CIS8004: Enterprise Planning and AI in the Motor Vehicle Industry

This report examines the transformative impact of Artificial Intelligence (AI) and machine learning on the motor vehicle industry. It explores strategic approaches for integrating AI, including the development of personal assistants for customers to manage autonomous vehicle navigation and insurance premium calculations. The report analyzes the business motivation, highlighting how AI can reshape insurance processes from reactive to proactive models, and presents a business model forecast for the next decade. It also covers value chains, organizational capabilities, and potential future states and risks, such as the threat of hacking. The study concludes that AI and machine learning offer significant opportunities for the motor vehicle insurance program, necessitating a strategic positioning to adapt to the changing business landscape. References from various academic sources are also included in the report.

Running head: ENTERPRISE PLANNING AND IMPLEMENTATION
ENTERPRISE PLANNING AND IMPLEMENTATION
Name of the Student
Name of the University
Author Note

ENTERPRISE PLANNING AND IMPLEMENTATION 1
Abstract:
The purpose of this study is to discover how the effect of AI advertisement AI innovation can
change the motor vehicle industry. The motor vehicle organizations have utilized generally
for the multivariate relapse for creating premium for the clients. The AI and Machine
learning can display some new open doors for the motor vehicle protection program.

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Table of Contents
Introduction:...............................................................................................................................3
Strategy:.....................................................................................................................................3
Motivation of business:..............................................................................................................4
Business model:.........................................................................................................................5
Value chains:..............................................................................................................................6
Capabilities of the organisation:.................................................................................................7
Future states and risks:...............................................................................................................7
Conclusion:................................................................................................................................8

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Introduction:
The motor vehicle industry is remaining on a seismic skirt that is a tech-driven move. AI and
machine learning will assume an essential job later on. In any case, for executing the
innovation there ought to be a few methodologies. There ought to be close to home right hand
for the clients that will be able to arrange them a vehicle that is self-sufficient vehicle for the
get together over the town (Amarasinghe et al., 2015). As looked by means of the eyes of a
Scott, at the season of jumping into land of vehicle and if the client needs to drive the vehicle
as his own into the dynamic mode, the individual help of the client will make the guide itself
that ought to be the course. Also, it will promptly impart the course to the back up plan that
will react quickly with one of the elective courses that will have the much lower swarm,
which will give low dangers and low mishap. In the wake of computing the month to month
premium, the right hand of the client will advise him that the protection premium will
increment by 5 to 10 percent that will be founded on the chose root and the volume of autos
that will exist around then out and about (Balasubramanian, Libarikian, and McElhaney,
2018). The help will likewise caution the client about the life coverage strategy of him that
will be evaluated dependent on pay as a client will live and will increment by 2% more.
Strategy:
The motor vehicle industry is staying on a seismic verge that is a tech-driven shift.
Machine learning and AI will play a primary role in the future. However, for implementing
the technology there should be some strategies. There should be personal assistant for the
customers that will be having the ability to order them a vehicle that is autonomous vehicle
for the meet up across the town (Amarasinghe et al., 2015). As looked via the eyes of a Scott,
at the time of hopping into arrive of car and if the customer wants to drive the car as his own
into the active mode, the personal assistance of the customer will make the map itself that

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should be the route. In addition, it will immediately share the route with the insurer that will
respond immediately with one of the alternative routes that will be having the much lower
crowd, which will provide low risks and low accident. After calculating the monthly
premium, the assistant of the customer will notify him that the insurance premium shall
increase by 5 to 10 percent that will be based on the selected root and the volume of cars that
will be existing at that time on the road (Balasubramanian, Libarikian, and McElhaney,
2018). The assistance will also alert the customer about the life insurance policy of him that
will be priced based on pay as a customer will live and will increase by 2% more.
The motivation of business:
While the situation will have appeared into the great beyond, the client stories that are
coordinated will develop every one of the lines that are identified with the protection. The
majority of the required innovations are as of now exist just as huge numbers of them are
accessible to the clients. With the new learning systems that are identified with the profound
wave like AI and convolutional neural systems are having the potential for satisfying the
guarantee of impersonating the thinking, learning, observation and the critical thinking
mentality of the brain of the human (Kašćelan, Kašćelan and Novović Burić, 2016). The
protection will be moved from the current situation with fix and recognize to forestall and
foresee, changing every one of the parts of the protection of the business in this procedure.
This change identified with the pace will quicken likewise as buyers, representatives,
guarantors and the money related middle people will turn out to be increasingly proficient
while utilizing the advances that are progressed, for improving the efficiency and basic
leadership, lower cost just as upgrade the experience of the client.
The next 10 years business module should be as follows:

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2019 2021 2022 2023 2024 2025 2026 2027
Net premium
revenue ($m) 14,122 19,544 20,646 21,265 21,322 22,644 22,898 24,773
Net incurred
claims ($m) 11,545 11,979 12,785 12,745 12,212 13,497 16,521 16,289
Underwriting
result ($m) -749 3,876 3,496 2,877 3,269 3,713 -21 1,893
Investment
income ($m) 2,487 2,979 4,794 4,499 4,134 4,796 3,792 3,179
Net profit / loss
($m) 813 3,467 4,796 5,134 5,793 5,976 2,054 3,086
Net loss ratio 78% 63% 67% 64% 54% 58% 72% 66%
Total assets ($m) 60,154 77,794 78,796 81,791 83,753 92,796 92,650 96,278
Shareholders'
equity ($m) 15,647 21,488 24,227 24,988 24,463 25,416 25,466 29,807
Return on assets 1.3% 4.7% 6.3% 6.7% 6.4% 6.3% 2.3% 3.3%
Return on
equity 5.6% 17.3% 21.2% 20.7% 21.1% 21.2% 7.7% 11.2%
Solvency
coverage 2.67 2.41 2.91 2.64 2.80 2.40 1.58 1.19

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Table: Insurance at a glance
Business model:
As the AI and machine learning are becoming more and more integrated into the in this
insurance industry must position themselves for responding to the business landscape that is
changing. The executive who will handle the insurance policies must have the understanding
about the factors, which may contribute to the changes as well as how the AI and machine
learning shall reshape the distribution, claims, pricing and the underwriting (Kitchens and
Harris, 2015). The AI and machine learning are underlying the technologies that are being
already deployed in vehicles, homes and businesses. AI and machine learning will reshape
this insurance related industry over the future decade. The fields that are related to the
robotics have seen so many achievements in recent years.
Figure: Business canvas model

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Value chains:
The data frameworks are made increasingly versatile by the AI advancements to the human
just as it can improve the association between the PCs and human (Lamberton, Brigo and
Hoy, 2017). In the wake of doing this the AI and machine learning gives the back up plans an
edge that how they will have the capacity for dealing with the guaranteeing the administration
through utilizing the innovations in some average ways.
For predicting claim of the patterns of the volume.
 For enabling the claims that are automated, to detect the frauds by using the enrich
analytics of data.
In dollars 2010 2011 2012 2013 2014 2015 2016 2018
Direct business
of which:
14,456 21,455 22,922 22,487 23,455 24,975 25,462 27,476
House
owners/householders 2,115 3,455 3,464 3,454 3,455 3,466 4,454 4,794
Commercial motor vehicle 1,445 1,456 1,475 1,466 1,345 1,164 1,794 1,646
Domestic motor vehicle 3,134 4,489 4,464 4,797 5,796 5,641 5,134 6,164
Fire and ISR 1,469 2,794 2,796 2,264 2,791 2,464 3,213 3,216
CTP motor vehicle 1,765 2,400 2,763 2,943 2,796 2,971 2,713 2,791
Public and product liability 867 2,794 2,856 1,969 2,464 1,945 1,454 2,496
Professional indemnity 533 1,796 1,794 1,134 1,466 1,134 1,796 1,146
Employers' liability 716 779 1,166 1,646 1,794 1,461 1,164 1,646

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Other direct classes 2,794 3,974 3,464 3,158 4,746 4,461 4,446 4,164
Inwards reinsurance 2,794 6,894 5,167 5,794 5,796 5,766 6,464 5,846
Table: Gross premium revenue
Capabilities of the organisation:
The fast kind of advancement of the present business will be fuelled by the
robotization reconciliation and broad appropriation, biological systems of outside information
and profound learning. While it can't be, anticipate how the protection will look like in the
following couple of years. In any case, by getting savvy on the AI related patterns and
innovations, by creating and starting the usage of a key arrangement that is lucid the
organizations can be improved (Mandic, 2013). Just as the organizations must need to make
an information technique, that is exhaustive and the correct innovation and ability foundation.
Current and Future states and risks:
The motor vehicle protection industry has experienced childhood in the previous 5 years
consistently just as it has been portrayed by means of the profits of the speculations that are
falling and loaded with the rising harms which are ascending through the cataclysmic events.
There ought to be close to home aide for the clients that will be able to arrange them a vehicle
that is a self-governing vehicle for the get together over the town. As looked by means of the
eyes of a Scott, at the season of jumping into land of vehicle and if the client needs to drive
the vehicle as his own into the dynamic mode, the individual help of the client will make the
guide itself that ought to be the course. Moreover, it will promptly impart the course to the
back up plan that will react quickly with one of the elective courses that will have the much
lower swarm, which will give low dangers and low mishap (Meirambayeva et al., 2014). In

9ENTERPRISE PLANNING AND IMPLEMENTATION
any case, there are a few dangers also as hacking will be a major issue in AI-based collision
protection, as the programmer may probably follow the client information subsequent to
getting to the whole database.
The critical hazard that is associated with this procedure is hacking.
Conclusion:
Thus, it can be concluded that it is changing the whole amusement in this procedure.
The motor vehicle organizations have utilized customarily for the multivariate relapse for
creating premium for the clients. AI and machine learning can introduce some new open
doors for the motor vehicle protection program (Rejikumar, 2013). As the AI and machine
learning are winding up increasingly more incorporated into the in this protection industry
must position themselves for reacting to the business scene that is evolving. While it can't be,
foresee how the protection will look like in the following couple of years. In any case, by
getting brilliant on the AI related patterns and innovations, by creating and starting the usage
of a vital arrangement that is rational the organizations can be improved.

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References:
Agarwal, P.K., 2018. Public Administration Challenges in the World of AI and Bots. Public
Administration Review, 78(6), pp.917-921.
Amarasinghe, M., Kottegoda, S., Arachchi, A.L., Muramudalige, S., Bandara, H.D. and
Azeez, A., 2015, August. Cloud-based driver monitoring and vehicle diagnostic with OBD2
telematics. In 2015 Fifteenth International Conference on Advances in ICT for Emerging
Regions (ICTer)(pp. 243-249). IEEE.
Balasubramanian, R., Libarikian, A. and McElhaney, D., 2018. Insurance 2030—The impact
of AI on the future of insurance. McKinsey & Company, New York, NY, USA, Apr.
Golden, L.L., Brockett, P.L., Ai, J. and Kellison, B., 2016. Empirical Evidence on the Use of
Credit Scoring for Predicting Insurance Losses with Psycho-social and Biochemical
Explanations. North American Actuarial Journal, 20(3), pp.233-251.
Kašćelan, V., Kašćelan, L. and Novović Burić, M., 2016. A nonparametric data mining
approach for risk prediction in car insurance: a case study from the Montenegrin
market. Economic research-Ekonomska istraživanja, 29(1), pp.545-558.
Kitchens, F. and Harris, T., 2015. Genetic adaptive neural networks for prediction of
insurance claims. Int. J. Eng. Adv. Res. Technol.(IJEART), 1(6), pp.2454-9290.
Lamberton, C., Brigo, D. and Hoy, D., 2017. Impact of Robotics, RPA and AI on the
insurance industry: challenges and opportunities. Journal of Financial Perspectives, 4(1).
Mandic, D., 2013. Multi-criteria AHP analysis in risk assessment for motor vehicle
insurance. Metalurgia International, 18(1), p.128.

11ENTERPRISE PLANNING AND IMPLEMENTATION
Meirambayeva, A., Vingilis, E., Zou, G., Elzohairy, Y., McLeod, A.I. and Xiao, J., 2014.
Evaluation of deterrent impact of Ontario's street racing and stunt driving law on extreme
speeding convictions. Traffic injury prevention, 15(8), pp.786-793.
Rejikumar, G., 2013. A pre-launch exploration of customer acceptance of usage based
vehicle insurance policy. IIMB Management Review, 25(1), pp.19-27.