Chuqur o‘rganishga asoslangan moliyaviy firibgarlikni aniqlash uchun yondashuvlar

Xayriddin Normamatov

GREEN ECONOMY AND DEVELOPMENT, 2025, vol. 3

Abstract: Moliyaviy firibgarlikni aniqlash bugungi kunda dolzarb va murakkab masalalardan biri bo‘lib, moliyaviyinstitutlar uchun sezilarli darajada moliyaviy yo‘qotishlarga olib kelmoqda. An’anaviy aniqlash usullari ko‘p vaqt va resurstalab qilgani sababli, real vaqt rejimida ishlovchi avtomatlashtirilgan yondashuvlarga ehtiyoj ortmoqda. Ushbu maqoladachuqur o‘rganish asosidagi metodologiya asosida firibgarlikni aniqlash masalasi ko‘rib chiqiladi. Taklif etilgan yondashuvdakonvolyutsion (CNN) va takrorlovchi (LSTM) neyron tarmoqlari asosida kam xususiyatli, ammo yuqori aniqlikdagi modellaryaratiladi. Shuningdek, nomutanosiblik muammosini bartaraf etishda Autoencoder asosidagi ortiqcha namunalash usulitaklif etiladi va SMOTE usuli bilan solishtiriladi. Tajriba natijalari CNN va LSTM modellarining LightGBM modeliga nisbatansamaradorligini ko‘rsatdi. Ikki xil real ma’lumotlar to‘plamida o‘tkazilgan tajribalar natijalari taklif etilgan yondashuvningbarqaror va ishonchli ekanligini tasdiqlaydi

Keywords: chuqur o‘rganish; moliyaviy firibgarlik; anomal yozuvlar; autoencoder; LSTM; Bi-LSTM; GAN; VAE; grafik neyron tarmoqlar; GNN; mashina o‘rganish; firibgarlikni aniqlash; real-vaqt monitoring; moliyaviy tranzaktsiyalar; neyron tarmoqlar. (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:teu:ged000:v:3:y:2025:id:7839

DOI: 10.5281/zenodo.17606443

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