Podcast ¦ Arum: Using AI to transform collections system delivery

Key Takeaways

1. AI and large language models (LLMs) are revolutionizing the delivery of collections and recovery system implementations, upgrades, and migrations across various sectors.

2. The integration of AI contributes to enhancing human capabilities rather than replacing them, enabling expert teams to deliver value more swiftly and accurately.

3. AI aids in the requirements gathering stage by processing extensive legacy documents to create initial drafts of business requirements efficiently.

4. A structured approach called MASK, SHARE, GENERATE, RESTORE is employed to ensure that sensitive data is not exposed during AI operations.

5. Utilizing AI for UAT test case creation can expedite the process and promote greater accuracy, reducing time spent on potential compliance issues.

6. In the design phase, AI can validate configurations and ensure consistency, significantly reducing future issues during testing.

7. AI has demonstrated the ability to identify and flag inconsistencies before they escalate, saving organizations from prolonged User Acceptance Testing (UAT) delays.

8. AI generates process descriptions, data dictionaries, and configuration summaries, enhancing overall project documentation and maintainability.

9. During testing, AI automation improves coverage and reduces manual efforts, particularly in generating test scripts and summarizing expected outcomes.

10. Strong governance measures are integrated to ensure the safe and responsible use of AI, including private model usage and expert reviews of AI outputs.

11. Organizations are encouraged to explore practical AI applications within the realms of requirements, design, and testing to alleviate project friction.

12. Future applications of AI may extend to areas such as change control, training content generation, and post-go-live monitoring of outcomes.

Key Statistics

• Over 500 best practice standards have been referenced to benchmark requirements against organizational expectations.

• An instance of AI flagging over 60 inconsistencies in a new strategy build was cited as saving weeks of UAT issues.

Key Discussion Points

1. The role of AI in transforming customer engagement and change program delivery in financial services and other sectors.

2. How AI can expedite the requirements gathering phase by processing legacy documents and extracting business needs.

3. The importance of governance in utilizing AI technologies securely and responsibly.

4. The practical impact of AI on maintaining compliance and reducing risk throughout system changes.

5. The MASK, SHARE, GENERATE, RESTORE model as a framework for safe AI integration.

6. Enhancements in design quality and maintainability through AI validation and consistency checks.

7. The reduction of manual testing efforts through AI-generated test scripts and expected outcome summaries.

8. The potential for AI tools to assist business users during UAT as a virtual quality assurance partner.

9. Validating data reconciliation and migration processes with AI to ensure accuracy in customer data handling.

10. The future outlook on AI’s capabilities within change control and training content generation for organizations.

11. Strategies for organizations to identify areas of friction and inefficiency and how AI can address these issues.

12. The importance of collaboration with AI to unlock value and enhance delivery efficiency while remaining compliant.

Podcast Description

In this episode of RM AI Insights, we explore the transformative impact of artificial intelligence and large language models on collections and recovery system implementations. Host Ella discusses key strategies for leveraging AI in various phases such as requirements gathering, design, and testing, alongside expert Owen Atkinson. The conversation emphasizes the effective and safe adoption of AI technologies, enhancing human capabilities, maintaining compliance, and ultimately delivering superior project outcomes.