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Income estimation at the heart of responsible lending

In today’s financial market, it’s critical to have a comprehensive view of your customers’ ability to pay in both the short and longer term. Understanding an individual’s income levels allows you to make more informed decisions about their credit capacity.

In addition, regulators are also placing greater onus on organisations to ensure that every decision is made according to the best interests of the customer. You will need to ensure that you offer precisely the right financial products to each customer at the right time, and that these products continue to meet the customer’s needs over their lifetime.

New regulations, job losses, limited wage increases, tax and interest rate rises are impacting disposable income and putting pressure on customers’ ability to meet their payment commitments. These challenges are impacting lenders’ ability to identify customers with a higher credit capacity, and to understand which customers can afford to take on new credit or have an increase to their credit line. Income estimation offers lenders a new way to manage this increased risk to protect the organisation and its customers.

Do your customers have the ability to pay?

Whilst credit scoring has been used successfully for many years to assess the creditworthiness of new applicants for credit it has some limitations: it has tended to focus on establishing an individual’s propensity to pay rather than their ability to pay.

Consumers with a high willingness to pay may not be able to afford to buy an asset at a given price; which means that, if the source of income is not verified, ability to pay could be overlooked, resulting in the wrong decisions being made.

That’s why (especially where credit bureau information is unavailable) many lenders are examining in detail their processes on income and expenditure determination and the calculation of net disposable income estimates. Their scope is to enhance the ability to pay calculation that will allow them to understand the key drivers of customer performance and to demonstrate, where required, compliance with regulators.

Estimating income using advanced analytics

Understanding the true income of customers requires sophisticated solutions. By using advanced analytics you can assess a customer’s complete financial picture and improve decision making by providing in-depth insight into a customer’s overall credit capacity.

The “philosophical” reasoning behind income modelling may be readily apparent – for example, a consumer whose credit history reflects a mortgage open for five years with a given payment, an instalment loan for a car with a given payment and open revolving accounts, and who has had no material delinquencies is likely to have a certain amount of income in order to support those payments.

The advanced analytics approach to income estimation covers the below elements:

  • Definition of data layer for the identification of internal and external data sources that are relevant to observe or estimate income
  • Calculation of target variable through the definition of the target variable for modelling
  • Development of clusters for the creation of homogeneous groups that can be specifically used for estimating separated models
  • Build of models and validation through the application of analytical techniques to obtain income estimation models and validate them on an on-going basis
  • Definition of the business strategy to incorporate the income estimation into the day-to-day lender’s activities
  • Monitoring of results to ensure the impact to the business is aligned with expectations

Benefits of income estimation models

Income estimation models allow – through multiple uses across the life cycle –reduced manual and expensive verification procedures, better understanding of the consumer’s ability to pay, improved underwriting processes, measurement of the customer’s potential value in the near future, and increased collections recovery by targeting the individuals most likely to be able to pay.

Income estimation models also provide greater efficiencies by validating consumer income in real time, a better consumer experience by protecting consumer privacy using an alternative to consumer-stated income and cost savings by improving efficiency through time-consuming prioritisation and expensive income verification.

Implementing a robust method for estimating income can help increase the security of lenders in finding the right customers and making them the right offer. A flexible, practical and compliant measure of consumer’s capacity to take on additional credit enables the achievement of financial success and business growth through enhanced customer experience.

Author: Enrico Libbiani, Head of CSDA Analytics, Experian EMEA