The financial services sector has always focused on the use of Data and Technology as their differentiated strategy. Even as a lot has changed in terms of the acquisition, buying risk, consumer behaviour and product requirements due to the current crisis, the Financial sector has worked diligently to adopt new technology, various digital tools for new ways of customer acquisition, and various types of data for better understanding of customers. In the current competitive environment, organisations invest in technology to collect relevant data from multiple sources and process them using AI/ML to get better insights about their consumers.

Alternate data has had many definitions over the years. However, to put it simply, any data readily available in the digitised form but not related to conventional repayment history is called alternate data. There are various sources of collecting this information using technology and digital platforms. Alternate data has evolved over many years to being currently used across the lending ecosystem. This data provides a better understanding of consumers, their needs, behaviour, and their financial discipline, which becomes key to forecasting customer performance on the lending proposition. Alternate data not only mitigates risks for financial institutions but also helps consumers become eligible for more credit based on deep analysis and understanding of their payment history on alternative accounts that traditionally would not have been considered.

One of the significant challenges towards adopting alternate data is that it comes from various sources and is found in different structures. To address this challenge, an organisation must build a robust ecosystem within which to use such data. The key steps to set up a qualified ecosystem are:

• A compliant source of information
• Depth and breadth of data sources
  Consistency of information and data points usage
• Predictive model fit
• Data accuracy

Before using this data, an organisation must curate the data and put it in a structured format. The use of advanced analytics can indicate a pattern to be used for better insights on customers. These insights can be used for various purposes during the consumer life cycle. The structured data such as mobile phone, utility bill, rental information, etc., are sources of alternate data that provide deep and reliable insights into the customer spending pattern, financial discipline, and payment behaviour. Simultaneously, there are various unstructured data sources such as social media, e-mail, internet usage etc., which cannot be taken at face value. With the use of AI/ML, a pattern can be established between the two, reflecting on overall customer behaviour and lifestyle.