Fine-Tuning AI for Financial Services: Risk Prediction and Fraud Detection Training Course
Refining involves tailoring pre-trained AI models to suit specific industries and datasets.
This instructor-led, live training session (available online or at your venue) is designed for experienced data scientists and AI engineers within the financial sector who aim to refine models for purposes such as credit scoring, fraud detection, and risk modelling, utilising financial data specific to the industry.
Upon completion of this training, participants will be capable of:
- Refining AI models using financial datasets to enhance fraud and risk prediction.
- Implementing techniques like transfer learning, LoRA, and regularisation to boost model efficiency.
- Incorporating financial compliance requirements into the AI modelling process.
- Deploying refined models for operational use within financial services platforms.
Course Format
- Interactive lectures and discussions.
- Numerous exercises and practice sessions.
- Practical implementation within a live laboratory environment.
Course Customisation Options
- To request tailored training for this course, please contact us to arrange.
Course Outline
Introduction to AI in Financial Services
- Use cases: fraud detection, credit scoring, compliance monitoring
- Regulatory considerations and risk frameworks
- Overview of refining in high-risk environments
Preparing Financial Data for Refining
- Sources: transaction logs, customer demographics, behavioural data
- Data privacy, anonymization, and secure processing
- Feature engineering for tabular and time-series data
Model Refining Techniques
- Transfer learning and model adaptation to financial data
- Domain-specific loss functions and metrics
- Using LoRA and adapter tuning for efficient updates
Risk Prediction Modelling
- Predictive modelling for loan default and credit scoring
- Balancing interpretability vs. performance
- Handling imbalanced datasets in risk scenarios
Fraud Detection Applications
- Building anomaly detection pipelines with refined models
- Real-time vs. batch fraud prediction strategies
- Hybrid models: rule-based + AI-driven detection
Evaluation and Explainability
- Model evaluation: precision, recall, F1, AUC-ROC
- SHAP, LIME, and other explainability tools
- Auditing and compliance reporting with refined models
Deployment and Monitoring in Production
- Integrating refined models into financial platforms
- CI/CD pipelines for AI in banking systems
- Monitoring drift, retraining, and lifecycle management
Summary and Next Steps
Requirements
- Understanding of supervised learning techniques
- Experience with Python-based machine learning frameworks
- Familiarity with financial datasets such as transaction logs, credit scores, or KYC data
Audience
- Data scientists in financial services
- AI engineers working with fintech or banking institutions
- Machine learning professionals building risk or fraud models
Need help picking the right course?
southafrica@nobleprog.co.za or +27 (0)10 005 5793
Fine-Tuning AI for Financial Services: Risk Prediction and Fraud Detection Training Course - Enquiry
Upcoming Courses
Related Courses
Advanced Fine-Tuning & Prompt Management in Vertex AI
14 HoursVertex AI equips developers and data teams with sophisticated tools for fine-tuning large models and managing prompts. These capabilities allow you to enhance model accuracy, streamline iteration processes, and maintain rigorous evaluation standards through integrated libraries and services.
This instructor-led live training, available either online or onsite, is designed for intermediate to advanced practitioners seeking to boost the performance and reliability of generative AI applications. The course focuses on supervised fine-tuning, prompt versioning, and evaluation services within Vertex AI.
Upon completing this training, participants will be able to:
- Apply supervised fine-tuning techniques to Gemini models in Vertex AI.
- Implement prompt management workflows, including versioning and testing.
- Utilise evaluation libraries to benchmark and optimise AI performance.
- Deploy and monitor improved models within production environments.
Course Format
- Interactive lectures and discussions.
- Practical labs involving Vertex AI fine-tuning and prompt tools.
- Case studies focused on enterprise model optimisation.
Course Customisation Options
- For bespoke training requirements, please contact us to arrange a tailored session.
Advanced Techniques in Transfer Learning
14 HoursThis instructor-led, live training in South Africa (online or onsite) targets advanced-level machine learning professionals who wish to master cutting-edge transfer learning techniques and apply them to complex real-world problems.
By the end of this training, participants will be able to:
- Understand advanced concepts and methodologies in transfer learning.
- Implement domain-specific adaptation techniques for pre-trained models.
- Apply continual learning to manage evolving tasks and datasets.
- Master multi-task fine-tuning to enhance model performance across tasks.
Continual Learning and Model Update Strategies for Fine-Tuned Models
14 HoursThis instructor-led, live training in South Africa (online or onsite) is aimed at advanced-level AI maintenance engineers and MLOps professionals who wish to implement robust continual learning pipelines and effective update strategies for deployed, fine-tuned models.
By the end of this training, participants will be able to:
- Design and implement continual learning workflows for deployed models.
- Mitigate catastrophic forgetting through proper training and memory management.
- Automate monitoring and update triggers based on model drift or data changes.
- Integrate model update strategies into existing CI/CD and MLOps pipelines.
Deploying Fine-Tuned Models in Production
21 HoursThis instructor-led, live training in South Africa (online or onsite) is aimed at advanced-level professionals who wish to deploy fine-tuned models reliably and efficiently.
By the end of this training, participants will be able to:
- Understand the challenges of deploying fine-tuned models into production.
- Containerize and deploy models using tools like Docker and Kubernetes.
- Implement monitoring and logging for deployed models.
- Optimize models for latency and scalability in real-world scenarios.
Domain-Specific Fine-Tuning for Finance
21 HoursThis instructor-led live training in South Africa (online or onsite) is aimed at intermediate-level professionals who wish to gain practical skills in customizing AI models for critical financial tasks.
By the end of this training, participants will be able to:
- Understand the fundamentals of fine-tuning for finance applications.
- Leverage pre-trained models for domain-specific tasks in finance.
- Apply techniques for fraud detection, risk assessment, and financial advice generation.
- Ensure compliance with financial regulations such as GDPR and SOX.
- Implement data security and ethical AI practices in financial applications.
Fine-Tuning Models and Large Language Models (LLMs)
14 HoursThis instructor-led, live training in South Africa (online or onsite) is designed for intermediate to advanced professionals who wish to tailor pre-trained models for specific tasks and datasets.
By the end of this training, participants will be able to:
- Grasp the principles of customisation and its applications.
- Prepare datasets for customising pre-trained models.
- Customise large language models (LLMs) for NLP tasks.
- Optimise model performance and address common challenges.
Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)
14 HoursThis instructor-led, live training in South Africa (online or on-site) is designed for intermediate-level developers and AI practitioners who wish to implement fine-tuning strategies for large models without the need for extensive computational resources.
By the conclusion of this training, participants will be able to:
- Understand the principles of Low-Rank Adaptation (LoRA).
- Implement LoRA for efficient fine-tuning of large models.
- Optimise fine-tuning for resource-constrained environments.
- Evaluate and deploy LoRA-tuned models for practical applications.
Fine-Tuning Multimodal Models
28 HoursThis instructor-led, live training in South Africa (online or in-person) is designed for advanced professionals who want to master multimodal model refinement for innovative AI solutions.
Upon completing this training, participants will be able to:
- Comprehend the architecture of multimodal models such as CLIP and Flamingo.
- Effectively prepare and preprocess multimodal datasets.
- Refine multimodal models for specific tasks.
- Optimize models for real-world applications and performance.
Fine-Tuning for Natural Language Processing (NLP)
21 HoursThis instructor-led live training in South Africa (online or onsite) is aimed at intermediate-level professionals who wish to enhance their NLP projects through the effective fine-tuning of pre-trained language models.
By the end of this training, participants will be able to:
- Understand the fundamentals of fine-tuning for NLP tasks.
- Fine-tune pre-trained models such as GPT, BERT, and T5 for specific NLP applications.
- Optimize hyperparameters for improved model performance.
- Evaluate and deploy fine-tuned models in real-world scenarios.
Fine-Tuning AI for Healthcare: Medical Diagnosis and Predictive Analytics
14 HoursThis instructor-led live training in South Africa (online or onsite) targets intermediate to advanced medical AI developers and data scientists who wish to fine-tune models for clinical diagnosis, disease prediction, and patient outcome forecasting using structured and unstructured medical data.
Upon completion of this training, participants will be able to:
- Fine-tune AI models on healthcare datasets, including EMRs, imaging data, and time-series data.
- Apply transfer learning, domain adaptation, and model compression techniques within medical contexts.
- Address privacy concerns, bias, and regulatory compliance during model development.
- Deploy and monitor fine-tuned models in real-world healthcare settings.
Fine-Tuning DeepSeek LLM for Custom AI Models
21 HoursThis instructor-led, live training in South Africa (online or onsite) is designed for advanced-level AI researchers, machine learning engineers, and developers who wish to fine-tune DeepSeek LLM models to create specialised AI applications tailored to specific industries, domains, or business needs.
By the end of this training, participants will be able to:
- Understand the architecture and capabilities of DeepSeek models, including DeepSeek-R1 and DeepSeek-V3.
- Prepare datasets and preprocess data for fine-tuning.
- Fine-tune DeepSeek LLM for domain-specific applications.
- Optimise and deploy fine-tuned models efficiently.
Fine-Tuning Defense AI for Autonomous Systems and Surveillance
14 HoursThis instructor-led, live training in South Africa (online or onsite) is designed for advanced defence AI engineers and military technology developers who wish to fine-tune deep learning models for use in autonomous vehicles, drones, and surveillance systems, whilst meeting stringent security and reliability standards.
Upon completion of this training, participants will be equipped to:
- Fine-tune computer vision and sensor fusion models for surveillance and targeting operations.
- Adapt autonomous AI systems to dynamic environments and varying mission profiles.
- Implement robust validation and fail-safe mechanisms within model pipelines.
- Ensure strict alignment with defence-specific compliance, safety, and security standards.
Fine-Tuning Legal AI Models: Contract Review and Legal Research
14 HoursThis instructor-led, live training in South Africa (online or onsite) is designed for intermediate-level legal tech engineers and AI developers who wish to fine-tune language models for tasks such as contract analysis, clause extraction, and automated legal research within legal service environments.
Upon completion of this training, participants will be able to:
- Prepare and cleanse legal documents for the fine-tuning of NLP models.
- Apply fine-tuning strategies to enhance model accuracy on legal tasks.
- Deploy models to assist with contract review, classification, and research.
- Ensure compliance, auditability, and traceability of AI outputs in legal contexts.
Fine-Tuning Large Language Models Using QLoRA
14 HoursThis instructor-led, live training in South Africa (online or onsite) is aimed at intermediate-level to advanced-level machine learning engineers, AI developers, and data scientists who wish to learn how to use QLoRA to efficiently fine-tune large models for specific tasks and customisations.
By the end of this training, participants will be able to:
- Understand the theory behind QLoRA and quantisation techniques for LLMs.
- Implement QLoRA in fine-tuning large language models for domain-specific applications.
- Optimise fine-tuning performance on limited computational resources using quantisation.
- Deploy and evaluate fine-tuned models in real-world applications efficiently.
Fine-Tuning Lightweight Models for Edge AI Deployment
14 HoursThis instructor-led, live training in South Africa (online or onsite) is aimed at intermediate-level embedded AI developers and edge computing specialists who wish to fine-tune and optimise lightweight AI models for deployment on resource-constrained devices.
By the end of this training, participants will be able to:
- Select and adapt pre-trained models suitable for edge deployment.
- Apply quantization, pruning, and other compression techniques to reduce model size and latency.
- Fine-tune models using transfer learning for task-specific performance.
- Deploy optimized models on real edge hardware platforms.
