Business Analytics Assignment

1. ANALYTICS IN THE
FIELD OF BANKING
NIVEDITASRI N
REG NO: 2037049

2. Analytics and Its Significance
 Analytics refers to systematic analysis of data or statistics to obtain patterns within dataset and
arrive at meaningful conclusions.
 It widely deploys data and analytical tools like SAS, SAP, R, Micro Strategy, SQL etc. to derive
solutions for businesses, establish causal relationships, predict outcomes and automate decisions.
 Analytics could fit in any domain according to the need. For example, business analytics, finance
analytics, banking analytics etc.
 One of the core implications of analytics is that it can be used as an effective tool to forecast trends
and make necessary predictions.

3. Analytics and Its Significance Contd..
 Analytics highly borrows from applied mathematics, statistics,
predictive modeling and machine learning techniques.
 It enables prospective users to make inferences from the data
analyzed and make decisions or plans accordingly.
 Business analytics, specifically refers to the skills, technologies,
and practices for exploration and investigation of past business
performances in order to foster future business planning.

4. Types Of Data Analytics
 Descriptive Analytics: This type draws raw data from multiple sources to give meaningful insights
into the ‘happened’ (from the past). It gives a clear picture of the past signaling if something is
wrong or right but not delve deeper into the reasons for the happenings.
 Diagnostic Analytics: This type provides in-depth information about a particular problem. In short, it
helps diagnose the problem. This type requires that the company/firm already have sufficient data to
ease the process of data collection if not it could be a time consuming process.
 Predictive Analytics: This type refers to a much sophisticated and advanced analysis wherein
predictions are made based on available data and machine or statistical evidence. However, it is
understood that forecasting is merely an estimate whose accuracy depends on the quality of data.
 Prescriptive Analytics: This type of analytics employs tools and technologies like business rules,
machine learning, algorithms etc. which makes it relatively complex to implement and manage.
Prescriptive analytics also requires both historical internal data and external data due to the complex
nature of algorithms it is based on.

5. Analytics in the field of Banking
 Banks are a prominent financial institutions that are primarily involved in lending and borrowing
money in the form if accepting deposits and advancing of loans.
 Analytics in the field of banking or banking analytics helps in improving the process by which
banks segment, target, acquire and retain customers.
 In a broader sense, with the emergence of digital revolution, data analytics has evolved and made
its place in the banking industry.
 The growth of analytics in the field of banking has seen an upward trend ever since its evolution
in the industry.
 Analytics can widely foster improvement in customer base and for optimization of costs in the
system of banking.
 Further, by allowing for enhanced risk management, analytics curates customer understanding
through profiling and avoids risk of frauds and related threats posed to banking system.
 It thus helps banks to build, maintain and grow a healthy and profitable customer base.

6. Uses of analytics in the field of Banking
 Banking industry popularly deploys the predictive data analytics in order to curate a reliable
customer profile.
 Banks are fragile systems. They engage in transactions that would eventually reward them with
profit.
 Banks borrow from depositors and compensate them with stipulated interest rates. They further
lend money to potential borrowers in the form of loans and charge comparatively higher interest
rate. This way banks make profit.
 In order to lend to the right borrowers banks must do a background check of the potential
customer and ensure that the client has reasonable credit worthiness. Data analytics play an
important role in such situations, it helps banks to improve their capabilities and evaluate the
credit worthiness of potential customers.

7. Customer Segmentation
 Applications of data analytics is widespread. One of the most important use of analytics in the field
of banking is customer segmentation.
 Analytics facilitates customer segmentation based on customers’ historical data such as spending,
expenditure, risky behavior etc.
 Cross-selling can be personalized based on the process of segmentation.
 It becomes important to differentiate between customers that foster money making to the bank and
the customers that might lose money to the bank.
 Banks can analyze profitability of certain groups of customers and extract useful insights.
 In order to foster wallet share and create a group of loyal and affluent customer base, banks are
requires to focus on selling the right product to right customer which is facilitated by analytics.

8. Risk Modeling
 Risk assessment is of the highest priority in the banking industry.
 Risk assessment is of high priority for banks, as it helps to regulate financial activities and in the
pricing of financial investments.
 Banks have a variety of customer range inclusive of companies, groups, individuals.
 Hence, analytics can foster in understanding the financial health of a company to assess credit
risks and credit worthiness of the company.
 Similarly, as discussed earlier, analytics allows for screening of individual customers for a loan
by understanding the spending patterns and previous credit history.
 It can help in rapidly assessing the risks of advancing a loan.

9. Fraud management & prevention
 Analytics allows for a detailed insight into the spending patterns of customers this also helps in recognizing a
red flag (if any) in case of an outrageous happening.
 For instance, if there is a sudden increase in the expenditure of a decently cautious customer, it might indicate
an occurrence of fraudulent practices.
 It might signify that the customer’s card was stolen and used by fraudsters.
 Analyzing such risks with the help of analytic tools greatly helps in cutting down the risk of fraudulent
actions.
 The key steps used in fraud detection are as follows:
1. Obtaining data samplings for model estimation and preliminary testing
2. Model estimation
3. Testing stage and deployment.

10. Identifying channels of transactions
 Different channels of transactions and their magnitude of use speaks volumes about the
needs and spending patterns of the customer.
 Through analytics, banks are facilitated to track the history or past usage patterns/trends of
in and out payments at various branches of banks and ATMs.
 Hence, indirectly, it fosters prediction of future needs of banks’ potential customers.
 This process further leads to optimal management of liquid assets which in turn results in
increased income.
 Ultimately it helps in obtaining an overview of future changes in investment and liquidity
options.

11. Customer Lifetime Value (LTV)
 Customer’s lifetime value refers to the understanding as to how long the organizations would be able to retain
their customers.
 Analytics massively helps in identifying the trusted customers and helps with ways in which those customers
could be facilitated better.
 It further secures such customers’ loyalty which are all crucial in the banking industry.
 Predictive analytics is the most popular type of analytics that helps in achieving the above mentioned necessities.
 It provides a proper framework on what customers must constitute the focal point and also specifies ways in
which new customer engagement can be improved.
 Predictive analytics details out the factors which enhanced returns on customer engagements in the past. The
bank can benefit from this and keep up with the trends which can onset the customers to stay loyal with the bank.

12. Customer Lifetime Value (LTV) Contd..
 Through analytics, banks will be enabled to know what promotion based messages did the
customer respond to and work on them further to maximize profits while catering to
customers’ interests.
 Predictive analytics assists banks to build and maintain their relationship with the customers.
 It will ensure that banks deliver the right kind of services to the right customers. This way
individual preferences will be met in the most sorted way and thus loyalty of customers with
the bank will surface.

13. Conclusion
 Data analytics under the umbrella of data science largely allows for banks to have a
comparative advantage.
 Hence, it becomes crucial for the banking industry to understand the importance of data
science and put it in practice greatly.
 Data analytics when included in the decision making processes in the banking industry will
help in developing strategies based on actionable insights from banks’ clients data which will
ultimately enhance the performance and longevity of the bank.