Why Weak Link in Credit Conveyor Could Be Not Only Rate but Also Bad OCR
Smart Engines
Smart Engines argues that document recognition is a key element in speeding up credit processing. Poorly organized manual data entry causes delays, overload, and errors. Their Smart Document Engine automates the process, enabling a high-performance credit conveyor, as demonstrated by the Abanking case.
Smart Engines highlights that the most time-consuming stage in credit processing is not scoring but manual data entry from various documents. Clients bring a stack of documents: passports, SNILS, INN, financial statements, and more. Manual input leads to long decision times, overloaded specialists, and errors. To solve this, Smart Engines offers its Smart Document Engine, which automatically classifies and recognizes documents from photos even in real-world conditions, handles 80+ registration and financial forms, and 40+ identity documents, all on-premises without cloud data transfer. The system provides confidence scores for each recognized character, ensuring explainability, and validates document forms and dates. In the Abanking case, the engine processed document packages in fractions of a second, increasing throughput and allowing analysts to focus on risk assessment, while automatic scoring recalculated credit scores instantly.
- Abbreviations
- OCR = Optical Character Recognition — оптическое распознавание символов
- GPU = Graphics Processing Unit — графический процессор
- ERP = Enterprise Resource Planning — планирование ресурсов предприятия
- ECM = Enterprise Content Management — управление корпоративным контентом
- CRM = Customer Relationship Management — управление взаимоотношениями с клиентами
- ABS = Automated Banking System — автоматизированная банковская система
Source: Habr — хаб ИИ —
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