Data Lakes for Insurance Industry: Exploring Challenges and
Opportunities for Customer Behaviour Analytics, Risk Assessment, and
Industry Adoption
Bálint Molnár
1 a
, Galena Pisoni
2 b
and Ádám Tarcsi
1 c
Eötvös Loránd University, ELTE,IK Pázmány Péter 1/C, 1117, Budapest, Hungary
University of Nice Sophia Antipolis, Nice, France
Data Warehouse, Data Lake, Enterprise Architecture, Data Science, Insurance.
The proliferation of the big data movement has led to volumes of data. The data explosion has surpassed
enterprises’ ability to consume the various data types that may exist. This paper discusses the opportunities
and challenges associated with implementing data lakes, a potential strategy for leveraging data as a strategic
asset for enterprise decision-making. The paper analyzes an information ecosystem of an Insurance Company
environment. There are two types of data sources, information systems based on a transactional databases
for recording claims, as the basis of financial administration and systems policies. There exists neither Data
Warehouse solutions nor any other data collection solutions dedicated to utilizing by Data Science methods and
tools. The emerging technologies provide opportunities for synergy between the traditional Data Warehouse
and the most recent Data Lake approaches. Therefore, it seems feasible and reasonable to integrate these
two architecture approaches to support data analytics on several aspects of insurance, financial activities, risk
analysis, prediction and forecasting.
The rise of Fintech, changing consumer behavior,
and advanced technologies are disrupting equally all
the financial services industry, among which also it’s
most prominent member, insurance. The insurance
industry is preparing for the process of digital trans-
formation and how it conducts business and big data
capabilities lie at the centre of it. The insurance in-
dustry has been using data to calculate risks for years,
still, with new technology now available to collect and
analyze large volumes of data for patterns and bet-
ter risk prediction and calculation, the value of under-
standing how to store and analyze it has grown expo-
nentially (Liu et al., 2018).
Insurers are at their early stage of discovering the
potential of big data, and multiple technology compa-
nies are investigate how to make value of such tech-
Equally important is to note that the majority of
insurance companies are left with systems, processes,
and practices that would be still recognizable by those
that were in the industry in the 1980es. Changes
brought by digitalization even more pressure the in-
surance industry (Traum, 2015; Pisoni, 2020).
Due to the big volume of data generated by insur-
ers, it is especially important to enable the best use of
them, and in this paper, we discuss opportunities and
challenges in implementing data lakes for insurance
companies and a potential strategy for leveraging data
as a strategic asset for enterprise decision making, es-
pecially in the domain of customer behavior analytics
and risk assessments. We present potential data lake
reference architecture, it’s respective data warehouse,
and a mapping between the different components of
the Data Lake to Zachman architecture. Also, we
identify potential future challenges companies will
face due to the use of big data, and we draw what in
our view will be the new business logic of insurance
companies in the digital era. The organization of the
paper as follows: Section 2 an overview and analysis
of architectures and applications of Data Warehouse
and Data Lake, Section 3 investigates huge data col-
lection and opportunities and limits for data analytics
in the domain of customer behavior analytics, Section
4 analyzes how Data Lake and Data Warehouse can
Molnár, B., Pisoni, G. and Tarcsi, Á.
Data Lakes for Insurance Industry: Exploring Challenges and Opportunities for Customer Behaviour Analytics, Risk Assessment, and Industry Adoption.
DOI: 10.5220/0009972301270134
In Proceedings of the 17th International Joint Conference on e-Business and Telecommunications (ICETE 2020) - Volume 3: ICE-B, pages 127-134
ISBN: 978-989-758-447-3
2020 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
Figure 1: Sources of data and usage, inspired by (LaPlante and Sharma, 2014; Boobier, 2016; Duggal et al., 2015).
help risk calculation, Section 5 examines the industry
response and potential new logic for insurers of the
future, and then the article concludes with a summary.
Potential domains for the use of the big data and data
lakes in the insurance process include:
Pricing and underwriting, estimating the price of
an insurance policy is based on complex risk as-
sessment process, big data can give the ability
to more accurately price each customer by com-
paring individual behaviour compared to a large
pool of data, a process that allows the insurance
companies to correlate behaviour to risk (Boobier,
2016). This is most visible in the car insurance,
where insurers can compare the behaviour of sin-
gle driver to the behaviours of a large sample of
other drivers, especially given the big number of
past claims they have data for customers.
Settling claims, a typical claim process starts with
an insurer asking to assess the loss or the damage
of the insured person or company, and this process
can be long and painful for both of the parties.
Different solutions have been devised already in
this respect: automated claims setting, especially
based on the domain of car insurance, where the
company has a large amount of data, and already
from past data can fully automatize claim report
and their response to them. Insurance compa-
nies can execute and automate decisions related
to big data, such as whether the insurance com-
pany should pay the claim or not risks, which can
be later only approved by staff member instead
of doing the process manually, which can also, in
turn, save personnel time and effort (Minelli et al.,
Monitoring of houses, the insurance companies
use and distribute IoT devices to monitor a range
of activities at home. By comparing the real-time
data collected from the delivers about the house
risk, the insurance companies can intervene be-
fore a claim occurs, by recommending the poli-
cyholder to adjust the high-risk behavior, such as
forgetting to lock the door or to set alarms. Hav-
ing integrated data in this scenario is a key, be-
cause it allows insurance companies to see which
policy-holders have good practices or which will
almost close to certainly submit a claim, and like
in future the company can reward good behavior
of customers (Pal and Purushothaman, 2016).
Monitoring of health, big data can be used to un-
derstand patient risks and prevent health issues
before they arise. One way may be through draw-
ing insights from big data to better make predic-
tions and recommendations to customers for dif-
ferent insurance policies and their coverage, by
connecting medical records and multiple sources
or information regarding attitudes towards health
like wearable of phone data. One example would
ICE-B 2020 - 17th International Conference on e-Business
Figure 2: Data Lake Reference Architecture.
be a diabetic patient who needs insulin and if such
intake is traced, the insurer would also be alerted
such an irregular use (Spender et al., 2019).
Improve customer experience, insurance compa-
nies have started to investigate the use of chat-
bots to allow for a more rapid response to cus-
tomer queries and automatizing the response pro-
cess. To implement the process, an AI algorithm
needs to be trained on a big data and huge amount
of data from the past on insurance policies, claims
and other areas of business of the company, and as
a result provide near to instance response to cus-
tomer questions. The chatbots can be used also
by new employees and even the staff can use and
query the chatbots, which can be also voice-based,
and like this to gain the information needed to
serve the customer in front of them(Koetter et al.,
2018; Riikkinen et al., 2018).
Other use can be for business process management
aims (Rodrıguez et al., 2012), as well as for improved
security of services (Ristov et al., 2012).
Generally, organizations are full of data that are stored
in existing databases, produced by various informa-
tion systems, data streams are coming from mobile
applications, social media, web information systems,
and other devices that are linked to the internet (Inter-
net of Things, IoT). The collected data can be consid-
ered as heterogeneous in both structure and content,
i.e. there are structured, semi-structured, and unstruc-
tured data items, some of them are accompanied by
meta-data. There is an essential difference regarding
data capture in traditional Business Information Sys-
tems and Information Systems for Financial Indus-
tries especially Insurance. In the Financial Industry,
especially the Insurance environment, the initial data
gathering is typically manual, and even the structured
data contains plenty of textual information. The in-
put data are created typically by human agents who
can make errors in data either intentionally or unin-
tentionally. Examples of error-prone data entry are
as follows: name, address, identifiers (social security
numbers). Thereby, the method of data entry leads
to imprecise data that makes difficult any data mining
exercises, e.g. association relationships that may have
been found prove to be erroneous. While inputting
data into Information System of Insurance Policies,
Customers Data, frequently use either templates or
copy-paste commands to fill in various forms as e.g.
claims in forms to comply with policies, standards,
and other regulations. The problem with that prac-
tice is that it conceals the subtle differences among the
electronic customers’ records that would be precious
for data analytics and knowledge discovery. Informa-
tion that is produced by automated systems along with
Data Lakes for Insurance Industry: Exploring Challenges and Opportunities for Customer Behaviour Analytics, Risk Assessment, and
Industry Adoption
Table 1: Comparison of Data Warehouses vs Data Lakes.
- Data Warehouse Data Lake
Data Structured, processed Raw: Structured / semi-structured / unstructured
Processing Schema-on-write Schema-on-read
Storage Requires large storage (architecturally complex) Requires larger data storages (architecturally less
complex). It can be cheaper despite the large amount of
data stored.
Agile-aware Fixed structure Tailored
Purpose of Data Fixed (BI, reporting) Not Yet Determined (Machine Learning, Data analytics)
Target audience Business Professionals Data Scientist
auto-fill and edit check options as e.g. speech to text,
OCR (optical character recognition) that computerize
customers’ data may introduce systematic and ran-
dom inaccuracies that differ from clerks to clerks, a
software tool to software tool. These errors are diffi-
cult to quantify and forestall.
To ensure that there will be a centralized loca-
tion that would serve as the single source of truth, the
data with different types and structures from various
sources should be collected and loaded into a Data
Lake. The most recent technologies can yield oppor-
tunities for the application of data analytics and mod-
els of Data Science. The results of running data ana-
lytic algorithms could be actionable knowledge in the
clinical research environment.
Generally, it is assumed that the data coming from
source systems are in good quality however, the mar-
ket and administrative forces have not enforced a sat-
isfyingly high level on standards of data quality. The
typical life history of data can be seen in Figure 1.
On the left part of the diagram, the various major
source systems of financial data can be found. The
data are represented as customers’ electronic personal
records, geo-codes for geographical information, and
other loosely coupled data related to management,
and business administration.
Our paper showcases architecture for a moderate
size insurance enterprise environment so that the Vs
(volume, velocity, variety, veracity, variability, value)
of the Big Data are as follows: A population of a
million customers may generate electronic customer
records in the order of terabytes yearly. Primarily, the
variability of structured, semi-structured, and unstruc-
tured data increases the complexity thereby the diffi-
culty of ensuring the single point of truth within the
data collection.
Data Lake as a Big Data analytics system allows
the continuous collection of structured and unstruc-
tured data of an organization in the form of data rep-
resentation without changing the original data, with-
out data cleaning and transformation of data models
- i.e. without loss of information, so the informa-
tion can be analyzed with the greatest degree of free-
dom. Data Lake solutions focus primarily on inte-
gration and efficient data storage processes, besides
providing advanced data management, data analyt-
ics, machine learning, and self-service Business In-
telligence (BI) and data visualization services as well.
A Data Lake offers services for Business Users us-
ing BI tools but the typical target audience is the
data scientists. Data Lake is effective for an organi-
zation where a significant part of the organizational
data is structured (and interpreted, stored in several,
not yet reconciled sources), complemented by a large
amount of unstructured data. The most important is
that goal of the data processing is to utilize corpo-
rate data assets, exploring further new contexts, typi-
cally non-repetitive research questions. Our proposed
Data Lake architecture consists of a Data Warehouse
as well, to support or the information requirements
of the organization (see Table 1). Within a hybrid
Data Lake that contains a robust Data Warehouse as
well, the life cycle of data commences with transfor-
mation, cleansing and integration. The objective to
build up a Data Lake is to separate the daily opera-
tion, transactional data from the non-production data
collection. Historically, the Data Warehouse technol-
ogy has been employed for that purpose. During the
data staging phase, the data are cleansed and filtered
for the target data structure within the Data Ware-
house, i.e. the fact table and dimensions. The phase
of data staging includes data migration, data integra-
tion, translation of codes used for data representation,
transformation between data base management sys-
tems. The Data Warehouse served as a basis for data
analysis traditionally. The ETL (Extract, Transform,
Load) procedure is applied for feeding data into the
Data Warehouse. During that step the general data
cleansing and transformation happens, e.g. amputa-
tion of trailing and leading white spaces, superfluous
zeros, standardization of identifiers/identifying num-
bers, inflicting constraints on data fields, converting
English units into metric units. While the before men-
tioned data-transformation is carried out relationships
among entities may be dropped or harmed. Similar
way, the data integration from multiple source sys-
tems, can lead to errors that are transmitted into the
Data Warehouse
To overcome the data quality limitations of Data
Warehouse, the idea of the Data Lake is conceptu-
alized. Touted idea of Data Lake is that it deposits
data in their original form, i.e. the Transientand/or
Raw Data (see Figure 2 and Figure 3, (LaPlante
and Sharma, 2014)) contains the data after an in-
ICE-B 2020 - 17th International Conference on e-Business
Figure 3: Fintech Data Lake Data Warehouse.
gestion phase so that the Data lake collects struc-
tured data from RDBMS (Relational Database Man-
agement Systems), semi-structured data (XML, bi-
nary XML, JSON, BSON etc.), and unstructured data
along with meta-data that are represented typically
in semi-structured format. The ingestion phase may
mean loading, batch processing, data stream process-
ing of source data while the necessary quality checks
are carried out utilizing the MapReduce capability
(Duggal et al., 2015). The essential property of the
raw data zone is that it is considered as “a single
source of truth” as it keeps the data in their original
form, however the masquerading, and tokenizing of
the data may happen in that zone. Data scientists,
business/data analysts can return to that zone when
they look for associations and relationships that may
have lost during data conversion, transformation, en-
coding, and encrypting.
The Trusted Zone executes the procedures for data
alteration as quality assurance, compliance with stan-
dards, data cleansing, and data validation. In that
zone, several data transformations happen to corre-
spond to prescribed local and global policies whereby
the data can be considered as a “single version of the
truth”. This zone may contain master data and the
fact data that are governed through the data catalog
that is filled in by meta-data automatically or semi-
automatically. The data in the Refined Zone undergo
several further changes that aim at the usability of
data in algorithms of Data Science. These transfor-
mations include formatting, potential detokenization,
data quality control to fulfill of requirements of algo-
rithms so that models of the subject area (e.g. health-
care) and data analytics can be created. Thereby,
knowledge discovery exercises can be carried out and
understanding of data collections may be achieved.
The access rights of users within each zone should
be strictly maintained in the form of Role-Based Ac-
cess Control, and for the temporary deviations from
the baseline can be solved by Attribute-Based Ac-
cess Rights. For researchers, managers, and other
subject area experts who would like to pursue ex-
ploratory data analytics (Myatt, 2007), the Sandbox
provides opportunities to set up models, discover as-
sociations and relationships among attributes without
involving members of IT department and other extra
costs. The researcher can bring data into the Sand-
box from any other zones within a controlled environ-
ment. It is even allowed to export interesting results
back to the raw data zone for re-use. The claimed
advantage of the Data Lake is that the data extracted
from the source systems are transformed before the
actual use data for analysis. This approach permits
more adaptability to requirements than the controlled,
structured environment of Data Warehouses. (see Ta-
ble 2, (Zachman, 1987)).
The Financial Sector, especially, the Fintech com-
panies are consumer-centric enterprises. The Insur-
Data Lakes for Insurance Industry: Exploring Challenges and Opportunities for Customer Behaviour Analytics, Risk Assessment, and
Industry Adoption
Table 2: A mapping schematically between Zachman architecture and component of Data Lakes.
Aspects /
what how where who when why model view
Contextual Fact, business
data / for analysis
Business Service Business
Business entity,
Chain of Business
Business goal Scope
Conceptual Underlying
Conceptual data
model / Data
Lake structured,
semi- and
unstructured data
Intelligence with
added value
Workflow Actor, Role Business Process
Enterprise Model
Logical Class hierarchy,
Logical Data
Model structured,
and unstructured
Hierarchy of Data
Analytics Service
User role, service
Business Rule System Model
Physical Object hierarchy,
Data model
(Object Data
Hierarchy of
Choreography Rule Design Technical Model.
Detail Data in
other file
Hierarchy of Data
Analytics Service
Rule specification Components
Data Function Network Organization Schedule Strategy
ance Industry that tries to follow the mainstream tech-
nology and not to lag put emphasis on data analyt-
ics and the application of Big Data related technolo-
gies. Traditionally the Financial Industry generally
and the Insurance Industry especially are interested
in the consumer groups that can be defined by age,
gender, on-line activities, social and economic status,
geographical distribution. In the Financial Industry,
the enterprises customize the services and products to
encounter the requirements of every customer frag-
ment. Naturally, consumers are not treated equally,
and those who have bigger affordances are tradition-
ally being treated better. The identification and spe-
cial treatment of these affluent consumer groups are
important and the use of data lakes and data ware-
houses can significantly improve the process of de-
tection of different target groups. Another of the ben-
efits of exploitation of the Big Data technologies in
the Financial Industry is to offer fraud detection ap-
proaches. The Insurance Industry gravitates towards
on-line services on a large scale so there is a high
chance to be the victim of a fraud. The Data Sci-
ence assists the companies in the Financial Industry
to understand their consumers behaviour, even the
patterns of on-line actions. The key to fraud detec-
tion is in making use of more contextual data. Not
just data about the immediate transaction and session,
data about user is needed from his/her patterns of ac-
tivities, biometric data about the person involved in
the transaction, and background data on the user in-
volved. Again, the advantage of use of data lakes is in
the fast response with adequate information from the
pool of data. In the Financial Industry, banking, pay-
ments, and insurance the concept of personal touch in
services acquires importance and it becomes the most
significant tool for marketing. The rivalry among the
competing companies enforces the the enterprises to
adopt such personalized type of services. In the In-
surance Industry, the personal recommendation may
include how to save money by combination of insur-
ance policies, which combined insurance policy with
investment has benefits for the consumer, etc. Once
more, such data can be easily generated from the data
lake of the company.
Risk management is an important task in all indus-
tries. In the Financial Industry, Data Science provides
the opportunity to recognize the potential risks, as bad
payers or incorrect decisions of investments. The ap-
plication of data analytics methods cannot make the
potential risk avoidable, however, it offers the identifi-
cation of potential risks in a timely fashion. The tech-
niques of Data Science can assist enterprises in the
Financial Industry to adapt their company strategies
and business processes to minimize the risks. It has
recently become commercially viable to create risk
profiles for individual customers, defined by factors
such as age, gender, health, work activity, place of res-
idence, driving behavior, etc., and the willingness to
pay and make the categories corresponding individual
offers. One of the frontiers in applying the data lakes
will be exactly in this, and how to do it fast given that
the data is transformed during analytics. Developing
new, or more sophisticated, risk models can enable in-
surers to offer more competitive rates, or to offer in-
surance for previously uninsurable risks, due to infor-
mation gaps which today are filled in by the increased
ICE-B 2020 - 17th International Conference on e-Business
Figure 4: Business logic of insurance company in the digital era.
availability of data. Data lakes in this regard make
the work of a data analyst in a company much easier
and the use of new analytical methods would be faster
with the use of data lakes.
Big data and new ways to elaborate data available
from multiple sources brought to raise the possibil-
ity for the insurance companies to offer usage-based
insurance(Thiesse and Köhler, 2008). For the mo-
ment it is only for niche customers, and as technol-
ogy advances are significant, it is expected to signif-
icantly increase market penetration. It might also in-
crease accessibility to insurance, and will allow to cat-
egories that previously did not insure to get coverage
through the monitored coverage programs. New busi-
ness models, based on a unique personal situation tied
to the consumer will rise. Potential examples of the
new scenario for which insurance companies neither
think nor have an offer yet, especially in the context
of usage-based insurance:
Frequent air travelers, and people who travel of-
ten. Should travelers insure for all the flights or
only for the flights that are likely to be canceled
or delayed? Should this decision be made on in-
formation about weather, the company profile of
canceled flights, or other events that may impact
flight cancellations?
Homeowners, and owners of personal posses-
sions. How to insure a house while on a rental
contract? What about Airbnb renting? In general
for personal possessions, should companies insure
only new possessions, or also old ones? Again,
should companies insure the rents for a longer du-
ration of time, or insure for single rents?
Car insurance and drives that drive frequently or
infrequently. Should there be insurance coverage
for part-time driving, or based only on the num-
bers of km customers drive? Does the price that
now customers currently pay truly take into ac-
count the driving history of the person or is de-
termined based on the real risk of your car be-
ing stolen? If someone borrows the car for 2
days, should he have separate insurance, espe-
cially if the current insurance policy is based on
the owner’s behavioral profile? What about rides
in Blablacar?
In general, the benefits for insurers will be also in
reduced costs of underwriting and administration of
insurance policies, and using the data lakes approach
will have better integration with data from other in-
dustries, and significant improvement in the use and
making sense of such data.
An important obstacle and limitation for the adop-
tion of such approaches by insurance companies are
to avoid risk prediction that is too thin’ and leaves
the company with big financial exposure to a potential
unlikely event. The lower the cost of the coverage for
high-risk events, the poorer the insurer will be. Fu-
ture works will need to investigate how to build new
risk models that will support usage-based insurance
Data Lakes for Insurance Industry: Exploring Challenges and Opportunities for Customer Behaviour Analytics, Risk Assessment, and
Industry Adoption
In Figure 4 we present the basic structure of the
"data-driven" insurers. The data collected will be an-
alyzed in real-time in the data lake, visualizations will
be presented to insurers, and the insurer can decide
on optimization in offering and product or pricing.
This information can be presented further in knowl-
edge graphs (Tejero et al., 2020) and can be used for
the generation of reports with new insights, the execu-
tion of advanced data analysis task between business.
In this paper we presented our first attempt to tell
the ongoing problems in the insurance industry, what
would be potential advantages and challenges for use
of data lakes in the insurance industry, reflected how
they will influence customer behavior analytics, and
risk assessment, and lastly we discuss industry adop-
tion and challenges that may arise. Besides, we
present the basic structure of the digital insurance
ecosystem, with data lakes and data warehouses be-
ing at the center of it.
Big data and new ways of integrating data in the
digital transformation, such an integrated approach
will foresee the development of new open business
intelligence models, to better detect similar cases
among data and stress similarity and explore more the
use potential re-use of the same effort for different
The future of insurance will be data-driven and
there will be the need to manage risk even if data
makes it easier to estimate it. The pace of change
however if big and modern data structures like data
lakes and data warehouses will give rise to new busi-
ness models for their functioning, that will change the
way they work as of today.
The project was supported by no. ED_18-1-2019-
0030 (Application-specific highly reliable IT solu-
tions) program of the National Research, Develop-
ment and Innovation Fund of Hungary, financed un-
der the Thematic Excellence Programme funding
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