Role of Alternative Data in Ascertaining Credit Eligibility and Reducing Possibility of Fraud

The increasing speed of digitalization of transactions is generating a greater quantity and variety of consumer data, thereby expanding the store of data that lending institutions can feasibly use to determine the creditworthiness of applicants. In addition, the worldwide proliferation of open financial infrastructures has facilitated data exchange between industries participants, the availability of customer data will increase. Making use of machine learning, a rising quantity of applicant's financial data & history is utilised to construct prediction models for determining creditworthiness and cross selling in the banking industry. However, a number of FinTechs have created fundamentally new methods for evaluating creditworthiness that use a wider range of data sources. Consequently, the current study's goal was to examine how different data contribute to the assessment of borrowers' creditworthiness. The sample size for the research is 200 respondents (99 government workers and 101 private sector bank employees), who were selected using the quota sampling technique. Most of the primary data used to complete this study was through the administration of questionnaires to respondents to answer a number of questions related to credit eligibility, alternative data, social media-based alternative data and key considerations for psychometric data application. were received. Using SPSS software version 25, correlation and regression tests were used to analyse the data. While analysing the credit eligibility of a bank's clients, the research found that factors of Major Considerations have the maximum impact on Credit Eligibility; however alternative data, social media-based alternative data, and psychometric data play a substantial and beneficial role in determining credit eligibility and fraud detection.