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Presentation DataScoring: Big Data and credit score

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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.

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Presentation DataScoring: Big Data and credit score

  1. 1. Big Data and credit scoring
  2. 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. 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. 4. Our clients are any financial organizations that provide crediting services: Who are our clients? Banks Credit Institutions Systems of р2р-crediting
  5. 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. 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. 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. 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

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