Fintech Takes x C&R presents Collections Conversations Episode 6: Humans in the Fintech Loop

Welcome back to Collections Conversations, a new miniseries from Fintech Takes, sponsored by our friends at C&R Software.

The series digs into how generative AI is reshaping debt collections; what it enables, what it complicates, and why it might finally force the industry to retire the word “collections” altogether.

In Episode 6, I sit down with Pedro Maya, Head of Collections and Credit Risk Execution at Tangerine, to talk about the biggest misconception he encounters inside banks: that AI will automate and fix everything. It won’t.

AI amplifies whatever you already have. Good design gets better. Bad design gets worse faster, and at scale.

Training teams to work alongside AI across the credit lifecycle turns out to be less a technology question than an accountability one. The human guardrail AI can’t offer is a question teams need to keep asking: is this the right outcome for the customer?

We get into two concrete use cases where that plays out. Quality assurance (QA), where analyzing every agent call (rather than a monthly sample) turns a compliance exercise into coaching; and agentic AI, and agentic AI, where offloading basic customer interactions frees agents for the ones that require them.

The KPI landscape is shifting, too. The new metrics are effectiveness-based: cure rates, NPS, and dollars collected as a function of the quality of the interaction (rather than its volume).

And because this is Collections Conversations, we close on the longer view: what does the ideal human-AI collaboration look like across the credit lifecycle two or three years from now, and what, then, has to go right between here and there.

This episode is brought to you by C&R Software.

More than just debt collection, C&R sets the global standard for AI-native, humanized credit management. They simplify the complex with end-to-end credit-risk lifecycle support, powered by automated workflows, AI-native intelligence, and real-time, data-driven decisioning. Learn more at https://hubs.ly/Q03Wl1DY0.

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