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party verifiers to cross-check their own findings. Other resources might
include tax records or utility bills. Once the loan is granted, Alibaba
continues to track customer behavior and spending patterns of the loan
provided to them and will also monitor business development.
Lenddo and Kreditech are other companies that utilize data scientists’
abilities to manage risk for recipients of automated loans. These companies
have developed credit scoring techniques that are helpful and innovative in
determining a customer’s creditworthiness. Sometimes even data from
online social networks is used to gauge a customer’s creditworthiness.
The Finance Industry and Real-Time Analytics
The finance industry can’t just rely on having a compilation of data that is
easily accessible. What really matters when it comes to data is when it’s
analyzed. It’s just not possible to rely on the data itself to make an informed
decision – it must be analyzed at regular intervals. Sitting on compiled data
for extended lengths of time decreases the likelihood to seize critical
opportunities and increases the possibility of things going awry. However,
by using the skills of data scientists the lag in time that once was an issue in
the finance sector is no longer worrisome. There are several benefits to
having real-time analytics as a function of your business.
Reducing the occurrences of fraud may be one of the most important
contributions of real-time data analytics when it comes to protecting the
consumer’s information. Banks and credit card companies, along with
others, have made it common practice to prioritize securing fundamental
account information with the use of big data. They want to ensure your
employment status is accurate and your location is up to date in the case
that something occurs that is out of the “normal” behavior pattern.
“Normal” behavior patterns include characteristics such as spending
patterns, account balances, credit history analysis, and other general – yet
important – details. With real-time analytics, the company is able to notice
almost instantaneously when something seems out of character for a
customer and can trigger a flag on the account. Usually, the account will be
suspended in case the activity is fraudulent and the account holder is
notified of the activity and suspension.
Big data real-time analysis is also beneficial for improving credit ratings.
An accurate credit rating literally requires amassing an ample amount of
current data, placing less focus on historical data. Since credit ratings are
such an important part of securing almost any kind of loan or asset, it’s
important that the applicant has an accurate and up-to-date credit rating to
determine the level of risk of an individual. Being available in real-time
gives a reasonable assumption about the financial capacity of a customer.
With the online marketplace readily available, many categories used for
calculating credit ratings are easily accessible. These can include
transaction histories, business operations a customer may be engaged in,
and other assets that are held by the customer.
Another benefit to customers is the ability to provide accurate pricing for
various products and services. For example, in the case of a loan, the
customer may be able to secure a better interest rate on borrowed funds if
their credit rating is higher than when they previously applied for financing.
Tools offered by car insurance companies are another service that uses real-
time data analysis to warn drivers of accidents or traffic jams in their path,
weather conditions, and other factors that may help reduce accidents.
Customers who choose to use these tools are typically offered benefits on
their insurance policy such as discounts or other rewards.
When data analytics is utilized, the cost of business is reduced across the
board. In today’s business arena, it is mostly attributable to real-time
analytics. Many major financial institutions turn to PriceStats, an online
firm that monitors prices for more than twenty-two countries to provide
real-time inflation rates for those economies. PriceStats was originally
founded to monitor only a few countries in South America but now can
monitor the international market. The information provided by PriceStats
allows businesses to tailor their actions in accordance with inflation rates
around the world.
How Big Data is Beneficial to the Customer
Many banks and other financial institutions pay to acquire data from
retailers and service providers. This is the foundation of data importance,
especially when the capacity to analyze and use the data exists. In reality,
all data is important depending on the reason someone wants it. Storing data
for the sake of having it is a waste; it becomes an unnecessary distraction
when it simply sits in a system, and no one accesses it. Even the influx of
massive amounts of data is useless if there is no program in place to process
and analyze it. That’s why having a system in place that can sort data
among and between departments is critical to share relevant data with
appropriate departments rather than disregarding and throwing it aside.
When necessary, irrelevant data can be discarded if it serves no purpose to
someone in particular or an entire department.
This chapter places great emphasis on the data scientist because knowing
how to handle and utilize data is a skill that not everyone innately
possesses. Data scientists have learned and refined their skills to be able to
help businesses deal with the massive amounts of data available to them.
Big data is especially helpful when it comes to customer-based industries.
The companies that pay for data acquisition aim to gain a full and well-
rounded perspective of their customer. Many businesses use the term KYC
– Know Your Customer – to delineate which customers may pose a risk to
engage in business transactions with, known as customer segmentation. The
information obtained to make this term relevant is credible because it is
acquired from big data to form an understanding of the customer. This
concept is supported by a leading figure with IBM Analytics, Sushil
Pramanick, who is also the founder of TBDI – The Big Data Institute.
Customer Segmentation is Good for Business
By segmenting the customer base, it is easier to meet their needs.
Categorizing customers based on similar financial capacity and age, similar
consumer tastes in the same income bracket and from the same cultural
background narrows the focus to meet their needs accordingly. You can
design products targeted at their preferences, you can tailor advertising
techniques to appeal to them and retain them as customers, and you will be
better able to re-engineer or invent products focusing on specific groups.
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