Smart Banking in Smart Cities
MUKESH PATEL SCHOOL OF TECHNOLOGY MANAGEMENT AND
ENGINEERING
SVKM’S NMIMS
Smart Banking in Smart Cities
A Secondary Research submitted in partial fulfilment
Of the requirements for the degree of
MBA (Tech)
By
Shrey Gupta (C041)
Puleen Gupta (C042)
Harpreet Singh Khalsa (M073)
Saurabh Singh (M091)
Under Supervision
Of
Dr. Anuja Agarwal
Year of Graduation (e.g. 2017)
1.) Introduction
This research project aims at finding the possible services and attributes that may be desired by the citizens or customers which may or may not be residing in Smart Cities.
The concept of smart cities is in an evolving phase. The concept can be made a reality or close to reality is by developing solutions which have been developed by keeping customer’s opinions and views in mind.
1.1) Purpose
The purpose of this data analysis is to find out the factors of the success of the concept of smart banking. This also aims to find out the relationship between the different variables that affect this success factor as well as the extent to which the variable affect the other. This data analysis would also help us to forecast the future usage and spending trends.
1.2) Hypothesis
1. Cashless and Digital banking will enable smart banking
2. Smart Loans with the help of technology may enhance the facilities served by smarter banking.
3. Transparency in banks will also lead to evolution of methods towards smarter banking.
4. Enabling an electronic portal for document sharing to enable faster access to proper documents.
5. The methods like e-cheque will lead to make banking smarter.
6. Better banking systems will also lead to efficient and smarter cities.
1.3) Key Documents
Various other research papers, news reports, research reports were accessed to determine the variables revolving around the concept of smart banking such as cashless banking, integrated identities and payments solutions.
2.) Methodology
Data was collected from 171 observant through a set of questions. These questions were framed keeping in mind the variables and the hypotheses framed. The questionnaire comprised of questions about their payment behaviour, their likeliness of using proposed services and their current banking activities patterns.
These questionnaire was filled by a variety of respondents. It included people age groups ranging from 18-25 to 41-60 and above 60 and students to service-based employees to businessmen. This helped us to have a variety of answers to our questions to better understand the relations between the variables.
The answers to the questionnaire were then converted to the required scales to appropriately use the analysis tools such as Correlation and ANOVA and regression.
We made the following scales for the analysis of data
Gender: Male: 1 Female: 0
Age: 18, 21.5, 33, 50, 60
Occupation: Student: 0, Housewife: 5, Service: 10, Freelancer: 15, Business: 20
Annual income values :50,000 , 1,00,000 , 3,50,000 , 9,00,000 , 15,00,000
These were our values for the independent values.
3.) Results
These were the following relationships between the data we were able to find out.
- Cashless Transactions
The following graph shows the relationship between the annual income and the usage of cashless transactions.
[pic 1]
The following graph shows the relationship between the age and the usage of cashless transactions.