The document discusses the integration of big data in consumer credit scoring, emphasizing the need for systematic processes in evaluating borrowers and managing credit portfolios. It outlines solutions such as real-time scoring based on behavioral analysis and automated workspaces for credit analysts, targeting financial organizations as clients. The document also highlights the qualifications of the team members and their extensive experience in IT and finance.

1. Big Data и
and credit
scoring

2. • Consumer crediting is one of the
highest priorities in financial sector
nowadays that demands maximum
attention. This process should
be approached systematically
and there should be efficient and
automatic process of borrowers
examination.
• Organization of continuous work
under repayments of granted credits.
• Preparation of credit portfolio for
sale or collectors.
Problems and our solutions
• Efficient scoring of potential borrower
based on “big data” analysis.
• Receipt of additional statistics
based on analysis of financial and
nonfinancial data set for further
communication with client.
• Work with credit portfolio of a client for
minimization of delays and elimination
of missed credit payments.
• Preparation of credit portfolio for sale
or collectors.
Problems Sulutions

3. During the development of software we use the stack of technologies from Microsoft,
Microsoft: .Net, Azure cloud, C#, and also technologies CUDA.
Technologies and algorithms
Our algorithms and models of analysis are based on:
• Group of self-learning neural networks;
• Systems of normalization of input parameters and semantic analyzer for
parsing of text information;
• Formation of psychological profile of potential client;
• Method of clustering data;
•
• Classical scoring systems.

4. Our clients are any financial
organizations that provide crediting
services:
Who are our clients?
Banks
Credit
Institutions
Systems of
р2р-crediting

5. Benefits for clients
• Efficient scoring (real time scoring based on behavioral analysis) of
potential borrower based on analysis of “big data” in the moment of loan
processing, loans support and after-sales servicing;
• Receipt of additional statistics based on analysis of financial and
nonfinancial data set for further communication with client with the
purpose of increasing conversion of offered financial and nonfinancial
products;
• Automatization of workspace of credit analyst, formation of the range
of roles for automatization of loans granting process, loans portfolio
support and offering clients relevant services in real time.

6. Business models
We use two types of business:
Purchase of annual licence for using of software
(Banks, credit institutions)
Provide service based on payments for analysis of one
(for small credit institutions, for systems of р2р-crediting)
questionnaire of potential client

7. Команда и контакты
CEO, age 32, Kiev
More than 10 years in IT business.
Successful sale of several businesses.
Management of several IT-companies
(Eureka! Solutions, Ticket Solutions).
Working as technical analyst in FISON fund.
Education: Management on goods and
services market, Kiev National Trade and
Economic University,
Department of Economics.
Anton Vokrug
anton.vokrug@gmail.com
+38 (093) 414-29-32
https://www.linkedin.com/in/antonvokrug
https://www.facebook.com/anton.vokrug
CТO, age36, Kiev
More than 15 years of development
of software and web-services, perfect
acknowledge of web technologies (.Net,
C#, C++, MS Azure, Java, PHP, Ruby).
Education: Applied mathematics, Taras
Shevchenko National University of Kiev,
Department of Cybernetics.
Alexander Gandzha
sasha.gandzha@gmail.com
https://www.linkedin.com/pub/sasha-
gandzha/18/921/a59
https://www.facebook.com/sasha.gandzha

8. Команда и контакты
Marketing, age 32, Kiev
Education: Prydniprovs’ka State Academy of
Civil Engineering and Architecture,:
Computer-Integrated Technologies.
Academy of Business and Law:
management innovative activity
More than 10 years of marketing and
promotion. Co-founder of venture capital
fund FISON. One year of work as a board
member of ukrainian syndicate UAngels.
Elena Khlevnaya
lena@fison.org
+38 (063) 845-36-74
https://www.linkedin.com/pub/elena-khlevnaya/9a/643/2a
https://www.facebook.com/lena.euro
Bank advice, age 39, Kiev
More than 10 years of successful work in
banking sector. СB “Privat Bank”, JSC
«Сведбанк» (public), Venture Fund “Лайф-
Среда” (project “LifePad”), PJSC “Фи-
доБанк” (project «FidoWallet»). Actively
participated in organization and promotion
of several FinTech startups in mPOS,
contactless payments, loyalty programs and
mobile wallet.
Sergey Skabelkin
sskabelkin@gmail.com
+38(099)606-23-36
https://ua.linkedin.com/in/sergeyskabelkin
https://www.facebook.com/SergeySkabelkin