МОДЕЛИ КРЕДИТНОГО СКОРИНГА НА ОСНОВЕ БОЛЬШИХ ДАННЫХ: ВЛИЯНИЕ НА РОСТ ВЫРУЧКИ ФИНТЕХ-КОМПАНИЙ

Адилов Рустам

студент бакалавриата, кафедра компьютерных наук

Университет ИНХА в Ташкенте

ORCID: 0009-0004-7250-4627

Email: rstmadylov@gmail.com

Аннотация. В статье анализируется влияние моделей кредитного скоринга на основе больших данных и машинного обучения на выручку финтех-компаний. Автор рассматривает механизмы роста дохода через уровень одобрения, уровень дефолта и операционные издержки, а также сопоставляет типы скоринговых моделей по их экономической отдаче.

Ключевые слова: кредитный скоринг, большие данные, машинное обучение, финтех, рост выручки, уровень дефолта, уровень одобрения, альтернативные данные, финансовая инклюзия, кредитный риск, операционные издержки.

KATTA MA’LUMOTLARGA ASOSLANGAN KREDIT SKORINGI MODELLARI: FINTEX KOMPANIYALARI DAROMADINI OSHIRISHDAGI TA’SIRI

Adilov Rustam

Bakalavriat talabasi, Kompyuter fanlari kafedrasi

Toshkentdagi INHA universiteti

ORCID: 0009-0004-7250-4627

     Email: rstmadylov@gmail.com

Annotatsiya. Maqolada katta ma’lumotlar va mashinali o‘qitish asosidagi kredit skoringi modellarining fintex kompaniyalari daromadiga ta’siri tahlil qilinadi. Muallif tasdiqlash darajasi, defolt darajasi va operatsion xarajatlar orqali daromad o‘sishi mexanizmlarini ko‘rib chiqadi hamda skoring modellari turlarini qiyoslaydi.

Kalit so‘zlar: kredit skoringi, katta ma’lumotlar, mashinali o‘qitish, fintex, daromad o‘sishi, defolt darajasi, tasdiqlash darajasi, muqobil ma’lumotlar, moliyaviy inklyuziya, kredit riski, operatsion xarajatlar.

BIG DATA-DRIVEN CREDIT SCORING MODELS: IMPACT ON FINTECH COMPANY REVENUE GROWTH

Adilov Rustam

Undergraduate Student, Department of Computer Science

INHA University in Tashkent

ORCID: 0009-0004-7250-4627

Email: rstmadylov@gmail.com

Abstract. This article analyzes the impact of Big Data- and machine-learning-based credit scoring models on the revenue of fintech companies. The author examines revenue-growth mechanisms through approval rates, default rates and operating costs, and compares scoring model types by their economic returns.

Keywords: credit scoring, big data, machine learning, fintech, revenue growth, default rate, approval rate, alternative data, financial inclusion, credit risk, operating costs.

Литературы

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