Complaints in consumer finance are a big deal. On one side, regulatory risk and the CFPB’s increased attention to complaints are major problems, and on the other, complaints mean customers waving a great big flag that says, “I might be leaving you for a competitor!”

Neither of those is good news.

So we put together a panel to talk about new ways to identify and capture complaints, and what to do with them when you’re done.

Check out the takeaways from Hanna Turner of Spinnaker Consulting Group (and formerly of Capital One), Paul Carlisle of Risk Management Solutions Group, and Scott Hamilton of Prodigal (formerly Capital One, Bank of America, and Chase).

Why complaints matter and why they’re tough to manage

Customer complaints have always been difficult to manage, but Scott highlighted how important it is to figure out how to do that effectively. 

“In many roles, you find yourself looping back to complaints because it's where so many things show themselves. Anything from NPS issues to compliance issues. It's a goldmine, but it's very, very tough, gold to mine.”

Hanna laid out six pieces of the puzzle for financial institutions to get a handle on when managing complaints:

1. Defining what constitutes a complaint
2. Training agents to identify and manage complaints
3. Structuring captured complaints data into models
4. Getting leadership buy-in and trust
5. Building effective reporting
6. Making data actionable

And it’s that last piece, she said, that is so central. “We didn't do all this just to collect data. I mean, sometimes we do because let's face it, the regulators want the data. But what they really want us to do with that is to drive change.”

Paul agreed. “Regulators are looking for specific instances of where you've leveraged that complaint data, and made adjustments to bank practices based on what you're learning from customers.”

Strategic planning for effective complaints management

The panelists emphasized that regulators expect institutions to properly identify, track, and resolve consumer complaints. 

That means having clearly defined policies, comprehensive training for personnel, and effective systems to record and track complaints. 

Leveraging complaints data for regulatory compliance and customer experience improvement

Understanding why customers are calling and using machine learning models to convert unstructured data into structured data can help identify root causes faster. Complaints serve as a window to the consumer and their experiences, providing early indications of potential issues with specific areas, products, or services.

By integrating complaints data with other business data, financial service providers can gain deeper insights and drive necessary changes. 

“I've learned that there's a lot of really good information in both negative and positive customer feedback. And the better we listen to our customer, the better chance we have to wow them,” said Hanna.

The role of AI in complaints management

Every step of the process Hanna outlined has been difficult to institute. And technology hasn’t brought relief.

Existing technology brought in to address complaints management has, Paul explained, “required a lot of manual calibration and upkeep to even get close to being functional,” and in use, “typically would just bury you in false positives or negatives requiring review. And so it puts pressure on your limited staff to perform the activities and really adds little to no value to the complaint management operation and just kind of muddies the water with respect to what's truly important.”

But the increasing role of Artificial Intelligence (AI) and machine learning (ML) in these use cases is a different story.

AI not only improves the accuracy of identifying and categorizing complaints but also enhances the overall customer experience by addressing their concerns more effectively. 

ML models are now more accurate than human judgment in identifying and categorizing complaints, as they can interpret the conversation and context, truly identifying whether a complaint occurred.

How AI is different

Scott said he wishes he’d had AI to manage complaints when he was working in banking management. 

“It really gets down to the models being so accurate, and so trained on exactly this type of conversation, that they're actually better than a human. Not just reasonably good, but actually better than if you had the same human doing it hundreds of millions of times. They can interpret the conversation, the context of the conversation, and can truly identify whether a complaint occurred. It is much cleaner and faster and more actionable.”

Key takeaways

1. AI is the future of effective complaint management in financial services, improving accuracy and efficiency.
2. Strategic planning is essential, including clear policies, comprehensive training, and effective systems.
3. Complaints data is crucial for regulatory compliance and customer experience improvement.
4. Complaints should be captured even if resolved during a call, as they provide valuable insights.
5. Integration of complaint data with other sources provides a comprehensive view of customer experience.

Complaints are not to be feared, but rather seen as changes for growth and improvement. Missing or mis-identifying them isn’t just a regulatory risk, it’s a missed opportunity.

Learn how AI trained specifically for consumer finance can transform your complaints management strategy.

Want to see the whole conversation? Catch the recording here:

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