Impact of Artificial Intelligence and Machine Learning on Automobile Insurance

This paper explores the impact of artificial intelligence and machine learning on the automobile insurance industry, specifically focusing on Allianz Car Insurance. It discusses the opportunities and challenges in implementing these technologies and their potential to reshape the insurance program.

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 paper is finding out how the impact of artificial intelligence
and machine learning can change the insurance of the automobile industry. The automobile
companies have used in the traditional way for the regression that is multivariate, to generate
insurance premium for the consumers. Artificial intelligence and machine learning are having
the ability to presenting some opportunities that are new for the automobile insurance
program.

2ENTERPRISE PLANNING AND IMPLEMENTATION
Table of Contents
Introduction:...............................................................................................................................3
Strategy:.....................................................................................................................................3
The motivation of business:.......................................................................................................4
Business model:.........................................................................................................................6
Value chains:..............................................................................................................................6
Capabilities of the organization:................................................................................................7
Future states and risks:...............................................................................................................8
Conclusion:................................................................................................................................8

3ENTERPRISE PLANNING AND IMPLEMENTATION
Introduction:
Already the artificial intelligence and machine learning is being used for the
automobile insurance industry as well as these two things are changing the entire scenario of
the game. The automobile insurance companies have been traditionally used for the
regression that is multivariate to generate insurance premium for the customer (Baecke and
Bocca, 2017). The artificial intelligence and machine learning both are having the ability to
presenting new opportunities and ideas for the automobile insurance program. The primary
purpose of this paper finds out how the impact of artificial intelligence and machine learning
can change the insurance of the automobile industry for Allianz Car Insurance. Presently the
automobile companies are offering auto insurance program that is too in the form of machine
learning and artificial intelligence (Blumer et al., 2015). Though machine learning and
artificial intelligence are having their infancy if their own but most of the experts are
assuming that machine learning and artificial intelligence will play an essential role in the
automobile insurance industry.
Strategy:
The automobile insurance industry is staying onto the seismic verge, which is the shift
of tech-driven. Machine learning and artificial intelligence will play an important role in the
future but for the implementation of these technologies. There has to be the existence of some
strategies, as there would be one personal assistance that is for the consumers, which shall
have, be an ability for ordering the consumer, an autonomous car for meeting up across entire
the town (Bourne et al., 2014). As looking through Scott's eyes, at the time of arriving of a
car as well as whether the customer shall make the route map himself, which would be the
route. The assistance will share the map of the route immediately with the insurance company
who must respond immediately as well as will suggest one more alternative route. It shall not

4ENTERPRISE PLANNING AND IMPLEMENTATION
be having more crowd that will provide low accident as well as low risks assumptions
(Kašćelan, Kašćelan and Novović Burić, 2016). The assistance will then calculate the
monthly insurance premium as well as will notify the customer about his update of the
premium that the premium of the insurance may be increased by 6 to 9 percent (Júnior et al.,
2017). The premium shall be based on the root that is selected as well as the volume of the
motor vehicles, which will be existing on to the road at that time. That personal assistance
may also alert the consumers about the life insurance policy of them, which must be priced
based on the pay they live as well as it, will also increase by 3 percent more (Koopman and
Wagner, 2017).
The motivation of business:
The scenario may be seemed at the beyond of the horizon, the stories of the user,
which are integrated, shall emerge every line, which is related to the insurance. All the
technologies that are required have existed already as well as most of them are having the
ability to the consumers (Martinelli et al., 2018). With the techniques of the learning that are
new, are having the relation with the deep wave such as artificial intelligence, convolutional
neural networks and the machine learning have the potentiality for the living that is up to the
promise that is for learning, reasoning, attitude of problem solving and perception of the mind
of human being. The premium of the insurance shall be shifted from the repairing of the
current state as well as detecting for preventing or predict as well as transforming every
aspect of that plan of the insurance that is of the industry in the entire process (Pappalardo et
al., 2013). The change is related to the pace that will accelerate as the consumers as well as
insurers, brokers and the intermediaries of the financial. Those shall become more and more
adept at the time of using some of the much advanced technologies, for the enhancement of
the productivity and the decision making, cost that should be lower and optimising the
experience of the consumers.

5ENTERPRISE PLANNING AND IMPLEMENTATION
The module for the business of the next ten years should be as follows:
2020 2021 2022 2023 2024 2025 2026 2027
Net premium
revenue ($m)
14,155 19,989 20,354 21,311 21,310 22,433 22,898 24,773
Net incurred
claims ($m)
11,787 11,577 12,234 12,91 12,181 13,203 16,521 16,289
Underwriting
result ($m)
-885 3,069 3,141 2,894 3,469 3,250 -21 1,893
Investment
income ($m)
2,433 2,248 4,028 4,431 4,272 4,660 3,792 3,179
Net profit /
loss ($m)
856 3,397 4,774 5,093 5,364 5,414 2,054 3,086
Net loss ratio 79% 60% 61% 61% 57% 59% 72% 66%
Total assets
($m)
60,013 77,091 78,736 81,536 83,605 92,017 92,650 96,278
Shareholders'
equity ($m)
15,066 21,291 24,007 24,938 24,786 25,984 25,821 29,807
Return on
assets
1.5% 4.6% 6.1% 6.4% 6.5% 6.2% 2.2% 3.3%
Return on
equity
5.5% 17.1% 21.1% 20.8% 21.6% 21.3% 7.9% 11.1%
Solvency 2.75 2.14 2.19 2.44 2.08 2.04 1.85 1.91

6ENTERPRISE PLANNING AND IMPLEMENTATION
coverage
Table: Insurance at a glance
Business model:
As the machine, learning and artificial intelligence are becoming more and more type
of the integrated into the industry of the insurance, have to position themselves to respond for
the landscape of the business that is changing day after day. The executive who has the
ability for handling the policies of the insurance must also be having the understanding about
those factors that may contribute for the changes and how the machine learning and artificial
intelligence will reshape the claims, pricing, underwriting and the distribution (Patil et al.,
2017). The machine learning and artificial intelligence are underlying all the technologies,
which have already been deployed in the homes, businesses as well as in the vehicles. The
machine learning and artificial intelligence shall also reshape the industry that is based on
insurance over the coming decade. These kinds of fields are related to the robotics where so
many achievements have seen in recent years.

7ENTERPRISE PLANNING AND IMPLEMENTATION
Figure: Business canvas model
Value chains:
The information systems have been made more and more adaptive by the technologies that
are related to the machine learning and artificial intelligence to the human being as well as it
is having the ability for improving the interaction that is between the human being and the
computers (Sanchez et al., 2015). After doing these, the machine was leaning, and artificial
intelligence gives the insurance companies one, edge that how the companies can have the
ability to manage the claiming of the management via using the technologies in some decent
ways.
 For the prediction of claiming of the patterns of volume.
 For enabling the automated claims for detecting the frauds through using the data,
analytics that is enriched.
In dollars 2009 2010 2011 2012 2013 2014 2015 2016 2018

8ENTERPRISE PLANNING AND IMPLEMENTATION
Direct business
of which:
14,377 21,985 22,229 22,978 23,745 24,501 25,445 27,436
House
owners/householders 2,240 3,214 3,165 3,326 3,620 3,933 4,220 4,624
Commercial motor vehicle 1,035 1,305 1,326 1,356 1,351 1,472 1,583 1,683
Domestic motor vehicle 3,436 4,672 4,705 4,985 5,150 5,402 5,666 6,209
Fire and ISR 1,499 2,734 2,738 2,812 2,738 2,752 3,228 3,238
CTP motor vehicle 1,943 2,400 2,478 2,427 2,398 2,188 2,133 2,423
Public and product liability 894 2,067 2,057 1,969 2,009 1,916 1,883 2,239
Professional indemnity 521 1,294 1,308 1,281 1,264 1,282 1,496 1,335
Employers' liability 726 977 1,001 1,177 1,189 1,182 1,200 1,202
Other direct classes 2,083 3,322 3,451 3,645 4,026 4,374 4,036 4,483
Inwards reinsurance 2,470 6,232 5,726 5,416 5,117 5,701 6,151 5,064
Table: Gross premium revenue
Capabilities of the organization:
The current evolution that is rapid in type in the present form of the industries will be
fuelled by the ecosystems, broad adoption, automation of integration and the deep learning.
However, it is not possible to predict how the type of insurance may look in the next ten
years. While by getting more and smarter on the trends and technologies that are Artificial
intelligence related, through developing and starting the strategic plan implementation, which

9ENTERPRISE PLANNING AND IMPLEMENTATION
is coherent, the insurance companies can be improved (Rubinstein and Kroese, 2013). The
insurance companies will have to create a strategy of the data, which is comprehensive as
well as the technology that is right, as well as talent infrastructure.
Current and Future states and risks:
The automobile insurance industry has steadily grown over the past 5 years, and it has
been characterised through the returns of falling investments as well as burdened with the
damages that are rising from the disasters that are natural. There would be one personal
assistance that is for the consumers, which shall have, be the ability for ordering the
consumer, an autonomous car for meeting up across entire the town. As looking through the
Scott's eyes, at the time of arriving of a car as well as whether the customer shall make the
route map himself, which would be the route (Ritov, Sun and Zhao, 2017). The assistance
will share the map of the route immediately with the insurance company who must respond
immediately as well as will suggest one more alternative route which shall not be having
more crowd that will provide low accident as well as low risks assumptions. However, there
are some risks too as the hacking can be one of the biggest issues in the machine learning
based motor vehicle insurance, as the hackers may be able to track the data of the user after
accessing the entire information database.
Conclusion:
The artificial intelligence and machine learning both are having the ability to
presenting new opportunities and ideas for the automobile insurance program. Already the
artificial intelligence and machine learning is being used for the automobile insurance
industry as well as these two things are changing the entire scenario of the game. The
automobile insurance companies have been traditionally used for the regression that is
multivariate to generate insurance premium for the customer. Artificial intelligence and

10ENTERPRISE PLANNING AND IMPLEMENTATION
machine learning are having the ability to presenting some opportunities that are new for the
automobile insurance program.

11ENTERPRISE PLANNING AND IMPLEMENTATION
References:
Baecke, P. and Bocca, L., 2017. The value of vehicle telematics data in insurance risk
selection processes. Decision Support Systems, 98, pp.69-79.
Blumer, F.T. and Fuller, J.R., VEHCON Inc, 2015. Vehicle data collection and verification.
U.S. Patent 9,183,441.
Bourne, J.J., Annibale, C., Behara, R., Copland, C.G.K., Ferrick, D.P., Farrell, T. and Nelson,
S., Agero Inc, 2014. Method and System to Determine Auto Insurance Risk. U.S. Patent
Application 14/051,210.
Júnior, J.F., Carvalho, E., Ferreira, B.V., de Souza, C., Suhara, Y., Pentland, A. and Pessin,
G., 2017. Driver behavior profiling: An investigation with different smartphone sensors and
machine learning. PLoS one, 12(4), p.e0174959.
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.
Koopman, P. and Wagner, M., 2017. Autonomous vehicle safety: An interdisciplinary
challenge. IEEE Intelligent Transportation Systems Magazine, 9(1), pp.90-96.
Martinelli, F., Mercaldo, F., Orlando, A., Nardone, V., Santone, A. and Sangaiah, A.K., 2018.
Human behavior characterization for driving style recognition in vehicle system. Computers
& Electrical Engineering.
Pappalardo, L., Rinzivillo, S., Qu, Z., Pedreschi, D. and Giannotti, F., 2013. Understanding
the patterns of car travel. The European Physical Journal Special Topics, 215(1), pp.61-73.

12ENTERPRISE PLANNING AND IMPLEMENTATION
Patil, K., Kulkarni, M., Sriraman, A. and Karande, S., 2017, December. Deep learning based
car damage classification. In 2017 16th IEEE International Conference on Machine Learning
and Applications (ICMLA) (pp. 50-54). IEEE.
Ritov, Y.A., Sun, Y. and Zhao, R., 2017. On conditional parity as a notion of non-
discrimination in machine learning. arXiv preprint arXiv:1706.08519..
Rubinstein, R.Y. and Kroese, D.P., 2013. The cross-entropy method: a unified approach to
combinatorial optimization, Monte-Carlo simulation and machine learning. Springer Science
& Business Media.
Sanchez, K.J., Chan, A.S., Baker, M.R., Zettinger, M., Nepomuceno, J.A. and Fields, B.,
State Farm Mutual Automobile Insurance Co, 2015. Risk evaluation based on vehicle
operator behavior. U.S. Patent 8,954,340.