x

AI+ Finance Agent Specialty™

AI+ Finance Agent Specialty™

  • 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
AVALIABLE AT COMPUNET LIMITED Enroll Now Enroll Now

Certificate Code

AP 2201

Exam Format

AI-Driven Remote Exam Proctoring

Course Overview

Important details and certification information

Instructor-led OR Self-paced course + Official exam + Digital badge
Instructor-Led: 3 Days (live or virtual)
Basic Knowledge of Financial Markets, Familiarity with Machine Learning, Programming Skills, Statistical Analysis Understanding, Interest in Financial Technology
50 questions, 70% passing, 90 minutes, online proctored exam

Certification Modules

  1. 1.1 Understanding AI Agents in Finance vs Traditional Financial Automation
  2. 1.2 The Evolution of AI Agents in Financial Services
  3. 1.3 Overview of Different Types of AI Agents in Finance
  4. 1.4 Importance of Agent Autonomy and Task Delegation in Financial Settings
  5. 1.5 Key Differences Between AI Agents in Finance and Traditional Automation
  6. 1.6 Hands-On Activity: Exploring AI Agents in Finance

  1. 2.1 Architecture of AI Agents in Finance
  2. 2.2 Tools and Libraries for Agent Development
  3. 2.3 AI Agents vs. Static Models
  4. 2.4 Overview of Agent Lifecycle
  5. 2.5 Use Case: Customer Support Agents in Banks for Handling KYC, FAQs, and Transaction Disputes
  6. 2.6 Case Study: Bank of America’s Erica: A Virtual Financial Assistant that Handles 1+ Billion Interactions Using Predictive AI
  7. 2.7 Hands-On Activity: Building and Understanding AI Agents in Finance

  1. 3.1 Supervised/Unsupervised ML for Fraud Detection
  2. 3.2 Pattern Analysis & Behavioural Profiling
  3. 3.3 Real-time Monitoring Agents
  4. 3.4 Real-World Use Case: AI Agents Monitoring Transaction Behaviour and Flagging Anomalies for Real-Time Fraud Detection in Digital Wallets
  5. 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
  6. 3.6 Hands-On Activity: Intelligent Agents for Fraud Detection and Anomaly Monitoring

  1. 4.1 Feature Generation from Non-Traditional Credit Data
  2. 4.2 Explainability (XAI) in Credit Decisions
  3. 4.3 Bias Mitigation in Lending Agents
  4. 4.4 Real-World Use Case: Agents Assessing New-to-Credit Individuals Using Transaction and Mobile Data
  5. 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
  6. 4.6 Hands-On Activity: AI Agents for Credit Scoring and Lending Automation

  1. 5.1 Personalization Using Profiling Agents
  2. 5.2 Portfolio Rebalancing Algorithms
  3. 5.3 Sentiment-Aware Investing
  4. 5.4 Real-World Use Case: AI Agent Adjusting Portfolio Weekly Based on Financial Goals and Market Trends
  5. 5.5 Case Study: Wealthfront’s Path Agent Uses Financial Behavior Modeling to Recommend Personalized Savings Goals and Investment Paths
  6. 5.6 Hands-On Activity: AI Agents for Wealth Management and Robo-Advisory

  1. 6.1 Reinforcement Learning in Trading Agents
  2. 6.2 Predictive Modelling Using Historical Data
  3. 6.3 Risk-Reward Threshold Management
  4. 6.4 Real-World Use Case: AI Trading Agents Performing Arbitrage Between Crypto Exchanges
  5. 6.4 Case Study: Renaissance Technologies Utilizes AI to Automate Short-Hold Trades, Generating Consistent Alpha via Adaptive Trading Bots
  6. 6.5 Hands-On Activity: Trading Bots and Market-Monitoring Agents

  1. 7.1 LLMs in Earnings Call and Filings Analysis
  2. 7.2 AI Summarization and Event Detection
  3. 7.3 Voice-to-Text and Key-Point Extraction
  4. 7.4 Real-World Use Case
  5. 7.5 Case Study: BloombergGPT — A Financial-Grade Large Language Model
  6. 7.6 Hands-On Activity: NLP Agents for Financial Document Intelligence

  1. 8.1 AI for Anti-Money Laundering (AML) and Know Your Business (KYB)
  2. 8.2 Regulation-aware Rule Modelling
  3. 8.3 Transaction Graph Analysis
  4. 8.4 Real-World Use Case: Agent tracking suspicious cross-border money transfers in real-time across multiple accounts.
  5. 8.5 Case Study: HSBC uses Quantexa’s AI agents to trace AML networks, increasing suspicious activity detection by 30%.
  6. 8.6 Hands-On Activity: Compliance and Risk Surveillance Agents in Financial Systems

  1. 9.1 Governance Frameworks for AI in Finance (RBI, EU AI Act)
  2. 9.2 Transparency and Auditability in Decision Logic
  3. 9.3 Fairness and Explainability
  4. 9.4 Real-World Use Case: Auditable AI Agent Logs Used During Internal Policy Audits to Ensure Fair Lending practices.
  5. 9.5 Case Study: Wells Fargo implemented internal AI fairness reviews for lending bots post regulatory scrutiny.
  6. 9.6 Hands-On Activity: Responsible, Fair & Auditable AI Agents in Finance

  1. 10.1 Case Study 1: JPMorgan’s COiN Platform
  2. 10.2 Case Study 2: AI in Fraud Detection – PayPal’s Decision Intelligence
  3. 10.3 Case Study: AI-Driven Credit Scoring – Upstart’s Lending Platform
  4. 10.4 Capstone Project
  5. 10.5 Key Takeaways of the Module

AI Tools Covered

Python
TensorFlow
Pandas
NumPy
Power BI
SQL
OpenAI API
APIs
Enroll Now Enroll Now