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«Наука через призму времени»
Декабрь, 2017 / Международный научный журнал
«Наука через призму времени» №9 2017
Автор: Носов Андрей Сергеевич, Студент Института экономики и менеджмента
Рубрика: Экономические науки
Название статьи: Using neural networks to evaluate the credit benefit of the borrower
УДК 2964
USING NEURAL NETWORKS TO EVALUATE THE CREDIT BENEFIT
OF THE BORROWER
Nosov Andrei
Student
Kemerovo State University, Kemerovo
Abstract. In our time, the world is rapidly changing and new
technologies are coming to our lives. In this article, we are trying to
disassemble one of the newest technologies, namely the neural network, namely
its application in order to assess the borrower's creditworthiness.
Keywords:
neural network, credit rating, modern technologies.
In this article, I would like to raise the problem of
using modern technologies in business and banking, in particular the use of
such an innovative technology as a neural network. To date, the world is full
of uncertainty and risk, the external environment of economic objects is very
volatile and little predictable. First of all this is due to the rapid
development of IT technologies that broke into our lives. Undoubtedly, the
world has changed very much over the past ten to twenty years, thanks to the
opportunities that IT technologies gave to business. Today, the lion's share of the processes is automated and does not require
human participation, although recently we could not imagine that the place of
the man behind the assembly line at the Ford plant will be occupied by a car.
But now this is a reality the world of technologies is developing and this is
pushing the economy forward.
And today in this article we will see that in the near
future some professions and specialties that seem to us an integral part of
society will not be needed. Consider this issue in the sphere of banking
services, namely in the sphere of lending to individuals. As you know, the
average size of a loan to an individual is much less than that of a legal
entity. At the same time, the cost of issuing a loan to an individual is quite
high, in particular, the analysis of a person's creditworthiness and the cost
of wages to a bank employee reduce the profitability of the loan. Naturally,
this raises the cost of this service. At the moment, banks are assessing the
creditworthiness of individuals on the basis of a scoring model. This approach
is based on the use of a quantitative risk assessment calculated by the credit
institutions themselves based on the results of a comprehensive analysis of the
borrower's activities. At the same time, the borrower's financial position, the
quality of servicing the loan debt, the borrower's gender, the number of
children, education, how many years he has been employed in his organization
and other information available to the bank are taken into account. Scoring is
designed to collect heterogeneous data, to give them weight. As a result, the
client of the bank receives a scoring score and is referred to a certain group
by the degree of risk of non-return of funds borrowed. For all its advantages,
it can not be said that the scoring model of creditworthiness assessment is
without flaw. It has a number of drawbacks: it needs to be reviewed at least
once every six months; banks independently subjectively develop this model; it
is necessary for a person to participate in a full evaluation. [3]
At this stage of development, there is a technology
that can replace the scoring model in the foreseeable future, and oust
specialists from the market, in assessing credit risk. This technology is a
neural network. The neural network is a sequence of neurons connected by
synapses. The structure of the neural network has come to the programming world
straight from biology. Thanks to this structure, the machine is able to analyze
and even memorize various information. Neural networks
are also able not only to analyze incoming information, but also to reproduce
it from their memory. A neuron is a computing unit that receives information,
performs simple calculations on it, and passes it on. They are divided into
three main types: input, hidden and output. There is also a displacement neuron
and a contextual neuron. In the case where a neural network consists of a large
number of neurons, the term layer is introduced. Accordingly, there is an input
layer that receives information, n hidden layers (usually there are not more
than 3 of them) that process it and an output layer that outputs the result.
Each of the neurons has 2 basic parameters: input data (input data) and output
data (output data). In the case of the input neuron: input = output. In the
rest, the total information of all neurons from the previous layer gets into
the input field, after which, it is normalized using the activation function
and falls into the output field. [2]
But within the framework of this article, we will not
delve into the work of neural networks, this is not
our ultimate goal. It is important for us to understand that the neural network
is trained, in addition it accumulates experience and
makes fewer mistakes with each new stage of training. A new neural network can
be compared to a child who, every year, improves and makes fewer mistakes,
relying on his experience.
This gives us great opportunities in the field of
lending. We can learn the neural network by introducing data that can not be
taken into account in scoring analysis!
You can consider the introduction of a neural network
using the example of abstract bank X. Assume that Bank X has information about
its customers, as well as people who have been denied credit. We make create
incoming neurons and teach the neural network based on this data. We will enter
the parameters of the scoring model, and the decision of the bank to issue a
loan. If there is a failure, we will have one output neuron. When issuing a
loan, there may be several outcomes:
• The loan was returned on time;
• The loan is not returned;
By inserting these data, we will teach the neural
network to work similarly to the scoring model. But, with further training, the
neural network is much better than the scoring model of evaluation, since it
will determine the weights of the factors, establish interrelations, and
therefore it will learn! The neural network will independently determine market
trends and respond to them, much faster than a person.
In addition to these indisputable advantages of the
neural network there are a few more, no less significant. We can withdraw
lending through the bank's website to a new level! This will not just leave the
application and a preliminary decision to grant a loan. It is possible to make
such a level of credit rating on the basis of a neural network that the bank
will be able to issue loans online with minimal risks. This can be achieved
through modern analysis technologies. The neural network can check the social
networks of the borrower, recognize the photographs (for example, if the
borrower often happens abroad, then he has higher solvency), learn geolocation, client routes and draw conclusions about his
solvency! Also, the bank will be able to reduce labor costs, as the evaluation
staff will no longer be needed. We need a reduced staff to work with the neural
network.
At the moment, there are already examples of the
neural network being squeezed out by some employees in the banking sector. In Sberbank was dismissed four
hundred and fifty lawyers. They were replacing the neural network. Herman Gref (CEO of Sberbank) advised
lawyers who do not understand neural networks, master new technologies. As he
said, with work on preparation of statements of claim neural networks now
consult better people.[1]
As a result, I want to say that the world is
constantly changing and our task is not to lag behind the current trends, to
keep pace with the times. And these are not just words, it must be done, at
least, in order to remain an actual labor force in the labor market and find a
place in the professional sphere. Of course, it is difficult to predict the
changes that will occur in a few years, let alone forecasts for a longer-term
perspective, but even now one can say that the technology of neural networks will
change the world, as we saw after examining its possible impact on banking.
Bibliography:
Список литературы:
- Official website of RBC TV channel:// http://www.rbc.ru/business/23/07/2017/5974b7a69a79477896b6708d
- Portal about programming and modern technologies:// https://habrahabr.ru/post/312450/
- International convergence of capital measurement and capital standards: refined framework approaches (Basel2) / Bank for International Settlements, 2012 – P. 266
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