Fintech Business Plan: Analysis of Payment with Eye Technology

This report presents a comprehensive business plan for 'Payment with Eye,' an innovative biometric payment method utilizing iris recognition technology. The plan encompasses a detailed market analysis, including market size, customer segmentation, and competitive landscape. It outlines product strategies for I-R PAY and I-R scan, along with pricing and promotional strategies. The report also includes a SWOT analysis, financial projections, and a discussion of the company's management team. The analysis covers various aspects of the fintech business, including market trends, customer behavior, and the competitive environment. Furthermore, the report explores the potential of iris recognition technology in secure financial transactions, addressing market needs and opportunities within the global biometric system market.

PAYMENT WITH
EYE
Name of the Student
Name of the University
Author’s Note

2
INTRODUCTION
- I – R is a biometric
payment method, which
uses the identification of
iris
- This application is used
for secured financial
transactions
- I – R falls under the
product development
strategy sector as it is a
new product in an existing
market

3
PRODUCT
I-R SCAN
I-R PAY
 I- R PAY is used mainly for e payments
and credit card payments
 I- R scan is used in standalone
electronics, businesses and military
for security purposes
 The product can record the time and
attendance of an individual and has
an auto zoom and auto scan feature
that is used for easy and hygienic
scanning.

4
MARKET SUMMARY
- The market size of the
global biometric system is
expected to grow from USD 33.0
billion in the year 2019 to USD
65.3 billion by the year 2024.
- Compounded annual growth rate
is projected 14.6 per cent.
- Factors driving growth rate are
Standalone devices, Government
initiatives.

5
CUSTOMER
SEGMENTATION
According to the market researchers global iris recognition customer
market will be segmented into the following major types of end users
Government & BANKS
 Banks are focusing on making these
payment more secure by the instalment of
iris recognition technology in their system.
 The growing demand of such technology in
this department was due to incidents of
violent activities and frauds
STANDALONE ELECTRONICS
 Standalone devices are looking forward for
installation of these iris recognition
technology in order to have advanced
security software in their systems and
maintain privacy.

6
COMPETITORS
- Cross Match Technologies
Inc.(HID Global),
- SRI International Inc.,
- Thales Group (Gemalto
NV),
- Honeywell International
Inc.,
- Iritech Inc.,
- Iris ID Inc.
- Safran SA among others. 20XX 20XX 20XX 20XX 20XX
$0
$10,000
$20,000
$30,000
$40,000
$50,000

7
SWOT
ANALYSIS
S- Strength
The iris recognition
biometrics do not
require any physical
contact while
scanning
W- Weakness
Complicated model
which requires highly
advanced technologies
and skilled employees
O- Opportunity
High demand in almost
all the sectors in the
market from military to
banking and finance to
smartphones and
electronics
T- Threat
The threat in the iris
recognition
authentication
technology, which
makes the competition
in the market intense
and fierce

8
PRICING STRATEGY
Iris recognition technology, for a
financial transactions advancement
plan can take up three following price
approaches which are as follows
- Competitive pricing
- Value based pricing
- Penetration pricing

9
PROMOTIONAL STRATEGIES
- ON POINT MARKETING
- PUBLIC RELATIONS
- WEBSITE ADVERTISING

10
REFERENCES
Determan, G.E., Jacobson, V.C., Jelinek, J., Phinney, T., Hamza, R.M., Ahrens, T., Kilgore, G.A., Whillock, R.P. and Bedros, S., Honeywell International
Inc, 2014. Combined face and iris recognition system. U.S. Patent 8,705,808.
Al-Waisy, A.S., Qahwaji, R., Ipson, S., Al-Fahdawi, S. and Nagem, T.A., 2018. A multi-biometric iris recognition system based on a deep learning
approach. Pattern Analysis and Applications, 21(3), pp.783-802.
Bansal, A., Agarwal, R. and Sharma, R.K., 2015. Determining diabetes using iris recognition system. International journal of diabetes in developing
countries, 35(4), pp.432-438.
Umer, S., Dhara, B.C. and Chanda, B., 2017. A novel cancelable iris recognition system based on feature learning techniques. Information Sciences, 406,
pp.102-118.
Bennet, M.A., Sankaranarayanan, S. and Babu, G.S., 2015. Performance and analysis of effective iris recognition system using independent component
analysis. Journal of Chemical and Pharmaceutical Sciences, 8(3), pp.571-576.
Nalla, P.R. and Kumar, A., 2016. Toward more accurate iris recognition using cross-spectral matching. IEEE transactions on Image processing, 26(1),
pp.208-221.

THANK
YOU!