=Paper=
{{Paper
|id=Vol-2815/CERC2020_paper23
|storemode=property
|title=An Innovative IoT Based Financing Model for SMEs
|pdfUrl=https://ceur-ws.org/Vol-2815/CERC2020_paper23.pdf
|volume=Vol-2815
|authors=Marcel Kehler,Stefanie Regier,Ingo Stengel
|dblpUrl=https://dblp.org/rec/conf/cerc/KehlerRS20
}}
==An Innovative IoT Based Financing Model for SMEs==
https://ceur-ws.org/Vol-2815/CERC2020_paper23.pdf
Business and Society
An Innovative IoT Based Financing Model for SMEs
Marcel Kehler1, Stefanie Regier1 and Ingo Stengel1
1 University of Applied Sciences Karlsruhe, Germany
marcel.kehler91@gmail.com
Abstract. Industry 4.0 and the developed technology of the Internet of Things
are changing the global economy rapidly. Companies have completely new op-
portunities to sell their products and services. This creates new business models,
such as p ay-per-use, in which billing is based on the data collected. In this pa-
per, a way is being worked out how banks can use this data for themselves in
order to offer their business customers new financing products. For this purpose,
the requirements of companies wishing to offer pay-per-use were addressed qual-
itatively. A financing model was then developed on the basis of this, which in
turn was qualitatively assessed by banking experts. In a final online survey, the
financing model was assessed quantitatively by the target group. In addition, the
risks associated with the changes are discussed and proposed solutions for elim-
inating them are presented.
Keywords: finance 4.0, IoT, banking, financial innovation, SME.
1 Introduction
Industry 4.0 is currently considered the fourth industrial revolution worldwide. Follow-
ing the invention of the steam engine, the discovery of electricity and automation
through electronics, and the invention of the personal computer, "smart" objects and
the associated technology of Internet of Things (IoT) are now leading to a further par-
adigm shift [16].
However, this revolution is associated with enormous investment costs [3]. Shorter
innovation cycles and constant cost pressure are putting increasing pressure on small
and medium-sized enterprises (SMEs). But it is not only in industry that major changes
are currently taking place. In the banking sector, the low interest rate period, new reg-
ulations and digitalisation mean that those involved will have to rethink and change
their processes in order to remain competitive [4]. Industry 4.0 offers a great oppor-
tunity for banks in this context [14]: with the end-to-end networking of devices and the
resulting data, new business models can be developed that are optimally tailored to the
personal needs of customers [14]. In the following, a financing model will be presented
that uses IoT to flexibly adjust the liquidity burden of investment loans to the respective
situation of the company in order to take account of economic and seasonal fluctuations.
Copyright © 2020 for this paper by its authors.
Use permitted under Creative Commons License 361 CERC 2020
Attribution 4.0 International (CC BY 4.0).
Business and Society
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2 Conceptual basis and relevant literature
Kevin Ashton first described the Internet of Things (IoT) in 1998 as the ability to con-
nect people and objects at any time, in any place, with anything and everything, and
ideally through any network or service [1]. IoT should represent all devices and persons
in the virtual world and connect them with each other with the help of a virtual footprint
[9]. The development of IoT is divided into five phases: the networking of two com-
puters, the introduction of the Internet, the development of smartphones and the asso-
ciated mobile Internet, social networks and finally the networking of objects from daily
life, such as cars, cash machines, lamps or refrigerators [14],[18].
In the banking sector, too, this concept is becoming increasingly important in order
to offer bank customers benefit. According to Drinkwater, this includes not only the
use of wearables and Bluetooth beacons but also the use of intelligent cars as mobile
ATMs, block chain technology and the development of chatbots [5]. Wang comple-
ments these with the possibility of real-time analysis of the devices and the use of ca-
pacity utilization and idle time for better pricing of leasing offers [7]. In their study,
Ramalingam and Venkatesan are working on an ATM 2.0 that enables the use of IoT
to connect to smartphones. In this way, they make it possible to withdraw money with-
out a bankcard [20].
When using intelligent objects, large amounts of data are generated that have to be
stored, processed and analysed. This enormous data is summarised under the term Big
Data. The use of large amounts of data also offers great advantages in banking. The
application scenarios described in the literature can be divided into the following cate-
gories: security, risk management, customer relationship management (CRM) and other
uses. The studies [10], [11], [19] and [21] provide a very good overview of Big Data in
the banking sector. In the security category, Wongchinsri and Kuratach's [23] meta-
study provides an overview of the current technologies in this area, especially with re-
gard to error detection and new customer selection. They analyse 41 articles dealing
with this topic area. The articles mentioned there are very well suited to gain a deeper
understanding of the topic. Kharote [15] uses Big Data to detect money laundering. To
make this possible, he has developed a framework that analyses transaction data using
a new algorithm. Through this, he tries to detect anomalies and thus identify possible
money laundering. In the CRM category, the researchers try to make statements and
predictions about the banks' customers. A wide variety of approaches is used, each with
different objectives. In their 2014 study [8], Fuschi and Tvaronaviciene make recom-
mendations on how to deal with Big Data so that service quality can be guaranteed.
They recommend that quality guidelines and the way data is collected to be constantly
redefined. These should be adapted to the constantly changing requirements. Others
try to analyse their customers with large amounts of data. In their study, Srivastava and
Gopalkrishnan [22] use the example of an Indian bank to show how big data can be
used to analyse customers and understand their behaviour. They focus on the output
patterns of their customers, the sales channels used, customer segmentation and profil-
ing, product cross-selling, mood and feedback analysis, and security and error detec-
tion. The data provided by the bank is processed and analysed using descriptive statis-
CERC 2020 362
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tics. In this way, the authors clearly show how Big Data can be used to analyse custom-
ers and thus generate significant benefit for banks. Other researchers try to use Big Data
to analyse their customers' supply chains in order to identify potential customers and
gain advantages over the competition. In their study, Hung and colleagues [25] were
able to clearly show that the response and completion rates of potential customers pre-
viously analysed by Big Data were significantly higher. Risk management attempts to
make predictions or assessments of the risk associated with the loan. The data is often
included in the scoring of the customer. This can be done in various ways. Yadav and
Thakur [24] have used past customer usage data to assess the risk of each customer.
Calis et al. [26] use clustering and classification to assess the risk of customers. In his
2018 study [17], Odinet identified several frameworks that use large amounts of data
to identify unfair credit to customers. Hurley and Adebayo [12] take a different ap-
proach. In their investigations, they uncover Big Data's problems in credit scoring and
the gaps in legislation.
A review of the relevant literature on IoT and banking shows, that there are no stud-
ies and concepts that use the resulting data volumes for a financing model. Thus, pro-
vide the customer with innovative financing products. Therefore, this subject will be
addressed in this paper. The focus is set mainly on small and medium-sized companies
that offer their customers the pay-per-use business model or plan to offer it in the future.
3 Research methodology and study design
The following results were compiled using the mixed-method approach. This approach
combines elements of qualitative and quantitative research and thus enables a deeper
understanding of the interrelationships [13].
In qualitative research, verbal, visual and audio-visual data are collected, structured,
and analysed in the course of the project. This approach attempts to understand the
behaviour of the participants and then to transfer it inductively to the general public [2].
In this work, guideline-based interviews with decision-makers from small and medium-
sized enterprises were conducted. These interviews aimed to gain new insights into the
current situation and the challenges facing companies. In addition, requirements for
banks and their products are defined. Based on this, a financing model is designed. This
was analysed and improved in further interviews with banking experts. The focus here
is primarily on the requirements for banks, to assess the feasibility and to identify prob-
lems that are likely to arise. The aim of the second qualitative study was to examine the
feasibility from the banks' point of view. A total of eight interviews with bank repre-
sentatives and SME decision-makers were conducted in March and April 2020.
The modified financing model was then presented in the form of a standardised sur-
vey to SME decision-makers, who were asked to evaluate the concept. The aim of the
quantitative survey in April and May 2020 was to obtain an initial assessment of the
acceptance of the developed financing model in the market and the willingness of com-
panies to pass on customer data. With almost 30 decision-makers from SMEs having
completed the survey, the sample is not representative due to the number alone, but it
363 CERC 2020
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does provide initial indications of the acceptance and possible weaknesses of the con-
cept. The new financing model as well as challenges and evaluation have been devel-
oped and optimised directly from the interviews and the survey and will be presented
in the following sections.
4 The IoT financing model
The target group for the developed financing model are SMEs that want to offer their
customers the pay-per-use business model or already do so. With pay-per-use, the cus-
tomer does not buy the product, but only pays for the use of the product. Since this
model is designed to generate more money in the medium to long term, the SME must
first make advance payments. They must make their products available without directly
receiving the full sales price. This means an enormous capital requirement, especially
when scaling up. After a successful launch, the recurring revenues will finance further
development and production of new products.
Fig. 1. Overview of the different roles
The financing model is based on the pay-per-use business model and therefore uses
the same calculation basis as used previously mentioned. With the financing model, the
repayment amount is calculated flexibly on the basis of the end customer's user data. In
order to ensure that planning security is still required, a maximum financing period is
agreed during which the loan must be paid off, if the financed product is not used.
However, if the product is used, an additional, previously agreed amount is repaid for
each unit. Thus, the more intensively the financed product is used by the end customer,
the higher the manufacturer's repayment to his bank and the shorter the term.
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The roles involved in the financing model are shown below and the respective rela-
tionships are clarified. The interaction of these roles is shown in Figure 1.
The SME plans to offer its products in the future with the pay-per-use business
model. This products could be for example machines (Machine-as-a-Service), lamps
(Light-as-a-Service) or cars (Mobility-as-a-Service). To this end, it will make its prod-
ucts available for use by its end customers and bill them on the basis of usage. In order
to be able to offer the model across companies, they need a cloud in which they will
consolidate, evaluate and automatically calculate their customers' usage data. This will
be operated as part of his billing. By selling via service contracts, it will generate re-
curring income, but this only covers the costs incurred on a medium to long term. When
scaling, this results in a significantly higher capital requirement for the products, which
has to be covered with the help of a loan.
The end customer wants to avoid the risk of a capital-intensive purchase and still use
the manufacturer's products. In addition, they want to have greater planning security
and avoid unforeseen expenses for repairs or new investments, for example. For these
reasons, they are looking for new business models with more planning security and less
risk of default. He uses the new business model from the product manufacturer and uses
his product without being the owner. Billing is based on the intensity of use.
The intelligent product is provided by the manufacturer and used by the end cus-
tomer. It records a wide variety of data via sensors built into it, which is then transferred
to the cloud operated by the manufacturer. From there, all participants can access data
and analyse, evaluate and use it for billing.
The bank provides the manufacturer with the required capital and at the same time,
it offers him an innovative, flexible financing model that will adjust the repayment rate
to the end customer's usage. To do this, the bank is given access to the data stored in
the cloud.
5 Challenges
When introducing pay-per-use or using the financing model, there are four challenges,
which are explained in more detail below:
─ Early repayment of the loan,
─ Minimum redemption rate in the absence of turnover,
─ Cluster risks,
─ Change in the manufacturer's balance sheet.
Thanks to the design of the financing model, the early repayment of loans is no
longer an exception, but has been deliberately brought about. However, this entails an
increased risk for banks. They have to borrow money from other credit institutions,
such as the European Central Bank (ECB), in order to grant loans to their customers.
For private customers, this will generate costs. A good indicator of the costs incurred
is the swap rate, which describes what fixed interest rate banks are prepared to pay for
certain terms [6]. Due to a high utilization of the machine, the loans will most likely
be repaid before the current financing term. As a result, the costs will continue to exist,
365 CERC 2020
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but on the other hand no income will be generated. If this is not taken into account in
the calculation, it is possible that banks will suffer losses. In order to counteract this,
banks must consciously plan and calculate risks. Early repayment interest rates, which
are based on the swap rate, are suitable for this purpose. In this way, banks will protect
themselves against a loss and the borrowers still will save parts of their costs (from a
holistic perspective).
A further challenge is the dependence on customer benefit. In extreme cases, this
can lead to the end customer not using the machine and thus not generating any income
for the manufacturer. However, the manufacturer has to service the minimum redemp-
tion amount toward the bank. This situation can be life threatening for the manufacturer.
In order to counteract this, the manufacturer should always agree a minimum purchase
quantity or a monthly rental price with his end customer with which he can certainly
cover the redemption payments. Regardless of this, an end customer can of course also
become insolvent. However, this risk also exists with other types of credit. Moreover,
the manufacturer has spread his risk over several customers and can make the product
available to another customer as quickly as possible.
Another risk that was identified during the expert interviews is the cluster risk at
banks. In the case of capital-intensive products, before the introduction of pay-per-use,
the end customers were obliged to finance the products. As a result, the necessary
amount of loan was spread over many companies and the bank was diversifying the
existing risks. Furthermore, it is very unlikely that all end customers were at the same
bank. As a result, the amount of credit was not only distributed among different cus-
tomers, but also among different banks. Pay-per-use increases the risks for banks, since
the manufacturers alone now need full financing. Although, it can be assumed that the
manufacturing costs are significantly lower than the current sales price, the financing
requirements of the manufacturer are significantly higher than those of individual end
customers. In addition, there is no need for the manufacturer to distribute the loan
among several banks, since the manufacturer normally wants to take out a loan with his
house bank. In conjunction with the regulatory regime to which all banks are subject,
this can mean that the loan cannot be guaranteed by just one bank. In addition, this leads
to an increase in the risk and as a consequence also to an increase of the costs of the
credit. In this case, the bank's action possibilities are limited due to the regulation con-
trolled by the banking supervisory authorities. This means that to jointly cover the forth-
coming large capital requirements, banks can only look for partner banks in advance.
The final challenge for the financing model is the change in the manufacturer's bal-
ance sheet and the resulting lower rating. Assessing the manufacturer's creditworthi-
ness, calculating the risk and the associated premium is always a particular challenge
with a new business model. According to the Basel 1, 2 and 3 resolutions, banks are
required by the Banking Supervisory Authorities to carry out a rating for each borrower,
in which they assess the borrower's creditworthiness. The worse the rating, the greater
the risk and the higher the interest on the loan. In the calculation, a worst-case consid-
eration must always be carried out and thus both qualitative and quantitative indicators
are used. For example, the industry and the competitive position of the company are
used as qualitative indicators. The annual financial statements and the balance sheet
contained therein are used as quantitative characteristics for the rating. From these, key
CERC 2020 366
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figures are calculated that are used for the rating. A very important key figure in the
rating is the equity ratio, which expresses the proportion of equity to total capital. The
lower the equity ratio of a company, the worse is its rating. The introduction of the pay-
per-use billing model has far-reaching effects on the provider's balance sheet and thus
also on the key figures calculated for the rating. With pay-per-use billing, the products
are no longer sold, but made available and leased using a service contract. As a result,
the products manufactured are listed on the assets side of the balance sheet. These are
typically, in the best case, financed by debt using the financing model developed here.
This external financing is noticeable on the liabilities side of the balance sheet. As a
result, the balance sheet total rises significantly and the equity ratio, which is important
for the rating, deteriorates significantly. In turn, this will lead to higher risk interest
rates, which will make the model unprofitable for manufacturers. This problem can be
solved in two ways. The first option is to develop a new rating system that takes into
account the exact conditions and might include data generated by the product. The other
possibility is to spin off the company into a subsidiary. In this way, the manufacturer
can continue to sell its products in the parent company as before and implement the
pay-per-use business via a subsidiary.
This can then be rated by the bank in the role of a leasing company. Experience
shows that these have a similar balance sheet structure.
6 Evaluation and Conclusion
The evaluation of the financing model by SME decision-makers provides initial indi-
cations of the acceptance, possible problems and opportunities of the model on the mar-
ket.
Already 50 % of the companies surveyed are already recording data, and are using
it for a variety of reasons. 10 % of the SME’s already use the data for billing their
services. Another 10 % use the data within the scope of service and maintenance con-
tracts. Surprisingly, 25% of those surveyed record data but do not yet use it or only use
it in individual cases. The most important finding is the possible potential of the financ-
ing model. 3 out of 4 companies consider recording the usage behaviour of their cus-
tomers to be useful and are therefore also considered as a target group for the financing
model.
Overall, it can be said that the IoT financing model has met with great interest among
the test persons. On a scale of 5, ranging from very unattractive to very attractive, the
test persons had to assess the attractiveness of the IoT financing model presented. 84
percent of the respondents rated the model as very attractive or attractive. Only 16 per-
cent opted for the neutral middle. The survey participants saw the advantages of the
idea of calculating the repayment participation on the basis of IoT data primarily in the
flexible liquidity burden.
However, the survey also shows that many companies are still uncertain whether
they are allowed to share the data with third parties from a data protection perspective
and whether they need the consent of their customers to do so. For this reason, it is
extremely important to sensitise customers to the topic of data protection from the start
367 CERC 2020
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8
and to explain to them in a comprehensible manner when the DSGVO takes effect,
which regulations must be complied with and how this is implemented in the present
financing model.
In addition, the lack of skilled workers is a major problem. The technical professions,
which are also indispensable for the introduction of pay-per-use, are particularly af-
fected. Companies have to think of suitable concepts for attracting talented and well-
trained people and retaining them in the long term. Banks have to think about how they
want to counter the conflict of objectives between the shortage of skilled workers and
cost reduction in the long term. This is the only way to implement and change towards
usage-based billing models.
In addition to the problems already mentioned, both the expert interviews and the
online survey revealed that banking regulations are an enormous challenge. Here, it is
of utmost importance for the success of the financing model that a new regulations are
found that enables banks to provide a suitable rating for companies that act on the mar-
ket as both product and service providers. Only in this way companies can take this
innovative path without being put at an enormous disadvantage in future lending.
In summary, it can be said that the model presented here is suitable for companies
who, in addition to selling their products, also want to position themselves as service
providers in the market. This business model is less suitable for companies that want to
act entirely as a service provider in the future, as banking regulations will push up risk
costs too much. This is mainly due to the fact that there is no suitable rating model for
the pay-per-use business model. This is precisely where further research will start.
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