Segmentation and Disposition Classification through Text MiningBFSI
Imagine a debt aging like cheese. Early on, it’s fresh and manageable. But as time passes, it hardens, becoming more challenging to recover. Late stage collections deal with these “aged” debts, typically exceeding 90 days past due. It’s the final act in the debt recovery play, where traditional tactics might have failed, and more assertive strategies are employed. Just like a skilled cheese connoisseur, effective late-stage collections require strategies and expertise, to implement them to achieve success.
The client, one of the largest banks in UAE, has initiated a Proof of Concept (POC) on the usage of text mining for customer segmentation and disposition classification in late-stage collections.
Challenges
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Information Extraction from Agent Call Transcripts
Extract relevant information from agent call transcripts, including customer interactions, sentiments, and reasons for late payments.
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Topics and Aspect Modeling
Topics and aspect modeling on the call transcripts to identify key themes and topics related to late-stage collections.
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Combining Features for Disposition Classification
Combine the extracted features of topics and aspects to classify dispositions, such as payment promises, disputes, or financial difficulties.
Our team collaborated with the client to deliver the following solution tailored to meet their needs:
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Outsourcing Late Recovery Cases
Cases identified as late recovery are outsourced activities, ensuring efficient resource allocation and focus on high-value recoveries.
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In-sourcing and Resolution Strategy
The classification method points to cases that should be in-sourced, along with a resolution strategy tailored to each disposition. This approach optimizes the handling of late-stage collections and improves recovery rates.
The POC on segmentation and disposition classification through text mining offers significant benefits for the bank in late-stage collections.
Key benefits
- Improved Segmentation
By leveraging text mining, the bank can segment customers better, allowing optimized targeted collections strategies.
- Enhanced Disposition Classification
Text mining enables accurate classification of customer dispositions, facilitating appropriate actions and resolutions for each case.
- Optimized Collections Strategies
Developing segments with recovery likelihoods allows the bank to prioritize resources and focus on cases with the highest potential for recovery, improving overall collections efficiency.