Artificial Intelligence has reached a seminal state and is being extensively used to underwrite the loans based on alternate data sources like telephone records and social footprint for the underbanked borrower. Despite these innovations, most debt collection methods seem to be stuck in the past with a major dependency on traditional approaches. Driven by aggressive targets, collection teams main focus is to approach customers in multiple ways possible and get repaid. In this process, the lack of separation between the risk segments could result into unpleasant recovery calls to the valuable low risk customers. This is at the cost of impacting customer experience.
With a low success rate of debt-collections, are the current ways of operating at their optimum best? Is Artificial Intelligence the answer to make the existing debt collection processes more effective? And is it really possible to make current systems intelligent to avoid an excessive rip and replace cost? Download this whitepaper to know more.
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