Every purchase and swipe indicates spending power. This further carves a route to the buyer's financial standing. With several options for modes of payment available, buyers now have the option to get debited and pay instantaneously or pay later through a credit card or loan. The growing aspirations of the working class have also led to a steady rise in the number of loan seekers. The easy access to credit has driven demand for many sectors as buyers are making the most of low or no-cost EMIs.
Credit scoring and approval rates changed substantially with the arrival of alternative lenders, mainly due to the adoption of new practices in collecting and analyzing potential borrower data. Alternative data has played its role in expanding horizons for financial institutions and for creating an opportunity to enter the financial sector fir technology startups and data-rich international companies. While social media, for example, as a source of data for creditworthiness assessment is still at a nascent stage, certain startups are already claiming to have incorporated information from social networks into their frameworks. In the quest to reinvent the way to assess consumer-related risk (as well as extend credit to unscored and questionable), startups were found more imaginative than traditional institutions. Alternative data requires alternative approach to data analytics, which wide adoption of machine learning and artificial intelligence brought.
As digital lending continues to grow in size, companies are looking for ways to make their services more efficient and profitable to both lenders and borrowers. And they believe artificial intelligence and big data hold the key to the future of loans. Lenders traditionally make decisions based on a loan applicant's credit score, a three-digit number obtained from credit bureaus such as Experian and Equifax. Credit scores are calculated from data such as payment history, credit history length and credit line amounts. They're used to determine how likely applicants are to repay their debts and to calculate the interest rate of loans.
Consider the massive size of real estate lending. The Fed's latest report shows mortgage debt topping $9 trillion. When including mortgages from businesses, it tops $15 trillion. Over 10 million homes and commercial properties sell each year. Equally staggering is how much data exists on the borrowers.
The traditional approach to loan-portfolio management puts collections and overall performance on one side and origination on the other, with decisions that should be closely coordinated made by separate departments often deployed across distinct software systems. But that's changing as senior managers work to strengthen ties between departments and advances in artificial intelligence allow for more nuanced -- and more inclusive -- procedures for vetting would-be borrowers. Putting origination on an equal footing with other parts of loan management does more than provide holistic overviews. It puts extra resources into gatekeeping, providing a crucial first step in credit-risk evaluation and fraud detection, a must-have for overall portfolio health. It also equips lenders to compete in today's tough economic environment, a byproduct of business shutdowns, workplace furloughs and the general public's hesitancy to congregate in a pandemic.