Artificial Intelligence and Machine Learning Courses

AI+ Finance Agent Specialty - Self-paced

Length 365 days access
Price NZD 500 exc GST
Inclusions Online exam

Course overview

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Why study this course

The AI+ Finance Agent Specialty course is designed for finance professionals, data specialists, AI practitioners, and technologists seeking to apply AI in financial services. It delivers the capability to design, build, and deploy autonomous AI agents across key financial functions, targeting roles in banking, fintech, risk, compliance, investment, and financial analytics to enhance decision-making, efficiency, and regulatory alignment.

Participants will explore key concepts including AI agent architecture, data collection and preprocessing, feature engineering, and machine learning model implementation, alongside ethical and compliance considerations. The course emphasises hands-on, practical application using no-code platforms and a capstone project, enabling learners to gain real-world experience in developing, deploying, and optimising intelligent data agents capable of operating effectively in dynamics.

Exam and certification

This course prepares students for the corresponding certification. The exam/assessment is completed online and provided as part of the course content.

The exam is:

90 minutes
50 multiple choice / multiple response questions
Pass mark is 35 out of 50 (i.e. 70%)
Online via AI Proctoring platform

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What you’ll learn

Through this course, students will be able to:

Develop the capability to design, create, and deploy intelligent agents that streamline core financial operations.
Gain practical understanding of fraud detection, credit scoring, robo-advisory, algorithmic optimization, and risk analytics powered by machine learning and NLP
Build hands-on experience in automating financial tasks to improve accuracy, efficiency, and decision-making.
Understand how to maintain compliance while integrating AI-driven processes into financial environments.
Strengthen the skills needed to innovate, adopt intelligent systems, and advance in finance–AI hybrid careers.

AI CERTs at Lumify Work

AI CERTs® stands at the forefront of AI and blockchain certification, offering world-class programs that prepare individuals to lead in these rapidly growing fields. AI CERTs courses and certifications are vendor agnostic and designed to bridge the gap between theoretical knowledge and practical application, ensuring learners are equipped to make an immediate impact in their careers.
Lumify Work is a Platinum Authorized Training Partner for AI CERTs in Australia, New Zealand, and the Philippines.

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Who is the course for?

This course is intended for:

Finance Professionals Seeking AI Integration
Technology and Data Practitioners
AI Enthusiasts Exploring Finance Applications
Business Leaders and Innovators
Students and Career Switchers

Course subjects

Module 1: Introduction to AI Agents in Finance

Understanding AI Agents in Finance vs Traditional Financial Automation
The Evolution of AI Agents in Financial Services
Overview of Different Types of AI Agents in Finance
Importance of Agent Autonomy and Task Delegation in Financial Settings
Key Differences Between AI Agents in Finance and Traditional Automation
Hands-On Activity: Exploring AI Agents in Finance

Module 2: Building and Understanding AI Agents in Finance

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

Module 3: Intelligent Agents for Fraud Detection and Anomaly Monitoring

Supervised/Unsupervised ML for Fraud Detection
Pattern Analysis & Behavioural Profiling
Real-time Monitoring Agents
Real-World Use Case: AI Agents Monitoring Transaction Behaviour and Flagging Anomalies for Real-Time Fraud Detection in Digital Wallets
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
Hands-On Activity: Intelligent Agents for Fraud Detection and Anomaly Monitoring

Module 4: AI Agents for Credit Scoring and Lending Automation

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

Module 5: AI Agents for Wealth Management and Robo Advisory

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

Module 6: Trading Bots and Market-Monitoring Agents

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

Module 7: NLP Agents for Financial Document Intelligence

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

Module 8: Compliance and Risk Surveillance Agents

AI for Anti-Money Laundering (AML) and Know Your Business (KYB)
Regulation-aware Rule Modelling
Transaction Graph Analysis
Real-World Use Case: Agent Tracking Suspicious Cross-Border Money Transfers in Real-Time Across Multiple Accounts
Case Study: HSBC Uses Quantexa’s AI Agents to Trace AML Networks, Increasing Suspicious Activity Detection by 30%
Hands-On Activity: Compliance and Risk Surveillance Agents in Financial Systems

Module 9: Responsible, Fair & Auditable AI Agents

Governance Frameworks for AI in Finance (RBI, EU AI Act)
Transparency and Auditability in Decision Logic
Fairness and Explainability
Real-World Use Case: Auditable AI Agent Logs Used During Internal Policy Audits to Ensure Fair Lending Practices
Case Study: Wells Fargo Implemented Internal AI Fairness Reviews for Lending Bots Post Regulatory Scrutiny
Hands-On Activity: Responsible, Fair & Auditable AI Agents in Finance

Module 10: World Famous Case Studies

Case Study: JPMorgan’s COiN Platform
Case Study: AI in Fraud Detection – PayPal’s Decision Intelligence
Case Study: AI-Driven Credit Scoring – Upstart’s Lending Platform
Capstone Project
Key Takeaways of the Module

Prerequisites

Understanding of stock markets, trading, and financial instruments.
Basic concepts and algorithms of machine learning.
Proficiency in Python or similar languages for coding.
Knowledge of data analysis and statistical methods.
Enthusiasm for applying AI to solve financial challenges.

Terms & Conditions

The supply of this course by Lumify Work is governed by the booking terms and conditions. Please read the terms and conditions carefully before enrolling in this course, as enrolment in the course is conditional on acceptance of these terms and conditions.