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AI+ Finance Agent™

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  • Empower organizations with AI+ Finance Agent™ to automate financial operations and improve decisions
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$82.00

ChartBar (1)-51bd46
Intermediate
AI+ Finance Agent.
24h
1 Year.
$82.00
Training 5 or more people?
Get a Quote

Streamline finance and drive smarter decisions.

  • Core Concepts Covered: Learn AI fundamentals for finance, focusing on analytics, trading, risk, fraud, automation
  • Capstone Application: Build practical AI finance agents supporting trading, risk evaluation, fraud monitoring, and forecasting
  • Career Readiness: Gain expertise in AI-powered financial roles through mentorship, hands-on training, designing AI agents for finance innovation
  • Core Concepts Covered: Learn AI fundamentals for finance, focusing on analytics, trading, risk, fraud, automation
  • Capstone Application: Build practical AI finance agents supporting trading, risk evaluation, fraud monitoring, and forecasting
  • Career Readiness: Gain expertise in AI-powered financial roles through mentorship, hands-on training, designing AI agents for finance innovation
  • Financial Accuracy & Reliability: AI automation reduces manual errors and enhances precision across reconciliation, reporting, and day-to-day finance tasks.
  • Strategic Insight & Intelligence: Data-driven forecasting and analytics empower faster, smarter decisions in budgeting, planning, and financial strategy.
  • Risk Management & Compliance Strength: AI tools elevate fraud detection, regulatory oversight, and secure handling of sensitive financial data.
  • Operational Efficiency in Finance: Intelligent automation streamlines routine workflows, enabling teams to focus on high-impact financial initiatives.
  • Career Advancement in Digital Finance: Certification positions professionals at the forefront of AI-enabled finance transformation, increasing market relevance.

Course Curriculum

Module 1: Introduction to AI Agents in Finance
1.1 Understanding AI Agents in Finance vs Traditional Financial Automation 1.2 The Evolution of AI Agents in Financial Services 1.3 Overview of Different Types of AI Agents in Finance 1.4 Importance of Agent Autonomy and Task Delegation in Financial Settings 1.5 Key Differences Between AI Agents in Finance and Traditional Automation 1.6 Hands-On Activity: Exploring AI Agents in Finance

Module 2: Building and Understanding AI Agents in Finance
2.1 Architecture of AI Agents in Finance 2.2 Tools and Libraries for Agent Development 2.3 AI Agents vs. Static Models 2.4 Overview of Agent Lifecycle 2.5 Use Case: Customer Support Agents in Banks for Handling KYC, FAQs, and Transaction Disputes 2.6 Case Study: Bank of America’s Erica: A Virtual Financial Assistant that Handles 1+ Billion Interactions Using Predictive AI 2.7 Hands-On Activity: Building and Understanding AI Agents in Finance

Module 3: Intelligent Agents for Fraud Detection and Anomaly Monitoring
3.1 Supervised/Unsupervised ML for Fraud Detection 3.2 Pattern Analysis & Behavioural Profiling 3.3 Real-time Monitoring Agents 3.4 Real-World Use Case: AI Agents Monitoring Transaction Behaviour and Flagging Anomalies for Real-Time Fraud Detection in Digital Wallets 3.5 Case Study: PayPal’s AI System Uses Graph-Based Anomaly Detection Agents to Flag 0.32% of All Transactions for Fraud with 99.9% Accuracy 3.6 Hands-On Activity: Intelligent Agents for Fraud Detection and Anomaly Monitoring

Module 4: AI Agents for Credit Scoring and Lending Automation
4.1 Feature Generation from Non-Traditional Credit Data 4.2 Explainability (XAI) in Credit Decisions 4.3 Bias Mitigation in Lending Agents 4.4 Real-World Use Case: Agents Assessing New-to-Credit Individuals Using Transaction and Mobile Data 4.5 Case Study: Upstart’s AI-Based Lending Platform Approved by CFPB Showed 27% Increase in Approval Rate and 16% Lower APRs for Borrowers 4.6 Hands-On Activity: AI Agents for Credit Scoring and Lending Automation

Module 5: AI Agents for Wealth Management and Robo-Advisory
5.1 Personalization Using Profiling Agents 5.2 Portfolio Rebalancing Algorithms 5.3 Sentiment-Aware Investing 5.4 Real-World Use Case: AI Agent Adjusting Portfolio Weekly Based on Financial Goals and Market Trends 5.5 Case Study: Wealthfront’s Path Agent Uses Financial Behavior Modeling to Recommend Personalized Savings Goals and Investment Paths 5.6 Hands-On Activity: AI Agents for Wealth Management and Robo-Advisory

Module 6: Trading Bots and Market-Monitoring Agents
6.1 Reinforcement Learning in Trading Agents 6.2 Predictive Modelling Using Historical Data 6.3 Risk-Reward Threshold Management 6.4 Real-World Use Case: AI Trading Agents Performing Arbitrage Between Crypto Exchanges 6.4 Case Study: Renaissance Technologies Utilizes AI to Automate Short-Hold Trades, Generating Consistent Alpha via Adaptive Trading Bots 6.5 Hands-On Activity: Trading Bots and Market-Monitoring Agents

Module 7: NLP Agents for Financial Document Intelligence
7.1 LLMs in Earnings Call and Filings Analysis 7.2 AI Summarization and Event Detection 7.3 Voice-to-Text and Key-Point Extraction 7.4 Real-World Use Case 7.5 Case Study: BloombergGPT — A Financial-Grade Large Language Model 7.6 Hands-On Activity: NLP Agents for Financial Document Intelligence

Module 8: Compliance and Risk Surveillance Agents
8.1 AI for Anti-Money Laundering (AML) and Know Your Business (KYB) 8.2 Regulation-aware Rule Modelling 8.3 Transaction Graph Analysis 8.4 Real-World Use Case: Agent tracking suspicious cross-border money transfers in real-time across multiple accounts. 8.5 Case Study: HSBC uses Quantexa’s AI agents to trace AML networks, increasing suspicious activity detection by 30%. 8.6 Hands-On Activity: Compliance and Risk Surveillance Agents in Financial Systems

Module 9: Responsible, Fair & Auditable AI Agents
9.1 Governance Frameworks for AI in Finance (RBI, EU AI Act) 9.2 Transparency and Auditability in Decision Logic 9.3 Fairness and Explainability 9.4 Real-World Use Case: Auditable AI Agent Logs Used During Internal Policy Audits to Ensure Fair Lending practices. 9.5 Case Study: Wells Fargo implemented internal AI fairness reviews for lending bots post regulatory scrutiny. 9.6 Hands-On Activity: Responsible, Fair & Auditable AI Agents in Finance

Module 10: World Famous Case Studies
10.1 Case Study 1: JPMorgan’s COiN Platform 10.2 Case Study 2: AI in Fraud Detection – PayPal’s Decision Intelligence 10.3 Case Study: AI-Driven Credit Scoring – Upstart’s Lending Platform 10.4 Capstone Project 10.5 Key Takeaways of the Module

Learning Objectives

  • Learn to Build Autonomous AI Agents: Develop the capability to design, create, and deploy intelligent agents that streamline core financial operations.
  • Grasp Key Financial AI Applications: Gain practical understanding of fraud detection, credit scoring, robo-advisory, algorithmic optimization, and risk analytics powered by machine learning and NLP.
  • Apply Automation to Complex Workflows: Build hands-on experience in automating financial tasks to improve accuracy, efficiency, and decision-making.
  • Develop Regulatory-Aware AI Solutions: Understand how to maintain compliance while integrating AI-driven processes into financial environments.
  • Equip Yourself for Advanced Roles: Strengthen the skills needed to innovate, adopt intelligent systems, and advance in finance–AI hybrid careers.

Resource Center

  • Videos, Use cases, Workshops, E-books and tools.

Target Audience

  • Finance Professionals.
  • Investment & Portfolio Specialists.
  • Fintech Enthusiasts.
  • Data & Tech Professionals.
  • Business Leaders & Decision-Makers.

Job Roles & Industry Outlook 

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Chief Al Finance Officer (CAIFO)
Drive enterprise-wide Al adoption in finance, shaping strategy, governance, and innovation to enable data-driven, automated financial ecosystems.
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Al Financial Systems Consultant
Advise organizations on implementing Al-driven financial automation, predictive analytics, and intelligent decision-support systems to enhance overall financial performance.
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Finance Automation Lead
Oversee the development and deployment of Al-based tools that streamline accounting, reconciliation, reporting, and cash-flow operations for improved efficiency.
Background (3)-7d31b3
Al Financial Analyst
Build and apply machine learning models to forecast trends, score risk, evaluate investments, and generate actionable financial insights.

Exam Information

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Duration
90 minutes
Passing Score Icon-71608a
Passing Score
70% (35/50)
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Format
50 multiple-choice/multiple-response questions
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Delivery Method
Online via proctored exam platform (flexible scheduling)
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Fees include
One year of access to course content, including continuous updates. Certification exam. One exam re-take.

Exam Blueprint:

Introduction to AI Agents in Finance - 10%
Building and Understanding AI Agents in Finance - 10%
Intelligent Agents for Fraud Detection and Anomaly Monitoring - 10%
AI Agents for Credit Scoring and Lending Automation – 10%
AI Agents for Wealth Management and Robo-Advisory – 10%
Trading Bots and Market-Monitoring Agents - 10%
NLP Agents for Financial Document Intelligence - 10%
Compliance and Risk Surveillance Agents - 10%
Responsible, Fair & Auditable AI Agents - 10%
World Famous Case Studies - 10%

Explore our Schedules

Oct 4 - Oct 6
GMT 09:00 AM - 05:00 PM
In-person
In-Person
Doha
724.5$
3 Days
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Sep 13 - Sep 15
GMT 09:00 AM - 05:00 PM
In-person
In-Person
Dubai
724.5$
3 Days
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Apr 26 - May 3
GMT 06:00 PM - 10:00 PM
LVT
LVT
Zoom
280$
6 Days
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Aug 2 - Aug 9
GMT 06:00 PM- 10:00 PM
LVT
LVT
Zoom
280$
6 Days
Enroll Now
Oct 18 - Oct 25
GMT 06:00 PM- 10:00 PM
LVT
LVT
Zoom
280$
6 Days
Enroll Now

Certificate of Completion

AI+ Finance Agent.

AI-Healthcare-Certificate-Blurred 1

$82.00

ChartBar (1)-51bd46
Intermediate
AI+ Finance Agent.
24h
1 Year.
$82.00
Training 5 or more people?
Get a Quote

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