It is common knowledge that large volumes of data are
being constantly generated and a good portion of this can be used to better
understand a potential borrower. This profusion of data has only provided
greater depth and reach to lenders. The emergence of alternative data as a
catalyst in expanding credit delivery and financial inclusion is unmistakable.
It not only expands the scorable population but also deepens the understanding
of their payment behavior.
The popular saying “the only thing that is constant is
change” applies to the way lenders use technology and scoring solutions to
understand the creditworthiness of applicants. Credit Risk Management has come
a long way from the days when banks used one credit score cut off to decision
loan applications. Risk managers now have a plethora of solution options to
craft a credit policy that hits the right balance between risk and reward.
Traditional vs.
Alternative Data Defined
Traditional data typically refers to data that credit
bureaus maintain on their files. This includes data from loan applications,
credit lines, loan repayment history, credit inquiries, and public information
like bankruptcies. Traditional data is FCRA compliant and the acid test is that
it must be verifiable and disputable by the customer.
Industry research has shown that scoring solutions that
use traditional data cannot score a significant section of the population.
According to the Consumer Financial Protection Bureau (CFPB), these ‘credit
invisibles’ number over 45 million people1. It further
points out that although this segment of the population may not have a regular
loan payment track record, they may still be paying their other bills
regularly. And for this reason, it is very important to track this payment
history – e.g. utility payments – to estimate their credit risk.
Definitions of alternative data may vary, depending on
where you look. But in a broad sense, it pertains to data that includes, but is
not limited to rent payments, mobile phone payments, cable TV payments as well
as bank account information, such as deposits, withdrawals or transfers.
The Pros and Cons of
Alternative Data
While alternative data has a very important role in
financial inclusion, it also has other important benefits. In addition to
improving the assessment of the risk of the customer, it can provide timely
information to lenders on activities that may not be reflected on bureau data.
Further, it enables lenders to provide enhanced customer experience. For
example, when they share an online bank account, the loan application
processing may be faster.
Like traditional data, alternative data is susceptible to
inaccuracies. Consumers may not be able to readily review and correct
alternative data although the standards governing it are constantly changing
and evolving to meet customer and regulatory expectations.
1 Kreiswirth, Brian. “Using alternative
data to evaluate creditworthiness.” 2017. //www.consumerfinance.gov/about-us/blog/using-alternative-data-evaluate-creditworthiness/
I had the opportunity
to participate in the #Lend360 event in Dallas, Texas (Sep 25 – 27 2019)
as a speaker. It was a great event that focused on online lending
industry, especially Fintechs, with over 800 lending professionals and
sponsors in attendance.
I was a member of the panel on “Serving Everyday America: Products and Services for the Non-Prime Market”. I spoke on how #Alternativecreditdata enables lenders to understand and reach their customers in three big ways.
Alternative
Credit Data is now being used by prime as well as non-prime lenders to
book new customers. By using a host of new generation Fair Credit
Reporting Act (FCRA) approved credit risk solutions, lenders can now
understand their customers better and hence tailor products and services
accordingly. Alternative data is also a key component that can help
expand #Financialinclusion initiatives to deliver financial services to
the hitherto unserved sections of society.
In my discussions I highlighted the following three -
1. The
US has an estimated 53 million adults who are outside the purview of
traditional credit bureaus. It is now well known that alternative data
can identify and score approximately 90% of this population
2. Secondly,
alternative data enables lenders to differentiate between consumers
with similar traditional credit bureau profiles. This is because it can
provide granular segmentation and a 360 degee view of applicants that is
possible only when we use FCRA compliant not-tradeline data.
3. Thirdly,
from an acquisition perspective, whatever segments the lender targets
using alternative data, for the same risk – or charge offs – alternative
data is capable of booking more accounts.
In his second term in office, Prime Minister Modi has talked about making
India a US$ 5 trillion economy by 2024-20251. This has not only generated a lot of
debate in India and but also has focused world attention on the Indian economy.
Many may think that
this might be a tall order for a country that till recently was home to the largest
number of utterly poor in the world. But the truth is that India may be closer
to this target than we may realize.
While
all sectors of the economy have to grow rapidly, the financial services sector
has a key role to play to reach the mark. By stepping up its inclusive program
that provides equal access to loans and other financial services to all sections
of society, it can create a multiplier effect.
The
obvious link here is that when a larger number of people borrow, especially the
poor, increased economic activity follows leading to growth in sustainable
means of income for broader sections of society. This, then helps rupture the
“vicious cycle” of poverty.
Public policy planners, to their
credit, have long been aware of the direct relationship between financial
inclusion and swift economic growth. In fact, in 2005, Dr. YV Reddy, the then Governor
of the Reserve Bank of India (RBI) had talked about financial inclusion in his
annual policy statement2. In 2008, the Dr. Rangarajan
Committee on financial inclusion3 recommended
a national mission to facilitate required policy changes.
Despite
all this, India’s progress had obviously been slow in the past. But the economic
fortunes of the poor have changed for the better – quickly and noticeably – only
in the last decade. A report published in the Times of India (TOI) in January
2019 quoting World Data Lab showed the steep fall in poverty in India and
estimated the current ‘extreme poor’ to be around 50 million.4 [According
to the World Bank, ‘extreme poor’ are those who make less than $1.9 per
day.]
It is important to see
the declining poverty levels in the context of the massive digital revolution that
is taking place in India in parallel. Contrary to what the electronic and print
media in India may have you believe, the digital revolution on multiple fronts
has aided and catalyzed the financial inclusion programs of the government.
As of December 2018, 1.23 billion people had Aadhar digital biometric
identity cards5 and over 1.21 billion
had mobile phones.6 Also, as of 2017, 80% of adults had a
bank account .7 Bulk of the new accounts were opened
with the aid of Aadhar identity cards.
Further, the country has also seen steep rise
in mobile payment transactions. According to the data released by
the National Payments Corporation of India (NPCI)
8 transactions via the Unified Payments Interface
(UPI), the country’s flagship payments platform, grew 25% and crossed Rs.1
trillion in value in December 2018.
However, millions
continue to live in poverty. India has a low credit access with only 154 loans
per 1000 adults7. This may be attributed to the
reluctance of lenders to lend to people whose credit worthiness cannot be
reasonably assessed. Unlike the US, India does not have robust credit reporting
agencies with depth of data that can help lenders in approving loans. This remains
a major challenge for credit expansion.
The good news, however,
is that the confluence of mobile penetration, establishment of a biometric
identity and the emergence of disruptive credit risk solutions that facilitate
the identification and assessment of borrower risk may set the scene for
massive credit inclusion process. Consequently, India’s efforts to eliminate
poverty may have reached a tipping point.
Many fintechs around
the world and in India are now using a consumer’s digital identity to predict
loan repayment behavior. In a report published in September 2018, the Federal
Deposit Insurance Corporation (FDIC) of the US has reported9 that
a predictive “model that uses only the digital footprint variables equals or exceeds
the information content of the credit bureau score”.
In other words, lenders
in India will now be able to assess credit risk of borrowers by using their
digital identity. This also simultaneously obviates the need to build credit
bureaus using traditional data – an expensive and time consuming effort in any
case.
The purpose of this
piece is not to speculate if India will reach the US$ 5 trillion mark by
2024-25, but to rather assess its preparedness in setting in motion a host of
services and programs that will benefit the largest number of poor. As is obvious, lifting millions of people out
of poverty is a multi-pronged, multi-mission driven exercise where the happy
meeting of cutting-edge technology and robust political will to execute the
mission are necessary and imperative conditions.
India has adequately
demonstrated its capability to execute complex projects on time and within
budget. This augers well for the extreme poor. If they rise up above poverty, so
will India, economically speaking, and crossing the US$ 5 trillion mark may
just be one of the milestones.
Modi’s achievements in
this regard, as substantiated by data from multiple sources, are substantial
and suggest that it is broad-based and truly inclusive. This is in stark
contrast to the efforts of the earlier government led by Dr Manmohan Singh who
claimed at the National Development Council that “the first claim on the
country’s resources for development”10were reserved exclusively for a particular religious
community.
It is indeed debatable if India, in its tryst with destiny, ever
managed to redeem its pledge, as Pandit Jawaharlal Nehru dreamt at that
midnight hour in 1947. Definitely data suggests that even after almost
six decades, the redemption of the pledge in terms of poverty
eradication, was not even substantial. But given the track record of
the last five years, Modi’s tryst with India is taking it places and the
poorest of poor are joining the bandwagon in their millions. And Modi
has the backing of the state-of-art technology. Of course, the claim on
the country’s resources for development will be inclusive and for all,
not the exclusive right of a select few.
The emergence of
alternative data as a key enabler in expanding credit delivery and financial
inclusion is unmistakable.
The
saying that the only thing that is constant is change, is attributed to
Heraclitus, the Greek Philosopher. This is so very relevant today in the way
lenders use technology and scoring solutions to understand the credit
worthiness of applicants. Credit Risk Management has come a long way from the
days when banks used just one credit score cut off to decision loan
applications. Risk managers now have a plethora of solution options to enable
them to craft the right risk reward balance when they design a credit policy
that would suit them.
It
is common knowledge that large volumes of data are being constantly generated
and a good portion of this can be used to better understand a potential
borrower. This profusion of data has only provided greater depth and reach to
lenders.
The emergence of alternative data as a key enabler
in expanding credit delivery and financial inclusion is unmistakable. It not
only expands the scorable population, but also deepens the understanding of
their payment behavior. The three credit bureaus, realizing the value of this
data asset have embarked on an acquisition spree.
A
basic definition of traditional data as well as alternative data will help
understand the scenario better.
Traditional Data
Traditional
data typically refers to data that credit bureaus maintain on their files. This
includes data provided by the customer in the loan applications, data on credit
lines, loan repayment history, credit enquiries as well as public information
like bankruptcies. Traditional data is FCRA compliant and the acid test is that
it must be verifiable and disputable by the customer.
Industry
research has shown that scoring solutions that use traditional data cannot
score a significant section of the population. According to the Consumer
Financial Protection Bureau (CFPB), these ‘credit invisibles’ number over 45
million people. It further points out that although this segment of the
population may not have a regular loan payment track record, they may still be
paying their other bills regularly. It is thus very important to track this
payment history – e.g. utility payments – to estimate their credit risk.
Alternative Data
Definitions
of alternative data may vary, depending on where you choose to look them up.
But in a broad sense it pertains to data that includes, but limited to rent payments,
mobile phone payments, Cable TV payments as well as bank account
information, such as deposits, withdrawals or transfers.
While
alternative data has a very important role in financial inclusion, it also has
other important benefits. In addition to improving the assessment of the risk
of the customer, it can provide timely information to lenders on activities
that may not be reflected on bureau data. Further it enables lenders to provide
enhanced customer experience. For example, when they share online bank account,
the loan application processing may be faster.
Like
traditional data, alternative data to is susceptible to inaccuracies. Consumers
may not be able to readily review and correct alternative data although the
standards governing it are constantly changing and evolving to meet customer
and regulatory expectations.
