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Smart Risk Management

Digital Bank | Digital Credit
Solution Overview

Based on the design concept of online, process oriented, and visualization, technology, data, and scenarios are combined, and models and views are integrated to achieve online automated evaluation, intelligent approval, intelligent warning, intelligent collection, intelligent analysis.

Business Challenges

  • Insufficient application of data mining

    Internal data is scattered, insufficient sedimentation, difficult to mine, and insufficient application.

  • Unclear information visualization

    Insufficient information integration, lack of intuitive visualization, and incomplete customer persona.

  • Manual approvals rely on experience

    Poor customer experience due to manual approvals that mainly rely on experience and with strong subjectivity

  • Passive and lagging risk monitoring

    Proactiveness and timeliness are difficult to improve due to risk monitoring mainly relies on manual labor.

Product Solutions

Comprehensive covering, real-time monitoring, and intelligent recognition,which help to build a full lifecycle and full chain of digital risk control.

Product Strengths

  • Scenario-based risk control

    The scenario-based risk control system consists of 8 categories and 30 + scenario models.

  • Refinement of Risk Control

    A four-step method of diagnosis, positioning, optimisation and monitoring refines risk control and increases the approval rate by 15%.

  • Intelligent Risk Control

    Fully automated online risk monitoring and close-loop management of monitoring targets based on 5000+ data labels, achieving 90% early warning accuracy rate.

Customer Cases

  • Assisting a national joint-stock bank to establish a digital risk early warning system

    OneConnect integrates 80 + label systems inside and outside the industry, and monitors the situation of enterprises in real time all day long with big data.

    OneConnect integrates 80+ label systems inside and outside the industry, and monitors the situation of enterprises in real time all day long with big data.

    FNN machine learning model with 2300 model variables and 180+ significant variables creates 600+ early warning rules to make an all-round risk early warning system.

    Combined with post-loan early warning, inspection and collection, the red flag of large post-loan closed-loop management is pushed in real time. The system is also supported by various AI tools to conduct mobile end due diligence check, automatic post-loan report generation, and automatic grouping collection.

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  • Solution Overview
  • Business Challenges
  • Product Solutions
  • Product Strengths
  • Customer Cases

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