Generative AI in Risk Management: What It Is and Why Now?
Updated: November 18, 2024
Summary
The video delves into the realm of generative AI and its application in risk management, showcasing its superiority over traditional machine learning techniques. It discusses various tools like Chat GPT and Bard, highlighting how generative AI enhances fraud detection, natural language processing, and credit scoring. The speaker emphasizes the need for human supervision in leveraging generative AI's strengths to refine risk systems, automate analyses, and improve operations efficiency significantly. Strategies to mitigate model hallucination in fraud detection and the crucial role of model governance in adopting generative AI are also touched upon. Overall, the video emphasizes the transformative impact of generative AI in revolutionizing risk management processes.
TABLE OF CONTENTS
Introduction to Generative AI
Overview of Generative AI
Applications of Generative AI
Advantages of Generative AI
Challenges and Concerns
Refining Risk Systems with Generative AI
Evaluation of Next-Gen Risk 3.0 Vendors
Streamlining Risk Operations
Generative AI Impact in Credit Risk Management
Bank Transaction Categorization
Fraud Detection and Scenario Analysis
Advice for Using Generative AI in Fraud Context
Handling Model Hallucination in Fraud Detection
Model Governance in Generative AI
Introduction to Generative AI
Discussion on the hottest topic in technology - generative AI and its application to risk management, providing a basic definition of generative AI.
Overview of Generative AI
Exploring the capabilities of generative AI, comparing it to traditional machine learning, and discussing tools like Chat GPT, Bard, Mid journey, Jasper, and more.
Applications of Generative AI
Examining various applications of generative AI in fraud detection, natural language processing, credit scoring, risk assessment, computer vision, and semiconductor design.
Advantages of Generative AI
Discussing the advantages of generative AI, including improved accuracy, lower false positives, better explainability, transparency, fairness, and ethical considerations.
Challenges and Concerns
Addressing concerns such as data ownership, decision-making, and recommendations, highlighting the need for human supervision and ensuring right recommendations.
Refining Risk Systems with Generative AI
Exploring how generative AI can assist in refining risk systems, improving operations efficiency, detecting complex fraud types, and automating postmortem analysis.
Evaluation of Next-Gen Risk 3.0 Vendors
Providing insights on recalibrating the approach for evaluating Next-Gen risk 3.0 vendors, focusing on a layered approach, data democratization, natural language interface, knowledge base, and data-driven case management.
Streamlining Risk Operations
The speaker discusses how utilizing data can streamline risk operations, reduce costs, and increase analysts' efficiency by 10 times with minimal effort. They highlight the importance of assurance in being able to test, trace, audit, govern, and explain models within a platform like R 3.0.
Generative AI Impact in Credit Risk Management
Generative AI, specifically in credit risk models, is impactful in feature extraction to identify hidden patterns in data, make better recommendations for transactions, and personalize product or feature recommendations based on unique risk profiles. It is also used in predicting characters in email patterns for fraud detection.
Bank Transaction Categorization
The use of generative AI in categorizing bank transactions to decipher cryptic codes on statements and create a profile of income and expenses. This application aids in underwriting by categorizing unstructured data to assess financial health and expenses.
Fraud Detection and Scenario Analysis
Generative AI is utilized in fraud detection for identity theft, false information, and fraud applications, along with scenario generation for stress testing and economic impact analysis on credit portfolios. It plays a crucial role in improving underwriting models and fraud detection through proactive simulations.
Advice for Using Generative AI in Fraud Context
The speaker advises leveraging generative AI strengths to complement existing models by automating feature generation and using natural language co-pilots for enhanced productivity in risk operations. They emphasize the importance of simulation, identifying attack vectors, and using generative AI to fight fraud.
Handling Model Hallucination in Fraud Detection
Strategies are discussed to mitigate model hallucination in fraud detection using techniques like retrieval augmented generation, continuous fine-tuning, and establishing KPIs to evaluate model performance. Maintaining a knowledge base and ensuring traceability are essential to reduce false positives and negatives in decisions.
Model Governance in Generative AI
The challenges and importance of model governance in adopting generative AI for fraud detection are addressed. The speakers suggest leveraging feature extraction and co-pilots before full model integration to navigate model complexity and ensure compliance with governance requirements.
FAQ
Q: What is generative AI?
A: Generative AI is a type of artificial intelligence that is capable of creating new data, such as images, text, or even financial transactions, based on patterns it has learned from existing data.
Q: How does generative AI differ from traditional machine learning?
A: Generative AI goes beyond traditional machine learning by not only predicting outcomes but also generating new data examples that resemble the training data.
Q: What are some applications of generative AI?
A: Generative AI is used in fraud detection, natural language processing, credit scoring, risk assessment, computer vision, semiconductor design, and more.
Q: What are the advantages of generative AI?
A: Advantages of generative AI include improved accuracy, lower false positives, better explainability, transparency, fairness, and ethical considerations.
Q: How can generative AI assist in refining risk systems?
A: Generative AI can assist in refining risk systems by improving operations efficiency, detecting complex fraud types, and automating postmortem analysis.
Q: In what ways is generative AI impactful in credit risk models?
A: Generative AI is impactful in credit risk models by enabling feature extraction to identify hidden patterns, make better recommendations, and personalize product offerings based on unique risk profiles.
Q: How is generative AI used in fraud detection?
A: Generative AI is utilized in fraud detection for identity theft, false information, fraud applications, stress testing, economic impact analysis, improving underwriting models, and proactive simulations.
Q: What are some strategies to mitigate model hallucination in fraud detection?
A: Strategies to mitigate model hallucination in fraud detection include retrieval augmented generation, continuous fine-tuning, and establishing KPIs to evaluate model performance.
Q: What is the importance of model governance in adopting generative AI for fraud detection?
A: Model governance is crucial in adopting generative AI for fraud detection to ensure compliance with governance requirements, reduce false positives and negatives, and maintain traceability.
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