Avant is a fintech that has helped more than 2 million people get the funding they deserve. Committed to working every day to create new solutions that help move financial lives forward, Avant has entrusted Spring Labs to help them implement generative AI to improve their customer experience.
Chapters
0:00 - Introduction
Jeff Brogger welcomes everyone and introduces the panel.
3:00 - Panel Introductions
John Sun, co-founder and CEO at Spring Labs introduces himself and talks about AI updates. Paul Zhang and Johnny Reinsch introduce themselves.
8:00 - Overview of Avant's AI Implementation
Discussion on how Avant is integrating AI to deflect customer service calls.
15:00 - Challenges and Solutions
Key challenges faced by Avant and the solutions provided by AI technology.
25:00 - Impact on Customer Experience (CX)
How AI is enhancing customer experience and service efficiency at Avant.
35:00 - AI's Role in Reducing Costs
Insights into cost reduction and operational efficiency through AI.
45:00 - Q&A Session
The panel answers questions from the audience about AI implementation and future prospects.
1:00:00 - Closing Remarks
Final thoughts from the panel and thank you from Jeff Brogger.
Questions Asked By Attendees
"We are working on an AI-powered chat with Spring Labs for our online banking solution. The tool works beyond our expectations, but one senior customer service professional is concerned that using AI-powered chat could create support calls rather than reduce them. I don't see that this would happen, but I'm curious if there is data that supports that actual call volume for self-service is reduced without generating additional calls regarding the use of the AI-powered chat service."
"With activities that have high regulatory impact through non-compliance, how are you monitoring AI-based decisioning (preventive/detective) controls? Three areas of particular interest:
1. How are you managing the 'black box' risk and lack of trace back on decisioning activities?
2. Managing AI 'hallucination' - losing context to the specific customer's data or guardrails in allowable messaging.
3. Is coalescing context across all the datasets (calls, messaging, account activity) challenging for the AI solutions?"
"What percentage would you say the LLM (Large Language Model) gets you there? Paul said 80%, and this owl slide makes it seem like the LLM is more like 20%."
"Is the Spring solution developed as a repeatable product, or should I view each implementation as primarily custom?"
"What is an example of a 'hyperbolic claim'?"
"In the current environment, do you think it’s possible to partner with an AI vendor without speaking to the engineering team?"
Webinar Recap
Welcome to our break down of the key points from our recent webinar on leveraging Generative AI to enhance customer service. The session featured insights from industry leaders and covered innovative ways to utilize Generative AI to improve customer experience. Here's a detailed summary of the conversation.
Introduction
The webinar kicked off with Jeffrey Brogger, Director of Marketing at Spring Labs, introducing the panelists:
John Sun, Co-founder and CEO at Spring Labs
Paul Zhang, CIO and Co-founder at Avant
Johnny Reinsch, Co-founder and CEO of Tradable
Each panelist shared their background and their excitement about discussing the advancements in Generative AI.
The Role of AI in Enhancing Customer Experience
Jeffrey emphasized that the core narrative of the webinar was centered around improving customer experience through AI. Paul Zhang from Avant highlighted how they aim to deflect 15% of customer service calls and enhance the overall customer experience using AI.
Early Adoption of AI at Avant
Paul discussed Avant's early adoption of technology for digital-only consumer lending. From the outset, Avant focused on using data science, AI, and machine learning to make better lending decisions and improve customer interaction through digital channels.
Generative AI: Short and Long-term Focus
Paul elaborated on Avant's short and long-term goals for integrating Generative AI. They are exploring large language models to analyze and generate text, with use cases in compliance, customer support, and fraud detection.
Challenges with Traditional Customer Service Chat
Avant had previously removed their chat function due to mixed results, focusing instead on providing best-in-class phone support. However, Paul expressed optimism about reintroducing chat with the help of Generative AI to create a better customer experience.
Analysis of Customer Interactions
Jeffrey and Paul discussed the analysis conducted on Avant's customer interactions, revealing that about half of the customer service tasks were self-serviceable. This led to the exploration of how Generative AI could improve these interactions and reduce the burden on customer service teams.
Measuring Success
Success indicators for implementing AI include improving customer Net Promoter Score (NPS) and increasing the use of self-service tools. Paul emphasized the importance of balancing self-service with maintaining high levels of customer satisfaction.
Customer Preference for Self-service
The panelists discussed the preference for self-service among younger cohorts and how empowering customers with self-service options can lead to higher satisfaction and engagement.
Positive Impact on Customer Service Teams
The conversation touched on the benefits of reducing repetitive tasks for customer service teams, which can prevent burnout and allow staff to focus on more meaningful interactions.
Financial and Customer Engagement Impact
Paul shared insights on the potential financial impact of AI, estimating significant cost savings and improved customer engagement. The scalability and iterative nature of Generative AI offer exciting opportunities for continuous improvement.
Partnering with AI Vendors
Johnny and Paul highlighted the importance of evaluating AI vendors, focusing on clear-eyed acknowledgement of the product development lifecycle and avoiding hyperbolic claims.
Future of AI in Financial Services
The panelists discussed the transformative potential of AI in financial services, emphasizing the need for a collaborative approach to develop innovative solutions that meet customer needs.
Conclusion
The webinar concluded with a Q&A session, addressing concerns about AI-powered chat services potentially generating more support calls, the importance of speaking with engineering teams when partnering with AI vendors, and the future of AI in customer service.
For those interested in exploring how Generative AI can transform their customer service operations, Spring Labs offers a risk-free proof of concept (POC) to demonstrate the potential benefits tailored to your specific use case.
Stay tuned for future webinars and blog posts as we continue to explore the exciting world of AI and its impact on customer service.