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Evaluating First-Time Defaulters
From the Inside Out

Intelligent segmentation helps lenders identify and target new opportunities


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Evaluating first-time defaulters from the inside out - Intelligent segmentation helps lenders identify and target new opportunities sheds light on the ways banks and other financial institutions can interact and utilize data analytics to identify and retain first-time defaulters in order to build profitable, long-term relationships.

By applying a predictive modeling approach, first-time defaulters can be evaluated across the customer development lifecycle, but with implied differences involving customer acquisition, customer servicing, cross- and up-selling and customer retention. Once first-time defaulters have been identified, banks and financial institutions may create targeted offers that improve short- and long-term profitability.

This approach is based upon three steps:

  1. Collect, format and manipulate data. Gather historical account, product and customer data, external demographics and psychographics data and evaluate as it relates to pre-underwriting/profitability model and “propensity to buy” models. This segmentation helps to confirm that prospective customers have a high likelihood of wanting to buy products or open an account and are within the financial institution’s risk tolerances.
  2. Identify customer segments. Develop customer clusters based on the preliminary risk profile along with potential profitability and “propensity to buy” using unbiased, assumption-free analytical methods.
  3. Define value propositions for each identified segment. Target the customer segments identified as potentially profitable with customized offerings that they are likely to buy and may become profitable to the bank.

Exhibit 1: Customer segmentation approach

In the enclosed findings, we examine these enhanced capabilities that may allow financial institutions to effectively target, to acquire and to retain liquidity-seeking first-time defaulters in a challenging market.

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