AI and Machine Learning: Bingle Insurance Business Strategy Analysis

This report provides a comprehensive analysis of Bingle Insurance's strategy for integrating Artificial Intelligence (AI) and Machine Learning (ML) into its business operations. It begins with an overview of the insurance industry and Bingle's position within it, highlighting the competitive landscape a...Read More

ENTERPRISE PLANNING AND IMPLEMENTATION
Name of the Student
Name of the University
Author Note

Table of Contents
Introduction................................................................................................................................2
Discussion..................................................................................................................................2
Overview of the company and industry:................................................................................2
Scenario-................................................................................................................................3
Strategy for incorporating Machine Learning and Artificial Intelligence:............................3
Motivation for applying Machine Learning and AI in the business process:........................4
Business Model Canvas:........................................................................................................5
Support of Machine Learning and AI for Value and value chain network:...........................6
Capabilities of organization and the necessary changes:.......................................................7
Project Milestone:..................................................................................................................8
Conclusion:................................................................................................................................9

Introduction
The methodology or procedure, which is employed for planning in regards with the
subsidiaries, branches or departments in an organization, is known as enterprise planning.
Enterprise planning aims at constructing plans on the basis of operational and financial reality
in every division of the organization (Costa et al. 2016). It includes the procedure of planning
in regards with external and internal factors, which affects an organization. The purpose of
the paper is to assess the application of technological functions, Artificial Intelligence and
Machine Learning in the business operations of an insurance provider, which offers car or
motor vehicle insurance. The paper will highlight the importance of application of the
project. The company selected for this paper is Bingle. The paper will stress on the
applicability and suitability of the technological implementation in the insurance company.
The paper will be concluded by presenting a summary about the benefits of incorporating
Artificial Intelligence and Machine Learning.
Discussion
Overview of the company and industry:
The industry/sector, which specializes in the protection of financial loss, is referred to
as the Insurance sector. It can be considered as the form of risk management, which is
facilitated at the time of uncertain loss. The insurance market of Australia is categorized in
various constituents, health insurance, life insurance and general insurance. The sector is
facing competitions from the existing banks of the international financial conglomerates.
Bingle Insurance, the trading name of AAI ltd. in Australia. It was founded and launched in
the year 2007. It offers care insurance and concentrates on the low-cost car insurance to the
low-risk driving skills. The policies of the company are sold online, which includes the
coverage of damage, theft and accidents of customer’s property (Bingle.com.au 2019).

Scenario:
Insurance firm has considerably lower investments in technological advancements
and is experiencing competition from the competitive firms and is facing competition from
the other insurance firms operating in the similar segments. The incorporation of the
technology is beloved to address the challenges from competition and changing customer
needs by the feature of claim assessment as well as by facilitating risk and fraud
management.
Strategy for incorporating Machine Learning and Artificial Intelligence:
Making reasonable assumptions, outline the high-level strategy of the organisation to
incorporate AI & Machine Learning
Predictive analysis- The predictive analysis helps with the facilitation and
establishment of advantages in regards with business intelligence. The transformation
of reactive reporting stance to a proactive stance for incorporating machine learning
and Artificial Learning (Saade and Nijher 2016).
The strategic planning includes the primary step of identification of goals and
objectives for the organization. In this case the goal is to respond to the increasing
competition and maintaining a strong customer base. The incorporation of machine
learning and Artificial intelligence will act as a response for improving the business
activities of insurance firm.
The implementation of the Artificial Intelligence and Machine Learning technological
advancement marks the need for a time frame, which will be constructed by
identifying the factors of complexity in regards with the new technology. The
identification of the reasons and factors which marks the requirement for replacing he

old technology as well as the understanding of the implication of the new technology
in the business process (Eling and Lehmann 2018). The phased in implementation
process allows the testing of the capabilities of new technology. The time frame
ensures the integration of new technology and business activities on schedule.
The strategies for incorporation includes the appointment of an outsourced support
system for new technological implementation for saving the cost involved with in-
house maintenance team.
The final strategies for incorporation of AI and Machine Learning can be the
organizational change strategies. The employees of the firm needs to be informed for
the changes and benefits by the implementation of technological infrastructure.
Motivation for applying Machine Learning and AI in the business process:
It helps in simplifying the product marketing and in assisting the accurate forecast
regarding sales as Machine learning and AI allows the consumption of data from
various unlimited sources.
It facilitates quicker analysis of processing and prediction of data and figures.
It helps in interpreting the past buyer behaviour. For instance wearable sensors can
help in collecting information about the consumers if they can be installed to their car,
it will collect data in regards with the driving skills and behaviour of the customer.
The data will identify the risks involved in the process (Lee, Yen and Hurlburt 2018).
It helps in implying the time-intensive demonstration of data entry.
The use of the chatbots, which helps in constructing the primary communication with
consumers without the involvement of ‘human elements’ as human resource abilities
can be, channelled elsewhere. It will help in decreasing the operational cost (Palmatier
and Martin 2019). Although, Bingle operates in online platform but it does involves
traditional communication medium for attracting and retaining clients.

AI and Machine Learning can also contribute towards customization of insurance
policies for the changing demographics of the consumers (Zhang and Kedmey 2018).
It ensures faster quicker claim settlement and ensures the elimination of the
occurrence of fraud.
Business Model Canvas:
Key Partners:
Outsourcing data
centres,
Technology
maintenance
centres, Internet
service provider
and outsourced
agencies for
implementing AI
and Machine
Learning
Key
Activities:
Online
services for
selling motor
vehicle
insurance
Value
Propositions:
Customization
of insurance
policies and
assessment of
customer
behaviour
data
Customer
Relationships:
Improved
customer
service 24/7,
strong and
loyal customer
base, better
customer
relationship
aspects by
efficient
communication
medium
Customer
Segments:
Age between 25-45
(demographic
segment), Modern
lifestyle
(Psychographic
segment) and high
sense of security
(behavioural
segment).
Key
Resources:
Human
Resources,
Channels:
Online
platform

Online
platform,
Machine
learning and
Artificial
Learning
infrastructure
Cost Structure:
Online platform maintenance cost,
Machine learning and AI
infrastructural cost
Revenue Streams:
Selling insurance, selling customized policies
Support of Machine Learning and AI for Value and value chain network:
AI and Machine Learning can help in improving the policy administration and
customer service by understanding the external requests and emails by potential
customers.
By constructing the infrastructure of automated web chat services for solving
customer concerns.
By enabling the self-service queries on the policies of insurance, cancellations,
renewals and endorsements with the help of virtual assistance (Brynjolfsson and
Mitchell 2017).
It also helps in structuring data for improved customer service.
Using Machine Learning vision, the customer interaction can be enhanced.

AI and Machine Learning can act as a virtual advisor for the process of
transformation of sales force experience.
It can also act as an everyday coach, which will reduce and manage risks by offering
customised solutions (Riikkinen et al. 2018).
Technological advancement like MALTA (Machine Learning Text Analyser) for
mitigating the challenges of unstructured data, too many channel of communication
and disconnect of business process with the customer or market information (Peters
2017).
The project will implement new technological infrastructure for supporting the goal of
low-cost car insurance, as the automation and outsourcing technological maintenance facility
will allow the company to decrease the operation cost. It will improve the logistics and
channel functioning. It will contribute towards the marketing and sales department by
attracting customer and retaining them by improved customer services (Shrivastava 2018).
The human resource management will be able to act efficiently by utilizing the human
element in crucial strategic planning areas of company.
Capabilities of organization and the necessary changes:
The capabilities of the organization involves offering of car insurance policies
through online platform. The organization reflects the capability of managing the customer
relationships but they lack management of widely available data regarding customer
behaviour. The company sells policies online and functions in non-traditional marketing
infrastructure. The sales department interacts through online site for assessing the concerns
and policies claims of customers.
During the implementation of the project, the capabilities, which need to be changed,
are the way the organization offers the risk attainment facility, as it does not lead to an

efficient customer risk response. The marketing and sales offerings needs to be changed as
the customer preference and knowledge awareness is increasing which leads to a requirement
of customization of policies. The data handling capabilities also needs to be transformed for a
better acceptance of new technology (Powell et al. 2019).
Project Milestone:
Current state: The
current state indicates
the general offering of
insurance offering
through online
platform. The system
and organizational
infrastructure lacks
Future state
Current state

efficiency in customer and market data handling. The current system lacks the understanding
of customer buying behaviour. It also lacks the effective pricing structure and customer risk
mitigation efficiency, which leads to the concerns of responding to increasing competition.
Future state: The future system will have a strong technological backing by the
implementation of AI and Machine Learning. It will be more efficient in data handling and
sourcing as well as it will attract and retain more customers.
Outcome:
 Policy customization
 Understanding of customer behaviour
 Customization of policies for risk mitigation
 Better policy returns claims facility
 Customer retention and competitive pricing structure
 Low operational cost
Conclusion:
Therefore, the paper can be concluded by stating that AI and Machine Learning can
be the most effective change in the insurance sector as it can help with better customer
service and contribute towards minimizing the operational cost. It also helps in improving the
customer interaction with the facility of easy risk mitigation measures.

References:
Bingle.com.au 2019. Cheap Car Insurance Winner 2016, 2017, 2018 - Car Insurance.
[online] Available at: https://www.bingle.com.au [Accessed 18 Apr. 2019].
Brynjolfsson, E. and Mitchell, T., 2017. What can machine learning do? Workforce
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Eling, M. and Lehmann, M., 2018. The impact of digitalization on the insurance value chain
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Lee, M.R., Yen, D.C. and Hurlburt, G.F., 2018. Financial Technologies and Applications. IT
Professional, 20(2), pp.27-33.
Palmatier, R.W. and Martin, K.D., 2019. Big Data’s Marketing Applications and Customer
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Peters, G., 2017. Statistical machine learning and data analytic methods for risk and
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Powell, A., Joshi, A., Carfantan, P.M., Bourke, G., Hutchinson, I. and Eichholzer, A., 2019.
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Riikkinen, M., Saarijärvi, H., Sarlin, P. and Lähteenmäki, I., 2018. Using artificial
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