Credit scoring in the traditional banking system is based on analyzing financial information about potential borrowers. Banks evaluate the timeliness of payments on previous transactions, the total amount of debt, types of credit products, etc.
However, what about unbanked consumers who have no credit history? Until recently, the verdict was unambiguous: such people could not expect to get a loan.
Recently, the situation has changed – providers of
Digital footprint refers to the data that remains on the web as a result of a user’s interaction with online resources or the use of digital devices.
It includes registrations on online platforms, creation of social media profiles, subscriptions to various services, clicks on links, online purchases, and much more.
There are several types of digital footprint:
Active. It includes information that users share on the web on their own. This can be data from accounts in social networks, avatars on various resources, publications in blogs, etc.
Passive. This is data that Internet resources collect without the participation of users. This includes information about the geolocation and demographics of the audience, the type of device from which the site is accessed, etc.
Positive. This is a digital footprint that has a positive impact on the digital credit rating and allows the borrower to be assigned a low risk level.
Negative. Such information, on the contrary, has a bad effect on the assessment of a person’s creditworthiness. It suggests the likelihood of fraud and allows you to deny a loan.
Unlike traditional data, digital footprint goes far beyond the credit history of a potential borrower. It allows the lending organization to obtain complete information about the borrower’s creditworthiness from alternative data sources.
What can be learned from an in-depth digital footprint analysis?
1. Financial status. Conclusions about the material statuses of applicants can be drawn from what devices they use (iOS or Android), on which email host their mailboxes are created (paid or free), and whether they subscribe to paid services.
2. Consumer behavior. Alternative data providers can check how regularly a potential borrower makes payments on paid subscriptions, is registered on gambling platforms, and makes online purchases. From this, it can be deduced what habits a person has and how they may affect the repayment of the loan.
3. Personal qualities of the borrower. Analysts say that certain personal qualities can indicate a propensity to default. One of them is impulsiveness, which can manifest itself in shopping on trading floors at night, among other things.
In addition to all of the above, digital footprint research allows you to assess the likelihood of fraud on the part of a potential borrower.
Certain data about an applicant’s online activity may allow a lending…