This document discusses the impact of big data analytics on decision making in the Lebanese banking sector. It begins with an introduction to big data and big data analytics. The research question asks what impact big data analytics has on the decision making process in banking. A literature review covers benefits of using big data in decision making and the impact of big data analytics on the financial sector. The methodology section describes qualitative interviews conducted with banking executives. Findings indicate that big data analytics promotes decision making intelligence but does not affect the final choice. At an organizational level, big data decentralizes decision making. Recommendations include applying big data analytics to improve decision making, uncover opportunities, and improve compliance.

1. 01
The Effect of Big Data Analytics on
Firm Decision Making:
The Case of the Lebanese Banking
Sector
Presented by Lina Shouman

2. Presentation
Agenda
INTRODUCTION
RESEARCH QUESTION
LITERATURE REVIEW
METHODOLOGY AND DATA ANALYSIS
FINDINGS
02
RECOMMENDATION

3. INTRODUCTION
03
The term Big Data refers to large complex data sets
that are used by organizations to enhance their business
operations, address business problems as well
as generate new opportunities
Big Data Analytics (BDA) analyze Big Data (BD)
to uncover relevant information such as customer
preferences, hidden patterns, market
trends and unknown associations

4. BDA and Banking
RETAIL BANKING
bank collections, credit cards,
private banking
COMMERCIAL BAMKING
credit isk analysis, customer
and sales management,
middle market loans
ASSET MANAGEMENT
wealth management,
management of capital
investments, analysis
of investment deposits 04

5. RESEARCH QUESTION
05
What is the impact of big data analytics on the
decision-making
process in the banking sector?

6. LITERATURE
REVIEW
Benefits of Using BD in
Decision Making
05
Big Data Analytics
BDA Impact on The
Financial Sector

7. BIG DATA ANALYTICS
07
BDA significantly affect business value and
company efficiency, resulting in savings, reduced operating costs,
communications expenses, increased returns, improved client relationships, and
new company plans
BDA consists of a systematic process of capturing
and analyzing business data, developing a statistical model either to explain
the phenomenon (Descriptive Analytics), developing a model to predict future
outcomes based on variable inputs (Predictive Analytics) or developing a model
to optimize or simulate outcomes based on variations in inputs (Prescriptive
Analytics).

8. BENEFITS OF BDA
08
When business transaction data are mined for association rules, they provide key
insights for decision makers about products bought together or for predicting future
demand for certain items.
The large datasets coming from a variety of sources in structured, semi-
structured or unstructured forms provide forms with several ways to
tap value from these datasets and make strategic, tactical and
operational decisions.
Banks are using BDA to observe customers’ “journeys” through the use of websites,
call centers, tellers, and other branch personnel to figure out the directions that
customers follow through the bank, and how those directions impact the purchase
of specific financial services.

9. BDA IMPACT ON THE FINANCIAL
SECTOR
08
Marketing: Marketing analytics can help banks obtain information that would assist them
in decision making and in turning lead into increased profitability
Fraud Detection: BDA have become an important element of any
approach that can be used for identifying and deterring financial crime
Credit Risk Management: BDA techniques help bankers gain a greater
insight into the behaviors of their customers by evaluating credit reports,
expenditure habits, and repayment rates of loan applicants. Big data tools
identify the probability of a customer to default on a loan or continuously fail
to meet payment deadlines

10. METHODOLOGY
09
A qualitative approach has been adopted by conducting in
depth interviews with executives that work in the financial
sector and whose work is related to BD.
The purpose of the interviews is to examine how BDA
directly affect decision-making in organizations and
organizational intelligence.
The interviews are divided into four categories based on Huber's theory
classifications (Huber, 1990). The following classifications are:
the impacts of using BDA at subunit level, at organizational level, on
organizational memory, and on decision-making and organizational
intelligence.

11. 10
DATA ANALYSIS
Getting familiar with the data
Coding or labeling
Searching for themes
Reviewing themes
Defining and naming themes
Writing up

12. FINDINGS
11
Data analytics promotes the decision-making process intelligence and
design stage, but does not affect the choice stage.
BDA does not affect the decision-making choice stage and the
decision-makers always have the final word when making the final
decision.
At the organizational level, the use of data analytics decentralizes the
decision-making process

13. Recommendations
12
FINANCIAL ORGANIZATIONS IN LEBANON SHOULD APPLY
BDA TO IMPROVE DECISION-MAKING, UNCOVER UNSEEN
INNOVATION OPPORTUNITIES AND IMPROVE
COMPLIANCE WITHIN A MORE STRINGENT REGULATORY
ENVIRONMENT.
FUTURE RESEARCH SHOULD INCLUDE MORE
ORGANIZATIONS IN THE STUDY SO THAT MANY ISSUES
CAN BE INVESTIGATED AND MORE
COMMONALITY IN THE FINDINGS CAN BE VERIFIED.
COMPARISONS CAN BE MADE BETWEEN ORGANIZATIONS,
AND THE RESULTS AND CONCLUSIONS CAN BE
GENERALIZED WITH MORE CONFIDENCE AND
RELIABILITY.
THE SAMPLE ORGANIZATIONS CAN ALSO BE EXTENDED
TO ORGANIZATIONS OPERATING IN VARIOUS INDUSTRIES,
SUCH AS MANUFACTURING, SERVICES,
AND HEALTH SECTORS TO SAY THE LEAST.

14. THANK YOU
EMAIL
lina.shouman@liu.edu.lb
13