I remember the 90s when I wanted to get a home
loan and it took me 3 months to complete the process from providing all
the hard copies of my income, tax returns, identity
proofs then bank checked my creditworthiness & provided the approval.
home loan, auto loan, education loan, two wheeler loan or even loan to buy
appliances like HD TV and Refrigerator.
are so many cases of defaulters, which keeps increasing and hence established
banks or lenders constantly looking for ways to improve the returns or
proactively identify risks.
loan applicant’s credit score, a three-digit number obtained from credit
bureaus such as the TransUnion, Experian, and Equifax. But these credit
scores are based solely on credit-history and do not take into account rich
data available, which can potentially give lenders access to data points as
varied as online purchases, the strength of social connections and travel
patterns. When viewed this data holistically, lenders can get a complete
picture of potential borrowers & can significantly improve their ability to
predict loan defaults.
Today digital transformation has changed everything. While the interest rate and closing costs
on loans are still primary considerations, the speed, simplicity, transparency
and customer service of the entire process is important.
gen Z continues to increase, they tend to purchase property and acquire assets
that will provide stability & generate wealth.
loan products drives a significant portion of new loans. The difference for a
digital-first customer is that they do their shopping online and may select an
alternative provider based on the right combination of cost and ease of
determine the creditworthiness of those who don’t have any credit history like
students or immigrants etc. It also helps to improve customer experience, e.g.
by showing pre-approved loan amount. AI makes loan approvals quick and easy,
reduce operational costs and these savings can then be extended to customers in
the form of lower rates. Artificial Intelligence can process large amounts of
data that human underwriters would simply not be able to make sense of.
drastically reduces the likelihood of errors and significantly cuts down the
time it takes to approve a loan and disburse funds to the borrower, thereby
enhancing the customer experience.
fraud by comparing customer behavior with the baseline data of normal customers
and removing outliers.
lenders are also looking at the digital footprint, payment data from other
sources, purchase history, professional reputation from LinkedIn and other
use of machine learning to analyze this alternative data in loans and credit
rating is going to raise some privacy, ethical, and legal concerns.
friction associated with the borrowing process, eliminating paperwork and
moving all of the steps of the customer journey to an online and mobile
capability. AI and Machine learning will become an inherent part of financial
Bigdata and data center