How Digital Footprint Data Will Revolutionize the Lending Industry

“The growth of the internet leaves a trace of simple, easily accessible information about almost every individual worldwide – a trace that we label “digital footprint.” Even without writing text about oneself, uploading financial information, or providing friendship or social network data, the simple act of accessing or registering on a webpage leaves valuable information,”[1]

As people become more concerned that Big Tech is monitoring their activity on their phone, it is time to understand how industry-changing FinTech’s are analyzing retailers’ Digital Footprint Data in order to make lending decisions. With the Coronavirus spreading across the world, there has been a sweeping change in people’s relationship with physical currency; they are not using it. According to Shift, there are 1.06 billion credit cards in the United States held by over 70% of the population.

What does this create for lenders? Endless Digital Footprint Data Points.

The days are coming to an end where lenders collected income statements, analyzed debt repayment history, and were concerned by the length of a credit card’s life. Instead, every aspect of a retailers’ transaction indicates their repayment behavior. This sounds pretty straight forward, right? The following metrics are only surface level date points FinTech’s are using to develop credit profiles for potential lendees:

  • Customers coming from a price comparison website are almost half as likely to default as customers being directed to the website by search engines
  • Customers having their names in the email addresses are 30% likely less to default on a loan repayment
  • Purchases between noon and 6PM are approximately half as likely to default as customer from midnight to 6pm

The current credit scoring model is not an impeccable system, however, it is not nearly as intrusive as the future lending models project.

Overview of Credit Scores; The Problem

According to Investopedia, a Credit Score is a number between 300–850 that depicts a consumer’s creditworthiness. Lenders use credit scores to evaluate the probability that an individual will repay loans in a timely manner.[2]

Fair Isaac Corporation (FICO) is the industry leading model used to derive a score to represent the credit profile of a potential investor. For those who’ve never analyzed the breakdown of a credit score, see below:

Payment History35%Whether an individual pays their credit accounts on time
Accounts Owed30%The amount of money an individual owes (Ratio of money owed to credit available)
Length of Credit History15%The longer the credit account is open, the better
Credit Mix10%The mix of retail accounts, credit cards, installment loans (mortgages/vehicle loans)
New Credit10%Recently opened accounts

The combination of these factors yields a number 300-850.

Fico-Credit-Score-Rating

The above score is rather mediocre in determining one’s ability to repay lines of credit. Critics question the current model for a various reasons:

  • Data from traditional credit bureaus can be falsified or stolen[3]
  • Financial lending companies can lack security compromising the lendee’s profile[4]
  • Roughly 25% of Americans are unbanked or underbanked[5]

This is where Digital Footprint Data is disrupting the lending industry.

Alternative Lenders Analyzing Digital Data Footprints

FinTech lenders are using infinite touchpoints in determining one’s ability to repay loans. The easiest way to present the potential touchpoints lendees must consider as the actions relating to their digital footprint data will have potential financial implications. Before diving into credit scoring, it is important to note that every company uses different digital footprint metrics, however, some core touchpoints are universal.

The Device (Footprints)

First, as a consumer, using a computer versus a tablet versus a mobile phone has strong implications about the profile of the lendee. Next, is the consumer using an iPhone or an android? iPhone users are more likely to pay their bills than Android users! Now what is the area code of the phone? Is the phone most active at 10 P.M. or 8 A.M.? Do you see where we’re going with this?

Digital-Data-Footprint

Say the consumer is on a computer; what is the brand and operating system running on the computer? Again, statistics support iOS users’ ability to repay loans over Windows. What is the I.P. address of the connection? What time is the user operating the computer? According to the FDIC, owning an iOS device is one of the best predictors for being in the top quartile of the income distribution.[6]

As shown there are infinite metrics to use in developing a creditor’s profile. The questions posed above are indented to illustrate the implications of minute variables most would consider irrelevant but, instead, are driving factors in lenders’ credit decisions.

The Purchase (Digital Data)

Like the variables surrounding the device used to purchase the item, the same types of questions can be begged about the item being purchased:

  • How did the customer find out about the product?
  • Did the customer click a banner on the side of the page?
  • Was the product found from a search on google?

Is the customer using a promotion code or buying the product at face value?

The narrative around the product itself has vast implications about a potential lendee’s character. An individual that purchases a new Mac Laptop at 12 P.M. in a Starbucks speaks differently than a person buying a flight to Las Vegas at 2 A.M. off their android. Though it might not be true, the profile of the buyer is “riskier” from a lending perspective.

Let’s take this a step further; when filling out the billing information, does the purchaser properly capitalize the proper nouns? Does the email address used include their name? Is the email address domain an old spam site or standard host?

Some might say details like this are minute, but used in aggregate, they can develop a powerful profile for the customer.

Interested in Big Tech? Check out an alternate perspective HERE!

Equal Credit Opportunities ACT

Like the implementation of BigTech applications, the digital footprint data could come across as a violation of users’ information. Think about it for a second, though… For FICO to generate a score for applicants, they must use one’s social security number, bank records, and have access to their lines of credit. For a lender to generate a credit profile using digital footprint data, they use relatively harmless touchpoints.

Further, Title VII of the Consumer Credit Protection Act prohibits discrimination on the  basis of race, color, religion, national origin, sex, marital status, age, receipt of public assistance, or good faith exercise of any rights under the Consumer Credit Protection Act.[7]

Do the digital footprint data metrics discussed above violate the Equal Credit Opportunity Act? No.

The digital footprint provides an alternative, potentially more fair, profile for those lacking access to banking.

How to Contain your Digital Data Footprint

In conclusion, the overall goal of FinTech’s moving into the space of digital footprint data profiling is to create inclusion for those that aren’t banked or are underbanked. Two billion working aged people in the world lack access to financial services![8[

As an American that is skeptical of Big Tech’s role in “creating equality,” how do you hedge against the invasion of privacy? There’s truly only one answer: buy goods and services in cash.

Curious about more topics in personal finance? Check out our ‘Personal Finance’ section HERE!

Article Sources

  1. FDIC “On the Rise of FinTechs – Credit Scoring Using Digital Footprints
  2. Investopedia “Credit Score
  3. Finezza “How Digital Footprint Data is Shaping the Future of Credit Scoring?
  4. Finezza “How Digital Footprint Data is Shaping the Future of Credit Scoring?
  5. CNBC “25% of American Households are Either Unbanked or Underbanked
  6. FDIC “On the Rise of FinTechs – Credit Scoring Using Digital Footprints
  7. Federal Trade Commission “Equal Credit Opportunity Act
  8. The World Bank “2 Billion: Number of Adults Worldwide Without Access to Formal Financial Services

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