Alternate Data to Serve the New to Credit

We live in an era of data explosion. New technologies and digitization have compounded the speed at which data is getting generated as well as multiplied the diversity of data points available. This phenomenon has given birth to ‘Alternate Data’.

Alternate data are data points beyond the traditional data points used for accomplishing or solving any problem or task. In the financial credit and risk modelling world, it generally refers to any data that does not directly pertain to an individual’s credit behaviour.

Traditionally credit decisioning has been limited to using data from credit bureaus, financial statements and bank account details. Around 40% of India’s adult population is not covered by Credit Bureaus. This is a staggering number considering India’s working population and a big opportunity that lenders would like to crack. However, lack of data required by traditional models of credit assessment is a huge limiting factor for lenders to be able to assess such individuals in a rightful manner and provide them with credit. You can also read more about Credit Monitoring.

Here is where Alternate Data can become a big game changer. Non-traditional data points which can be linked to an individual's credit performance can lead to creation of Alternate Data based credit decisioning models which can enable lenders to bag the opportunity of lending to the huge base of unserved customers.

What are the common types of Alternate Data being used in lending models

  • Telecom Data

This is as good as credit history data. However, since it does not show up in credit bureau reports, it has been neglected for very long. Monthly bill amounts, and repayment behaviour can provide a great alternate to traditional bureau data.

  • Banking Transaction Data

Bank statements have been a part of traditional models but the data points being looked at today in a bank statement or banking transactions are very different from what traditional models looked at. Payments being made towards insurance premiums; mutual fund SIPs and donations are new data points that can provide insights to an individual's prudence.

  • Social Networks

Contacts or connections can also provide insights. As they say birds of a feather flock together. Credit behaviour of your close contacts can help in creating lookalike models for predicting credit behaviour.

  • Geo Co-ordinates

Geo co-ordinates of an individual captured through Mobile devices also help in giving an insight into an individual's geo stability indicators. Consistent geo co-ordinates at different time periods can give a good insight to geo stability.

Alternate data provides an exciting opportunity for lenders to grab the huge segment of unserved customers.  

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