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CyberTech

AI & Machine Learning for credit evaluation

CyberTech AI Scoring

CyberTech AI Scoring applies AI and Machine Learning to evaluate credit, optimize the approval process and reduce risk for financial institutions.

Finance & Digital Identity
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Key features

  • Multi-source data integration

    AI consolidates data from telecom, social media, payment systems, bank transactions and ERP to build a comprehensive scoring model.

  • AI-powered risk management

    Machine learning models detect fraud and automate credit approval; the legacy page states a 40% reduction in credit risk.

  • Smart forecasting models

    Predictive analytics and risk assessment; the legacy page states banks can increase revenue by 15% and optimize performance by up to 40%.

  • API / Web SDK integration

    Integrates seamlessly with banking and financial systems for real-time credit assessment and decisions.

Market demand and challenges

  • Traditional credit scoring relies mainly on repayment history, credit usage frequency and new-loan data, which limits predictive power
  • Existing models do not fully use extensive data such as demographics, income levels and tax records
  • They struggle to predict credit risk accurately and cannot personalize financial products for customer segments
Mô hình hệ thống AI Scoring: Dữ liệu → Mô hình → Xếp hạng chung
AI Scoring system model: Data → Model → General class

Technology and applications

  • Big Data analytics with MongoDB: AI processes financial reports and recommends optimal credit products
  • NLP and sentiment analysis: analyzes customer information from multiple sources to improve risk evaluation
  • API/Web SDK integration with banking and financial systems

System model

The diagram on the page has three layers: Data (basic customer information, credit information, credit quality, transaction data, customer behavior, account history, financial information, relationship and shopping-behavior analysis) → Model (7 decision models, depending on each business’s needs) → General class (a score suited to each product), with the output being a credit score for unsecured loan/BNPL products. The diagram cites: Designing next-generation credit-decisioning models – McKinsey.

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