial role.
Importance in Fraud Detection and Prevention
Understanding the Fraud Triangle provides valuable insights into why fraud is committed and what can be done to prevent it. By addressing the underlying factors that contribute to fraud, companies can better safeguard their assets and maintain integrity within their operations. This model is not only used to combat fraud after it happens but is also a preventive tool, guiding policy development and internal control systems to deter potential fraudsters.


#cpaexam #accountingtutorial #fraud

Archive

How AI Will Transform Accountants and Future-Proof Your Accounting Career

AI in Education: How Generative AI Can Build Critical Thinking and Future-Ready Skills

AI Ethics and Machine Learning: A Step by Step Guide to Bias, Alignment, Risk and Responsible Use

AI vs Machine Learning vs Deep Learning vs Generative AI

Fraud Triangle Explained: Pressure, Opportunity and Rationalisation in Auditing