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Carol Byrne | 08 March, 2023

The power of audio analysis in debt collections & recovery

When treatment paths are solely left up to AI and Machine Learning (ML), customers may be sent down an ineffective repayment plan. Audio analysis humanizes collections and recovery by identifying customers facing difficulty in ways that are invisible to data, ML and segmentation.

Not only does this improve collections and recovery performance by ensuring customers are in the correct treatment paths, but helps those that would otherwise fall through the cracks of existing systems and processes.

What is audio analysis in debt collections & recovery?

Audio analysis enables collections and recovery teams to deepen their understanding of their customers. The linguistic AI used in audio analysis is able to distinguish a customer’s emotional state based on the tone and language used, highlighting whether they are at risk or vulnerable. This works hand in hand with machine learning (ML) technology to distinguish the right treatment for different customers.

Real-time audio analytics serves as a tool to coach and guide agents during their calls. The AI analyzes speech and recommends responses based on the data and preferred treatment path for the customer. Both these states of audio analysis work to better understand the customer and provide them with the help they need to resolve their debt.

How can audio analysis enhance collections and recovery?

Data driven guidance and efficiency

AI algorithms are based on thousands of conversations to understand the most productive and engaging approaches in a range of situations. By providing call agents with real-time audio analysis, they can use data-driven guidance to make decisions to better help individual customers with basic or complex situations. The analysis also offers constructive feedback to improve performance and efficiencies. For example, ​​McKinsey found that implementing ‘assertive’ script elements doubled the promise-kept rate to 95%, an increase of 2.2x.

Ensuring compliance

Audio analysis’ listening to calls in real-time detects anomalies in existing scripts. Collections and recovery teams can use these insights to correct any discrepancies and ensure compliance and adherence to their processes. This protects customers, ensuring they’re informed of everything they need to know.

Hitting KPIs more efficiently

KPIs are a vital element of customer calls, giving credit issuers and organizations hired on their behalf to distinguish agent performance against the expectations of the business. RPC, call quantity and average call duration should be at sustainable levels to ensure performance and the capability to help a large quantity of customers.

Audio-analytics enable collection and recovery teams to coach and train their agents in both real-time using AI, and post call via recording. This training then optimizes calls with customers by reaching resolutions faster and more efficiently. As a result, KPIs are met and more customers are supported through their debt management and resolution.

Improve your understanding of your customers with Debt Manager

By utilizing audio analysis and AI, collections and recovery teams can enhance their capability to help their customers manage distressed debt. The ability to understand emotion and vulnerability enables teams to help customers facing difficulties that are otherwise invisible to standard data analysis. These features are made all-the-more powerful when used in combination with other records to provide true, accurate and in-depth profiles of consumers; which is where Debt Manager comes in.

Debt Manager works in unison with audio analytics by helping you build datasets that you can use to train algorithms, understand consumer responses and learn how this affects your collections performance. To find out more about how Debt Manager can help humanize your approach to customer support, contact a member of our team today.

A guide maximizing customer experience during debt collection
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A guide to maximizing customer experience during debt collection

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