Data analytics is used in the banking sector in several ways: (1) To better understand customer priorities and tailor offers accordingly, (2) To detect fraud by monitoring for imbalances in customer account usage patterns, and (3) To strengthen the customer base by using analytics to identify reasons for customer dissatisfaction and make improvements.

1. Data Analytics In Banking Sector:

2. Introduction:
Data Analytics:
• The term data analytics refers to the process of examining datasets to
draw conclusions about the information they contain. Data analytic
techniques enable you to take raw data and uncover patterns to
extract valuable insights from it.
• Data Scientists and Analysts use data analytics techniques in their
research, and businesses also use it to inform their decisions.

3. Uses of Data Analytics in Banking Sector:
• Knowing Customers Priorities
Analytics can help banks classify customers according to their economic
paradigms, which makes a whole lot of difference in determining their
needs and challenges. Moreover, it aids in educating the banks about
their customers, so they can tailor offers, upgrades and other details in
a fashion that’s relevant to them, and appeals to them individually,
leading to growth in productivity.
• Fraud Detection
Customers who have loan accounts, mutual fund accounts or use credit
cards have a usage pattern which can be studied by analytics and it can
determine if any major imbalance is an indicator of fraud. With the
emergence of big data, most banks rely upon monitoring systems
which are operated by human experts.

4. • Strengthening the Customer Base
When a customer wants to leave a bank, analytics can probe the
reasons as to how or why the customer is disappointed, and even
prompts upgrades to fix the situation. It’s eventually about strategy-
building, about coming up with ways to be better than your
competition and fulfilling the needs of your existing customer base.
• To Improve Marketing Prospects
With the help of analytics, banks can design products to optimize sales
and minimize attrition. Analytics plays a huge role in minimizing risks
associated with a product, as it can fathom its limitations beforehand
and also by identifying non-performing assets.

5. • For Reducing Risks
Using predictive analytics, companies can effectively manage their
risks, especially since it can monitor so many diverse forms of data sets
at once, be it raw or structured. So it can assess the potential risks
involved in any field, be it marketing or be it workforce-related. Most
importantly, it can be used to detect the root of past mistakes or bad
fiscal phases, and to determine ways to fix loopholes.

6. Application of Data Analytics in Banking Sector:
• Risk Modeling
Risk Modeling a high priority for the banking industry. It helps them to
formulate new strategies for assessing their performance. Credit Risk
Modeling is one of its most important aspects. Credit Risk Modeling allows
banks to analyze how their loan will be repaid.
• Fraud Detection
With the advancements in machine learning, it has become easier for
companies to detect frauds and irregularities in transactional patterns. Fraud
detection involves monitoring and analysis of the user activity to find any
usual or malicious pattern. With the increase in dependency on the internet
and e-commerce for transactions, the number of frauds has increased
significantly.

7. • Customer Segmentation
In customer segmentation, banks group their customers based on their
behavior and common characteristics in order to address them
appropriately. In this scenario, machine learning techniques like
classification and clustering play a major role in determining potential
customers as well as segmenting customers based on their common
behaviors.
• Real-Time Predictive Analytics
Predictive Analytics is the process of using computational techniques to
predict future events. Machine Learning is the main toolbox of
predictive analytics. Machine Learning is an ideal tool for improving the
analytical strategy of the banks. With the rapid increase in data, there is
an abundance of use cases and the exigency of analyzing data is at its
peak.