The Big Picture

Delinquency is not only an unfamiliar situation for credit consumers but also one which is most often accompanied by temporary/permanent financial difficulty. Therefore, consumers display very little ability to cope with it. In this context, consumers display an ‘avoidance’ tendency – making themselves difficult to reach and making promises that they can’t keep. This leads to significant operations costs, write-offs, and frustration amongst service agents.

Transformative Solution

  1. Behavioral Sciences principle of ‘Commitment’ was deployed in the agent-customer conversation script to strengthen consumers intent to pay the dues one he has made a ‘promise to pay’.
  2. Behavioral Sciences principle of ‘Loss Aversion’ and ‘Reciprocity’ was brought into play to ensure that consumers prioritize delinquency resolution over other financial commitments.
  3. The scripts were customized to different delinquent customer segments – fresh tickets, 90 days and 180 days delinquent customers.

The solution was deployed across the bank’s multiple call centers throughout the country with minimal training requirement.

The Change

The scripting changes were intuitively appealing to the agents as they received a favorable response from customers. The subtleness of the solutions ensured that the company was able to scale the solution across multiple centers in the country. The key results of this deployment were:

  1. A 40% reduction in the number of connects with delinquent consumers before a resolution is achieved
  2. A 20% reduction in unresolved delinquencies
Deliver next best product recommendations during customer interactions

The Big Picture:

A leading retail bank was facing low customer engagement and satisfaction with its customers. The existing analytical models on product propensities generated lower accuracy, and missed critical data elements, such as offline and online interactions and transactions, and prevented an objective arbitration of offers among multiple competing product offers. This resulted in sub-optimal customer experience and lower response rates.

Transformative Solution:

To address the company’s challenges, a new next best product and service recommender was built using deep learning. It was designed to predict the top three recommendations from among a wide suite of products, and for services.

A single customer view was prepared with 4,000+ attributes such as customer product holdings, transactions, in-bank transactions, and online interactions.

The models were tested on a select population within the lead scoring platform and deployed centrally.

The Change:

As a result of the engagement, customer product-offtake rates jumped by 60%, resulting in significantly higher marketing ROI.