https://www.datatobiz.com/blog/data-science-in-fintech/ Data Science has played a significant role in transforming thefinance and banking industry by completely changing the ways in which they previously operated. Life has been made easier for the banking officials as well as the customers. FinTech: a new term coined for the innovation and technology methods aiming to transform traditional methods of finance with data science forming one of its integral components. Whenever you use your credit card, Amazon Pay, PayPal, or PayTm to make an online payment, the commerce company/seller and your bank, both utilize FinTech to make a successful transaction. With time FinTech has changed almost and every aspect of financial services, which includes investments, insurance, payments, cryptocurrencies, and much more. Fintech companies are heavily dependent on the insights offered by machine learning, artificial intelligence, and predictive analytics to function properly.

1. 5 Applications of Data
Science in FinTech
The Tech Behind the Booming FinTech Industry

2. 1. Credit Risk Scoring
With an aim to make “credit accessible to more number of people”, FinTech companies
use robust machine learning algorithms to predict the creditworthiness of people. This
lets them reach a wider customer base and reduce the rate of credit defaults.
Traditionally, banks use very complex statistical methods to determine the credit score
of an individual, but with the help of data science, the good and bad borrowers can be
separated in a fraction of seconds.
In order to accomplish this task, a large number of data points are utilized by the
companies. Also, all the data that is collected is further used to train the machine and
improve its performance. Therefore, data science provides a holistic view of one’s
creditworthiness.
Companies like Alibaba’s Aliloan is an automated online system that provides small
loans to entrepreneurs who otherwise would have been rejected by the banks because
they have no collateral against which the loans could be given. This automated system
collects information such as online transactions, business performance, ratings from
the customers and much more to calculate the creditworthiness of the business owner.

3. 2. Fraud Detection & Prevention
Fraud detection and prevention has always been a top priority for the FinTech
companies. At present, it is estimated that financial institutions lose about $80
million every year due to fraudulent activities. With the evolution of data science, the
ways to detect fraudulent activities have also changed. Machine learning based
algorithms are able to detect fraudulent activities better than the traditional systems
that may sometimes even produce false positives and classify a normal transaction
as a fraud as well.
The advanced fraud detection systems work on supervised and unsupervised
machine learning (ML) algorithms. Supervised ML-based systems are fed with
historical data that has been labelled as fraudulent and non-fraudulent. This data
set helps the system to classify any ongoing transaction as normal or anomalous.
On the other hand, the unsupervised ML-based systems are just fed with a large
amount of data that has not been previously classified, the system uses this data as
a training set and learns to differentiate between standard and a fraudulent activity
on the basis of transactions happening in digital space every day.

4. 3. Customer Retention & Marketing
Fintech companies collect a large amount of data from their customers which is
often used by them for financial analysis. This information can likewise be utilized
for enhancing client base and expanding their lifetime value. Customer data right
from their transactions, social media engagement, and personal information can
be taken into consideration and used to offer them a better experience.
For instance, by analysing the previous products purchased by the customers’
algorithms can be created to predict their future choices. These bits of knowledge
can also be utilized to comprehend that what sort of items must be promoted
among various age groups. FinTech companies may utilize client information to
make thorough profiles of their clients and offer them a customized program for a
superior ordeal.

5. 4. Revenue & Debt Collection
One of the biggest challenges faced by FinTech institutions is to be able to collect
revenue in timely and transparent manner. Predictive analysis and machine
learning algorithms can be used to profile customers and use the insights to
create optimal revenue collection strategies. Right from credit application scoring
to calling indebted individuals to pay the obligation, machine learning proves to
be useful.
The calculations enable a monetary foundation to monitor their clients’ activities
and subsequently discover the right time to make them a manual call for
reimbursing the obligation. These techniques not just allow the foundations to
spare a considerable measure of time yet in addition keep them from wasting
their monetary assets.

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