Abstract: The aim of the article is to identify the opportunities and challenges in the field of data processing faced by companies providing financial services in Poland. Half of the world’s consumers are surrounded by data collection devices. Financial services are becoming increasingly aware of the need to change to enter this new data-driven economy. This transformation is a complex process that will require the best management skills to be successful. Obviously, companies should remain as innovative as possible, but at the same time they cannot devote all their resources to investments in technology. This article reviews the literature on the current developments in data-driven technologies to provide an outline of the environment to which financial firms need to adapt. The author describes the sources from which data can be obtained, and gives examples of opportunities they create, namely autonomous cars, geolocation and targeted advertising, as well as offers insights into the challenges of data processing and the transition to a data economy
<a href="https://dx.doi.org/10.15611/fins.2020.4.03">DOI: 10.15611/fins.2020.4.03</a>
<p>JEL Classification: O300</p>
<p>Keywords: Big Data, Internet of Things, datafication, technological change. </p>
<h2>1. Introduction</h2>
<p>Data collecting devices surround nearly everybody: smartphones, smartwatches, autonomous cars, smart locks, light
bulbs, sensors and much, much more. It is estimated that there are approximately 50 billion devices connected to the
Internet of Things in the world (Hassan, Qamar, Hasan, Aman, & Ahmed, 2020). Most of these devices collect or
process data about the physical world and about consumers. These are huge amounts of data, offering an economic
potential that is only just beginning to be explored by financial companies. According to information from the
European Commission (2020), one in five enterprises uses advanced data processing technologies. The real economy is
changing, so how can financial services respond to such changes?</p>
<p>The aim of the study is to determine what opportunities and challenges lie before financial services companies. The
article complements the literature on strategic changes that companies increasingly face when making decisions about
switching to data-driven business. Being up-to-date requires serious investment from companies in new technologies and
data processing capabilities. Naturally, not all investments turn out to be profitable (Dobija, Klimczak, Roztocki,
& Weistroffer, 2012). Mikalef (Mikalef, Pappas, Krogstie, & Giannakos, 2017) outlines some guidelines for the
successful datafication of companies that motivates this article. They argue that companies need to learn how to
manage data resources from acquisition, through their use, to liquidation. They must be selective, so as to use data
in the areas that promise greatest returns, rather than areas where data are easily available. This approach should
help in the transition to a data economy.</p>
<p>What is a data economy anyway? Within the meaning of this article, this refers to the concept of using BigData to
accelerate the work of the enterprise and the efficiency of the product for its users. Taking into account the
technologies currently available, data processing itself is not a major problem such as the cost of investing in
BigData and the difficulties in transforming an existing company so that there is no catastrophe during the
transformation process. In the following sections of the article, the author looks at the possibility of collecting
data, their exemplary use and the problems caused by data processing itself.</p>
<p>The structure of the article is as follows. The next section describes the sources of data available to companies.
The third section explains how these data can be used. The fourth section examines the use of data-based technologies
in the financial sector. The fifth section outlines scenarios for the data-driven economy of the future, whilst the
final section offers some conclusions.</p>
<h2>2. Sources of data</h2>
<p>Data processed by companies do not come out of thin air. Europe has created a world standard for the care of personal
data and its security, the General Data Protection Regulation, or GDPR (UE 2016/679), while California and Japan
(Żyrek, 2020) are introducing similar legal solutions. Personal data are not the only type used en masse, data about
companies and their production activities are non-personal, although certainly the most valuable one. Data used to
build big datasets are anonymized, and then processed and used to predict trends and find relationships in society.
Importantly, data collecting companies are not concerned with collecting high-quality data about specific individuals
(Ghasemaghaei & Calic, 2019), but what they use are massive customer data obtained from millions of users and then
subjected to statistical processing.</p>
<p>Collecting large amounts of data has become possible in recent years thanks to the development and popularization of
smartphones and related personal devices. These devices are equipped with a set of sensors that collect data about the
users and their environment. Every smartphone is fitted with a microphone, and for example, a GPS and a gyroscope.
Moreover, app developers can obtain permission from users to access data from other apps on the same device.
Currently, data can be gathered from 5.2 billion mobile phone users (DataReportal, 2020). According to GSM Association
real-time intelligence data, today there are 5.23 billion people that have a mobile device. This means that 66.79% of
the world’s population use a mobile device. Moreover, according to Statista, the current number of smartphone users
worldwide is 3.5 billion (Statista, 2020b). This means that 44.87% of people own a smartphone. The stream of data
generated by devices is therefore of an unprecedented size and continues to grow as devices are increasingly popular
and the range of telecommunications networks is being extended (Statista, 2020a).</p>
<p>The connection of devices to the Internet enables the collection of data by tracking activity with tracker files,
called cookies (Soltani, Canty, Mayo, Thomas, & Hoofnagle, 2009). European regulations require websites to inform
users about tracking, but this is not the norm in other regions of the world. Cookies help to optimize the website
design process by identifying individual users. For example, cookies allow a website to display advertisements for
products based on the users’ interests, purchase history and place of residence. Thanks to cookies, the user does not
constantly see the same advertisement. Cookies also help in shopping, allowing the website to ‘remember’ the goods put
in the digital basket and navigate through the store without having to log in to each subpage. Thus cookies exemplify
the common characteristic of the connected world: users are being tracked while benefiting from a useful technology,
and the data they generate are collected and used for purposes beyond their control (Degeling et al., 2018).</p>
<p>At the same time data are collected using traditional methods such as forms. This is a tedious method, compared to
automated data collection from devices, and later data standardization to organize responses in a unified database
(European Commission, 2020). Fast form filling on the Internet facilitates this process compared to its paper
counterpart. For example, online service providers offer their customers the option of subscribing to a ‘free’
newsletter. If the form filled in by the customer on the occasion is well thought-out, it can be a great source of
information enriching the customer database. People who decide to subscribe to the newsletter are probably already
interested in the offered product and there is a high probability that they will use it in the future. Forms are
commonly filled in by clients of financial institutions before they can be offered insurance, investment or banking
products. </p>
<h2>3. Using data</h2>
<p>Targeted advertising is where the bulk of data processing efforts are placed (Lau, 2020). Currently, the main
provider of this type of advertising is Google Ads (formerly AdWords). The essence of Google Ads is to support the
marketing activities undertaken by clients through the Google search engine. Modern technologies provide the
opportunity to specifically target a group. Advertisements are displayed to users who are most often already searching
for a given product or service. A well-prepared ad should encourage potential customers to click, and discourage
people who are not interested in the specific product or service, so that clicking them does not generate an
unnecessary cost. This is because the advertiser is charged per click, not per view or impression. The rates for one
click range from a few cents to a dollar or more (WIWI, 2020). The strength of Google Ads is that their results are
fully measurable. Advertisers can easily compare the advertising expenditure and the revenue generated thanks to it.
The ease of using Google Ads is increased by integrating the service with Google Analytics, thanks to which customers
can check what exactly happened before and after a user clicks an ad. This increases the ability to optimize
advertising activities.</p>
<p>Targeted advertising gives financial companies completely new opportunities to reach potential customers. Indeed,
they are concentrating their budgets on the Internet and away from traditional media. What is more, they can adjust
advertising expenditures and manage them virtually overnight, depending on the results. In the long run, such a
possibility should convince even those enterprises that have so far attempted to reduce advertising budgets or have
not decided to advertise on the Internet at all, especially since the more entities advertise on the Internet, the
less clout they carry and the more they are willing to pay for an attractive and effective campaign. The companies
behind tools such as Google Ads or Facebook ads are global giants who have invested billions of dollars in the
development of technologies that are not comparable to any products known before.</p>
<p>Location data are the second most easily generated and used type of information (Mayer-Schoenberger & Cukier,
2013). There are several satellite navigation systems, the most popular being the American Global Positioning System.
Other countries have implemented their own solutions: Europe launched Galileo, China Beidou, France DORIS, Japan QZSS
and Russia GLONASS. Satellite navigation technology was originally developed for the military, and only years later
was it made available to the public. Nowadays these systems are free and available to anyone who has a receiver. GPS
vehicle tracking systems bring measurable benefits, including cost savings and effective vehicle performance
management. The system helps choose the right route and reduce the fuel cost of the trip. Thanks to the advanced
reports and analyses that one can constantly receive, it is easy to assess the effectiveness of working time while
performing official duties.</p>
<p>In addition to dedicated receivers used specifically for tracking and locating, such receivers are found in virtually
every smartphone. Moreover, location data is available not only to the user who generates it. They may also be visible
to the developers of the app the user of which has allowed access to location data. This combination gives great
opportunities to track not only the location, but also the specific behaviour of potential customers. A properly
designed application can be used to monitor the safety of children or the elderly. The constant overview of the
location of a loved one makes it possible to check whether something unexpected has happened and whether help is
needed. A natural extension for financial services are automatic loss detection and its management by insurance
companies. </p>
<p>Furthermore, in conjunction with data on finances or purchasing preferences, it is possible to recommend specific
stores or locations, i.e. geotargeting. Targeting ads to geographic locations is possible in Google Ads. When planning
ads, companies have several geotargeting options available, thanks to which they can display ads to selected users in
preferred locations. Companies can choose a specific country, region or area around the selected location. Facebook
also allows geotargeting, where by default an advertiser can choose a specific country, province or city, but it
allows the location to be determined with precision. For example, ads may target people who have recently been in the
vicinity of the bank’s branch or a competitor’s branch.</p>
<p>The infrastructure of the location system itself is very expensive. The service life of satellites is now estimated
at around 8-11 years, which means that they have to be constantly replenished when worn-out. In this context, the
greatest advantage of satellite navigation systems is that they are available for free worldwide, much like road
infrastructure. In some cases, the user does not even need to buy a receiver to use the data efficiently. It is enough
to gain access to data generated by potential customers who have an app installed in their smartphone. Therefore, it
is possible to access very large data sets, while maintaining low access costs.</p>
<p>Autonomous cars are a technology that promises to revolutionize transport, and car insurance along with it. This is
possible thanks to a combination of several modern technologies. According to Polish law on road traffic, an
autonomous car is “a motor vehicle equipped with systems that control the movement of the vehicle and enable its
movement without the driver’s interference, who can take control of the vehicle at any time.” There are several
classes of car autonomy (Ondruš, Kolla, Vertaľ, & Šarić, 2020): </p>
<p>Class 0 – no autonomy. For example, an older generation car with cruise control, ABS or APS uses technology to help a
human driver in following his or her decisions.</p>
<p>Class 1 – driver support when driving on a highway or expressway. Accelerates and decelerates independently to match
their speed to the vehicle in front and to keep in lane.</p>
<p>Class 2 – partial automation and extension of Class 1. The car can take control of the steering wheel in certain
situations.</p>
<p>Class 3 – conditional automation. The car can take control of driving under certain conditions, such as parking. The
car itself observes the surroundings using sensors and vision systems and no longer supports the driver but drives the
car. However, the driver must always remain alert and be ready to take control in an emergency.</p>
<p>Class 4 – advanced automation. The car controls the entire vehicle. It is also prepared to react independently in
emergency situations. This means that the driver sitting inside - despite appropriate announcements - may not take
control in an emergency.</p>
<p>Class 5 – full automation. A car that does not require a driver’s presence and does not even need to have any
steering instruments installed.</p>
<p>For independent driving however, the on-board computer requires a huge amount of information from sensors located
throughout the car. These include: radars – devices that search for objects using radio waves; ‘lidars’ – devices that
works similar to radar but uses laser light instead of radio waves; a satellite navigation system, most often a GPS
receiver, that provides position information on the basis of radio signals sent from space; cameras that enable
computer vision, which involves the processing of an image by a machine into a digital description through the use of
vision systems for further use by a computer. An ordinary car can be equipped with up to 200 sensors, which in
everyday use generate up to 1 TB of data per day (Johnson, 2018).</p>
<p>Such technology allows in the long run to reduce the costs of transporting people and cargo, while eradicating
driving errors caused by human inattention or bravado. Perhaps this would reduce, among other things, the number of
road accidents. If successful, this would mean an overall improvement in the safety of the road transport industry and
the cost of maintaining it. Autonomous cars also make it possible to use the time normally spent driving as normal
working time. All the passenger’s attention could remain focused on issues related to work or making important calls,
rather than on driving. As a result, enterprises would avoid paying highly skilled workers for purely manual work.</p>
<h2>4. Data in financial services</h2>
<p>The financial industry has extensive experience in collecting data. For years, financial companies have built
internal high-quality databases waiting for internal use or sale outside the company. Opportunities to generate value
by selling data are limited, however, as the necessary anonymization of such data greatly reduces their value and
usability. The sharing of data itself is also quite problematic due to the regulations on sharing, processing and
protecting data. Banks, for example, have an unprecedented wealth of data about their customers, ranging from complete
personal and financial data, through account activity, made on the transaction website or mobile payment app. At this
point, such data can be used, among others, to better target specific financial products for specific customers. For
example, it is possible to offer extreme sports insurance to a client who has recently purchased professional diving
or skiing equipment.</p>
<p>Forms are one of the least loved elements of interaction between financial services and their clients. Automation
helps reduce this burden. In recent years, many countries have seen an increased interest in pre-filled forms
(European Commission, 2020). In Poland, the government made the biggest contribution to increasing this indicator by
introducing pre-completed tax returns. In recent years, Poland has improved its results in the use of pre-filled forms
and the provision of services via the Internet, and in terms of the use of open data ranks above the EU average
(European Commission, 2020). The government and financial institutions have long had access to huge databases of
citizen and customer data. Pre-filled forms are currently one of the few ways to skilfully use these datasets. Thanks
to this, citizens save time and generate fewer errors caused by inattention, and the state does not have to
subsequently correct these errors, thus reducing processing costs.</p>
<p>Financial companies can enter transactions with data brokers to sell or purchase data. Consequently, data do not need
to be used immediately in a creative way. There are many other entities that have developed different ways of using
our data. Such companies may want to buy data to enrich their database on existing customers with new characteristics.
In this case, it is enough to systematize the collected data in the manner required by the client. The value of data
increases when other companies use previously collected data in a much more creative way (Klimczak, 2021). Combining
customer conversation records and their entries in social media, for example, can increase the effectiveness of
chatbots or virtual agents used by banks. Data markets in the United States operate on this principle. Unfortunately,
sometimes it is possible to identify people based on previously anonymised data, leading to unethical situations. In
the USA, the federal and state governments are already taking the first steps to reduce data consumption, gradually
withdrawing from the almost complete freedom enjoyed by American enterprises. For example, in 2019 the State of
Vermont enacted a law requiring registration by companies engaged in the collection and trading of data. </p>
<p>Financial companies in Poland face significant challenges in developing their data-based competencies. DESI, the
Digital Economy and Society Index, measures the level of digital productivity (European Commission, 2020). According
to the report, Poland ranks 23rd with a score of 45.0 compared to the 28 EU Member States with an average of 52.6.
Almost half of Polish citizens do not have basic digital skills, and about 15% do not use the Internet at all. The
skills of using computers, the Internet, and related technologies by Poles remain at a low level compared to the
European Union average. Only 44% of Poles aged 16-74 have at least basic digital skills, and specialists in the field
of ICT (Information and Communication Technologies) constitute only 3% of Polish society. This implies the lack of a
sufficient number of specialists who could improve the situation in enterprises, hence most of them do not use modern
technologies such as cloud services or social media. Enterprises at a low degree of digitalization constitute as much
as 60% of the market, and only 11% companies are highly digitalized. Internet sales are conducted by 13% of
enterprises from the SME sector, of which 5% conduct foreign sales to clients from other EU countries. Only 7% of
enterprises use the cloud, and 8% work with BigData. In these categories, innovative companies with a short history in
the market, i.e. start-ups, are relatively better.</p>
<p>The situation in the European Union is better than in Poland (European Commission, 2020). Basic digital skills are
possessed, on average, by 58% of the population. ICT specialists account for 3.9% of the labour market. Enterprises
with a low degree of digitalization constitute 39% of the market, while highly digitalized enterprises account for 26%
of the market. Internet sales are conducted on average by 18% of enterprises from the SME sector, of which 8% conduct
foreign sales to clients from other EU countries, including Poland. As many as 18% of enterprises use the cloud, and
12% work with BigData.</p>
<p>Poland is gradually increasing its level of digitization, however other member states are doing the same. In order to
catch up with the leaders, Poland needs to accelerate the development and research on the use of information
technologies in financial enterprises. One of the biggest challenges may be the lack of qualified staff, which is
already a development bottleneck in the SME sector.</p>
<h2>5. The data economy</h2>
<p>The current level of development of work on data sets and the speed of the development of technologies cooperating
with BigData indicate huge opportunities related to the analysis of the data stream in real time, however these
technologies will require new data transfer solutions. For example, according to data from IAV GmbH (BankMyCell,
2020), a single autonomous car produces up to 15 terabytes of data per hour. Storage drives with a capacity of 128 to
2048 GB that are usually installed in computers and laptops sold today would be full within a couple of minutes. The
transfer of such amounts of data would literally clog up the mobile telecommunications network, especially in the
event of an increase in the number of cars fitted with autonomous driving devices. Hence, investments in the
development of 5G technology offering transmission speeds of up to 20 Gb/s are a key infrastructural technology. </p>
<p>The 5G telecommunications network is currently under construction in Poland. Transmission speeds of about 400-500
Mb/s are becoming available in major cities (Duda, 2020). The introduction of 5G technology to widespread use, and in
the future 6G (estimated speed ~95 Gb/s), will enable the introduction of fully autonomous cars. With sufficient data
the cars can communicate with each other to avoid collisions and accidents. The technology can also benefit the online
mapping industry. Real-time data from cars and from phones located in cars can be used to create real-time traffic jam
maps. As a result, the systems will be able to better estimate travel times and intelligently predict the possibility
of traffic jams before the cars are lined up, one after the other, and cannot move. Combined with autonomous cars,
this will reduce fuel or electricity consumption. Technology for the computer recognition of traffic signs is also
needed. The proliferation of the autonomous car business activity could change the perception of public transport,
which could focus on making more smaller cars available through carsharing, rather than large vehicles for mass
transport. What will definitely change is car insurance, the most important line of insurance products today.</p>
<p>These changes are likely to differ depending on the country/region of introduction. This is due to the differences in
the insurance systems between countries. In Poland, for example, civil liability insurance is purchased for a specific
car, and the information about a specific driver contained in the contract is optional and most often relates to age
or experience. However, there are countries where insurance is purchased on the driver, who can therefore drive
virtually any vehicle. In the situation of the popularization of autonomous cars, it is the software, not the driver,
that will be responsible for the safety of passengers and the environment around the car (Margan, 2018). It is
therefore difficult to speculate about insurance based on the age or experience of the driver, if the driver might not
even be behind the wheel of the vehicle. Such a situation would require the reconstruction of the entire insurance
system, which would be based on the quality of the software and the sensors built into the car. Until an agreement on
liability is reached on autonomous cars, it will be difficult to establish liability as the control of the vehicle
varies between the manufacturer, driver, software provider, etc. Insurance for such a car could be passed on to the
manufacturers who in turn would pass it on to customers in the form of annual fees. Properly-functioning software
could lead to a decrease in the number of accidents, which could also lead to a total change in the automotive market.
Any innovative changes create challenges that the industry will have to face in the coming years.</p>
<p>In future, digitalization may lead to changes in decision-making in financial enterprises, where highly-paid
specialists can be replaced by self-learning algorithms using Big Data (McAfee& Brynjolfsson, 2012). The notion of
transferring responsibility from human decision-makers to automata raises many doubts, however (Newell &
Marabelli, 2015). Organizational and social inertia will probably reduce the pace of technology adoption
significantly. Nevertheless, the replacement of humans with algorithms in the majority of routine tasks appears to be
inevitable.</p>
<p>To sum up, the balance of the benefits and costs of digitalization is a multidimensional problem, and the
profitability of the implemented solutions depends on many factors. A tendency can be noticed that most of the
technologies used now were being developed over many years, and are the work of corporate giants who, based on these
projects, have earned their place on the market. A perfect example of this is Google with the most popular search
engine in the world accounting for 92.71% of the search market (StatCounter, 2020), which took decades to perfect.
Companies that cannot afford and sustain multi-million investments in research and technology development are forced
to use ready-made solutions proposed by larger enterprises. Naturally, the providers of these solutions capture most
of the value generated from data. </p>
<h2>6. Conclusion</h2>
<p>This article outlines the global progress of digitalization and its impact on financial services by looking at
sources of data, their potential applications, and the emergence of a data economy. It reviews the opportunities and
challenges that lie ahead for the financial services industry in the context of the global transition to a data
economy. The opportunities include technologies for targeting services to clients that may need them, using data on
consumer activities. This begins with advertising and client acquisition, but also with offering services to existing
clients on the basis of real-time data so that a personalized offer can be presented when and where the client needs
it. Speeding up the sales process with pre-filled forms and replacing lengthy bureaucratic decision-making processes
with algorithms are some of the applications. Furthermore, the spread of mobile devices has led to the emergence of
new IoT-based products whose potential has not yet been fully realized in financial services. Similarly, the trend
towards autonomous and driverless cars signals a profound change in the insurance market. Moreover, the sector itself
is being challenged as financial companies face stiff competition from global Internet giants who control the bulk of
international data flows and capture most of the value generated from the data they process. Consequently, financial
service companies need to focus on arranging their own resources in novel and profitable ways. In the case of Poland,
the main challenge lies in creating a technically qualified workforce capable of carrying out this transformation.</p>
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https://www.pozycjonusz.pl/ile-kosztuje-reklama-w-google-ads-adwords/</p>
<p>Żyrek, A. (2020). CCPA, czyli ochrona danych osobowych w Kalifornii. Retrieved from <a
href="https://www.parp.gov.pl/component/content/article/63647:ccpa-czyli-ochrona-danych-osobowych-w-kalifornii">https://www.parp.gov.pl/component/content/article/63647:ccpa-czyli-ochrona-danych-osobowych-w-kalifornii</a>
</p>
<p>PRZEJŚCIE DO GOSPODARKI OPARTEJ NA DANYCH <br />W USŁUGACH FINANSOWYCH</p>
<p>Streszczenie: Cel artykułu to identyfikacja szans i wyzwań stojących przed firmami świadczącymi usługi finansowe w
Polsce w zakresie przetwarzania danych. Połowa konsumentów na świecie jest otoczona urządzeniami do zbierania danych.
Sektor usług finansowych jest coraz bardziej świadomy potrzeby zmian, aby stać się częścią gospodarki opartej na
danych. Ta transformacja jest złożonym procesem, wymagającym najlepszych umiejętności zarządzania, aby odnieść sukces.
Przedsiębiorstwa powinny być jak najbardziej innowacyjne, ale jednocześnie nie mogą przeznaczyć wszystkich swoich
zasobów na inwestycje w technologię. W artykule dokonano przeglądu literatury na temat aktualnego rozwoju technologii
opartych na danych, aby przedstawić zarys środowiska, do którego firmy finansowe muszą się dostosować. Opisano źródła,
z których można pozyskiwać dane, i podano przykłady stwarzanych przez nie możliwości. Omówiono takie kwestie, jak:
autonomiczne samochody, geolokalizacja oraz reklama celowana. Zaprezentowano przegląd wyzwań związanych z
przetwarzaniem danych oraz przejściem do gospodarki opartej na danych.</p>
<p>Słowa kluczowe: Big Data, Internet rzeczy, danetyzacja, zmiana technologiczna. </p>
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<p>* This work is supported by the National Science Centre in Poland (NCN) under research grant<br />no.
2019/35/B/HS4/03800 entitled The evolution of corporate reporting language: textual-analysis with computer methods.
</p>
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