DataScoring: Retail lending is one of the most popular and prioritized businesses in financial industry as well as demanding the most attention. Lending to potentially bad borrowers may substantially harm bank or credit union therefore this process must be addressed systematically by setting up automated and effective borrowers scoring process. This problem is solved by our product: 1. We effectively score borrowers using big data. 2. We retrieve additional statistical data to conduct further communications with existing borrowers. 3. Optimize credit portfolio to minimize payment overdues and defaults. We stack Microsoft technologies in production of the product - .Net, Azure Cloud, C# and CUDA. Our algorithms and models are built upon (1) group of self-learning neuron networks, (2) system of input data normalization and semantic analyzer for text inputs; (3) customer psychological image design; (4) data clustering; (5) vanilla scoring systems.

1. Big Data
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. Team
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. Team
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