Choosing the Right AI for Debt Collections: Custom Language Models vs. Large Language Models

In this episode of Credit Shift, we dive into the world of AI in debt collection. They break down the key differences between custom language models and large language models, tackling the big question—why does it matter?

The conversation gets into the challenges of using AI in regulated industries, the importance of truly understanding customer intent, and where AI is headed in customer interactions.

We also chat about why one-size-fits-all AI doesn’t cut it in debt collection and how tailored solutions can boost efficiency and compliance.

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https://youtu.be/rqjK9lhXFSM

Key Takeaways:

• Custom language models are built for specific industries, making them more accurate and reliable.
• Large language models can sometimes miss the mark, generating irrelevant or incorrect responses.
• AI improves customer interactions by recognising intent and understanding context.
• Industry-specific training is essential to ensure AI provides meaningful and compliant responses.
• AI hallucinations can be risky, especially in finance, where accuracy is critical.
• Recognsing customer vulnerabilities is key to ethical and effective debt collection.
• AI isn’t a magic fix—it’s a tool that needs the right setup and oversight.
• The future of AI includes smarter features like conversational summaries and co-pilot assistance.
• Tailored AI models can dramatically cut down failed conversations in debt collection.

Keywords

AI, debt collection, custom language models, large language models, digital transformation, finance, generative AI, digital debt collection, NLP, compliance