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AI CERTS® certification

AI+ Finance Agent Specialty™

Formerly known as AI+ Finance Agent™ Empower organizations with AI+ Finance Agent Specialty™ to automate financial operations and improve decisions

Enquire about this certificationCourse code: AP 2201
AI+ Finance Agent Specialty™ certification badge

Course overview

Review the course before deciding whether it is suitable.

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

Included
Instructor-led OR Self-paced course + Official exam + Digital badge
Duration
Instructor-Led: 1 day (live or virtual)Self-Paced: 8 hours of content
Exam format
50 questions, 70% passing, 90 minutes, online proctored exam
Prerequisites
Basic Knowledge of Financial Markets, Familiarity with Machine Learning, Programming Skills, Statistical Analysis Understanding, Interest in Financial Technology

Course focus

Why this certification may be relevant.

01

Financial Accuracy & Reliability:

AI automation reduces manual errors and enhances precision across reconciliation, reporting, and day-to-day finance tasks.

02

Strategic Insight & Intelligence:

Data-driven forecasting and analytics empower faster, smarter decisions in budgeting, planning, and financial strategy.

03

Risk Management & Compliance Strength:

AI tools elevate fraud detection, regulatory oversight, and secure handling of sensitive financial data.

04

Operational Efficiency in Finance:

Intelligent automation streamlines routine workflows, enabling teams to focus on high-impact financial initiatives.

05

Career Advancement in Digital Finance:

Certification positions professionals at the forefront of AI-enabled finance transformation, increasing market relevance.

Who it may suit

Check the intended audience against your experience and goals.

Course content

Modules supplied by AI CERTs.

Open each module to review the stated topics.

01Module 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
02Module 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
03Module 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
04Module 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
05Module 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
06Module 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
07Module 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
08Module 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
09Module 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
10Module 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

Tools referenced by AI CERTs

Platforms and tools included in the course information.

PythonTensorFlowPandasNumPyPower BISQLOpenAI APIAPIs
Before you enrol

This course information is supplied through the AI CERTs catalogue. Kiwi Fern Education will confirm current availability, delivery arrangements, inclusions and fees before enrolment. The programme is not presented as a New Zealand Qualifications Authority qualification.

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