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100% fundable

AI+ Finance Agent Specialty™

Agent-based AI solutions are transforming how financial decisions are prepared, made, and documented. This continuing education course hones the necessary expertise to deploy intelligent financial agents in a targeted and responsible manner in practice.

Key Topics

  • Analysis of financial datasets using AI-powered agent systems.
  • Automated management of reporting, risk models, and fraud prevention.
  • Developing decision-making logic based on agent-based models.
  • Ensuring data integrity, governance, and compliance in AI environments.
  • Expanding and integrating existing financial structures with modern AI tools.

Prerequisites
: Anyone familiar with financial processes and data who is open to digital technologies can get started right away.

Target Audience
Suitable for professionals in Controlling, Accounting, Finance, Risk, and Audit who want to understand and implement agent-based AI systems both strategically and operationally.

This course builds confidence in working with AI agents—the foundation for informed decisions, efficient processes, and a future-oriented finance department.

Course Content
  • Learn about the differences between AI agents and traditional financial automation.
  • How AI agents have evolved in the financial services sector.
  • An overview of different types of agents in the financial sector.
  • Agent autonomy and task distribution in a financial context.
  • Key differences between AI agent systems and rule-based automation.
  • Hands-on exercise: Exploring AI agents in the financial sector.
  • Technical Design and Architecture of AI Agents in Finance.
  • Libraries and development environments for agent systems.
  • AI Agents Compared to Static Models.
  • Phases in an agent’s life cycle.
  • Use Case: Customer support agents in banks for KYC, FAQs, and transaction disputes.
  • Case Study: Erica (Bank of America)—a virtual assistant with more than 1 billion interactions powered by predictive AI.
  • Hands-on exercise: Building and understanding AI agents in the financial sector.
  • Supervised and Unsupervised Machine Learning for Fraud Detection.
  • Pattern and behavior analysis as the foundation for security systems.
  • Real-time monitoring agents in action.
  • Use Case: AI agents detect anomalies in real time during digital payments.
  • Case Study: PayPal uses graph-based agents for fraud detection with 99.9% accuracy across 0.32% of all transactions.
  • Hands-on exercise: Develop agents for fraud detection and anomaly monitoring.
  • Feature extraction from non-traditional credit data.
  • Explainability (XAI) in automated credit decisions.
  • Identifying and reducing biases in credit-granting agents.
  • Use Case: Agents assess credit-neutral risk using transaction and mobile phone data.
  • Case Study: Upstart increased its loan approval rate by 27% and lowered the annual percentage rate (APR) for borrowers by 16% using CFPB-approved AI.
  • Hands-on exercise: Set up AI agents for credit scoring and lending.
  • Personalization through profiling agents in the investment sector.
  • Real-time algorithmic portfolio rebalancing.
  • Sentiment-aware investing as an AI strategy.
  • Use case: An AI agent optimizes the portfolio weekly based on market trends and financial goals.
  • Case Study: Wealthfront’s Path Agent recommends personalized savings and investment goals based on behavioral models.
  • Hands-on exercise: Build AI agents for wealth management and robo-advisory.
  • Reinforcement Learning for Automated Trading Strategies.
  • Forecasting models based on historical market data.
  • Risk-return management through threshold control.
  • Use Case: AI agents execute arbitrage trades between crypto exchanges.
  • Case Study: Renaissance Technologies automates short-hold trades using AI and achieves consistent alpha returns.
  • Hands-on exercise: Design and test trading bots and market monitoring agents.
  • Large Language Models (LLMs) in the Analysis of Earnings Announcements and Financial Reports.
  • Automatic summarization and event detection using AI.
  • Speech-to-text processing and extraction of key statements.
  • Real-world use case: NLP agents analyze financial reports and corporate announcements.
  • Case Study: BloombergGPT—a specialized language model for financial data.
  • Hands-on exercise: Using NLP agents to analyze financial documents.
  • AI-powered processes for Anti-Money Laundering (AML) and Know Your Business (KYB).
  • Rule modeling with regulatory awareness.
  • Transaction graph analysis for pattern recognition.
  • Use Case: An agent monitors suspicious cross-border transfers in real time.
  • Case Study: HSBC increased the detection of suspicious AML activities by 30% using Quantexas AI agents.
  • Hands-on exercise: Integrating compliance and risk agents into financial systems.
  • Governance frameworks for AI in the financial sector (RBI, EU AI Act).
  • Transparency and traceability of decision-making logic.
  • Fairness and explainability as requirements in AI systems.
  • Use Case: Verifiable agent logs ensure fair lending practices during internal audits.
  • Case Study: Following regulatory audits, Wells Fargo implemented internal AI fairness audits for lending bots.
  • Hands-on exercise: Building fair and verifiable AI agent systems in the financial sector.
  • Case Study 1: JPMorgan’s COiN Platform for Contract Analysis
  • Case Study 2: AI-Powered Fraud Detection – PayPal’s Decision Intelligence
  • Case Study 3: Data-Driven Credit Scoring – Upstart’s Lending Platform
  • Final Project
  • Key Takeaways and Learning Objectives of the Course

Up to 100% funding for your continuing education

Attractive funding opportunities are available for numerous continuing education programs – depending on the funding program, up to 100% funding is possible.
The requirement is that you live or work in Germany. Our funding opportunities are not available outside of Germany.

Funding opportunities are available for career starters, job seekers, career changers, startups, experienced professionals, those seeking career advancement, and specialists.