AI Applications in Finance and Fraud Detection Training Course

Module 1: Foundations of AI in the Financial Ecosystem

• Overview of the evolution from Rule-Based Systems to AI-driven banking
• Identifying the ROI of AI in front, middle, and back-office operations
• Introduction to the FinTech AI stack: Python, Scikit-Learn, and specialized APIs
• Understanding the lifecycle of a financial machine learning project
• Key challenges in FinAI: Data privacy, latency, and non-stationarity of markets
• Practical session: Mapping a financial workflow and identifying high-value AI use cases

Module 2: Data Engineering and Preprocessing for Finance

• Handling imbalanced datasets in fraud: Techniques for oversampling (SMOTE) and undersampling
• Feature engineering for time-series financial data: Rolling windows and momentum indicators
• Cleaning "noisy" financial data and managing missing values in transactional logs
• Normalization and scaling strategies for volatile market variables
• Integrating alternative data: Satellite imagery, social media, and loT data for finance
• Practical session: Preprocessing a million-row credit card transaction dataset for model readiness

Module 3: Supervised Learning for Fraud Detection

• Implementing Random Forests and XGBoost for binary fraud classification
• Evaluating model performance: Why Accuracy is misleading in fraud (Precision-Recall curves)
• Cost-sensitive learning: Accounting for the financial impact of False Negatives
• Real-time vs. Batch processing: Architectures for instant fraud blocking
• Ensemble methods: Combining multiple models to increase detection rates
• Practical session: Building a supervised fraud detection model with Scikit-Learn and XGBoost

Module 4: Unsupervised Learning and Anomaly Detection

• Detecting "unknown unknowns" using Isolation Forests and One-Class SVMs
• Clustering transactions with K-Means to identify unusual spending patterns
• Dimensionality reduction for visualization using PCA and t-SNE
• Autoencoders for reconstruction-based anomaly detection in banking logs
• Dynamic thresholding: Adapting to changing consumer behavior patterns
• Practical session: Implementing an Isolation Forest to detect outlier transactions in an unlabeled dataset

Module 5: Credit Risk Modeling and Scoring Systems

• Transitioning from FICO scores to AI-driven behavioral scoring
• Logistic Regression vs. Neural Networks for Probability of Default (PD)
• Feature selection for credit risk: Identifying the most predictive borrower attributes
• Stress testing and scenario analysis using Monte Carlo simulations
• Managing model bias in lending to ensure fair and equitable credit access
• Practical session: Developing a credit scoring engine and evaluating its Gini coefficient

Module 6: NLP for Financial News and Sentiment Analysis

• Mining financial reports and earnings calls using text analytics
• Sentiment analysis of news feeds to predict short-term stock movements
• Named Entity Recognition (NER) for extracting tickers, companies, and figures from text
• Using Transformers (BERT/FinBERT) for financial document classification
• Automated summary generation for investment research reports
• Practical session: Building a sentiment analyzer for real-time financial news headlines

Module 7: Deep Learning for High-Frequency Trading (HFT)

• Introduction to Recurrent Neural Networks (RNNs) and LSTMs for price prediction
• Convolutional Neural Networks (CNNs) for pattern recognition in "Candlestick" charts
• Reinforcement Learning (RL) for autonomous trading agent development
• Dealing with market microstructure and execution latency in AI models
• Backtesting strategies: Avoiding look-ahead bias and overfitting
• Practical session: Training an LSTM model to predict 5-minute price intervals of an equity asset

Module 8: Anti-Money Laundering (AML) and KYC Automation

• Graph Theory and Network Analysis: Identifying "Money Laundering Circles"
• Automating "Know Your Customer" (KYC) through AI-driven document verification
• Link analysis to detect shell companies and ultimate beneficial owners (UBO)
• Reducing "False Positives" in AML alerts using intelligent secondary screening
• Behavior-based AML: Monitoring shifts in historical transaction velocity
• Practical session: Visualizing a transaction network to identify suspicious cyclical fund flows

Module 9: Explainable AI (XAI) and Model Interpretability

• The "Black Box" problem in finance: Why regulators demand transparency
• Implementing SHAP and LIME to explain individual fraud flags to auditors
• Global vs. Local interpretability: Understanding how the model works as a whole
• Feature Importance vs. Feature Contribution in financial decision-making
• Documenting AI logic for "Model Risk Management" (MRM) compliance
• Practical session: Generating an interpretability report for a denied credit application

Module 10: Regulatory Compliance and Future Trends in FinAI

• Navigating the AI Act, GDPR, and Basel III/IV requirements for algorithms
• Model validation and "Drift" monitoring: Ensuring models stay accurate over time
• Cybersecurity for FinAI: Protecting models from adversarial attacks
• The impact of Quantum Computing on future financial encryption and modeling
• Roadmap for organizational AI maturity: From pilots to production
• Practical session: Designing an AI governance framework and model monitoring dashboard

Requirements:

• Participants should be reasonably proficient in English.
• Applicants must live up to Armstrong Global Institute admission criteria.

Terms and Conditions

1. Discounts: Organizations sponsoring Four Participants will have the 5th attend Free

2. What is catered for by the Course Fees: Fees cater for all requirements for the training – Learning materials, Lunches, Teas, Snacks and Certification. All participants will additionally cater for their travel and accommodation expenses, visa application, insurance, and other personal expenses.

3. Certificate Awarded: Participants are awarded Certificates of Participation at the end of the training.

4. The program content shown here is for guidance purposes only. Our continuous course improvement process may lead to changes in topics and course structure.

5. Approval of Course: Our Programs are NITA Approved. Participating organizations can therefore claim reimbursement on fees paid in accordance with NITA Rules.

Booking for Training

Simply send an email to the Training Officer on training@armstrongglobalinstitute.com and we will send you a registration form. We advise you to book early to avoid missing a seat to this training.

Or call us on +254720272325 / +254725012095 / +254724452588

Payment Options

We provide 3 payment options, choose one for your convenience, and kindly make payments at least 5 days before the Training start date to reserve your seat:

1. Groups of 5 People and Above – Cheque Payments to: Armstrong Global Training & Development Center Limited should be paid in advance, 5 days to the training.

2. Invoice: We can send a bill directly to you or your company.

3. Deposit directly into Bank Account (Account details provided upon request)

Cancellation Policy

1. Payment for all courses includes a registration fee, which is non-refundable, and equals 15% of the total sum of the course fee.

2. Participants may cancel attendance 14 days or more prior to the training commencement date.

3. No refunds will be made 14 days or less before the training commencement date. However, participants who are unable to attend may opt to attend a similar training course at a later date or send a substitute participant provided the participation criteria have been met.

Tailor Made Courses

This training course can also be customized for your institution upon request for a minimum of 5 participants. You can have it conducted at our Training Centre or at a convenient location. For further inquiries, please contact us on Tel: +254720272325 / +254725012095 / +254724452588 or Email training@armstrongglobalinstitute.com

Accommodation and Airport Transfer

Accommodation and Airport Transfer is arranged upon request and at extra cost. For reservations contact the Training Officer on Email: training@armstrongglobalinstitute.com or on Tel: +254720272325 / +254725012095 / +254724452588