Algolytics Technologies Documentation
  • End-to-end Data Science Platform
  • ABM
    • Introduction to ABM
    • Repository
    • Classification - adding, running and deleting projects
    • Approximation - adding, running and deleting projects
    • Models and variables statistics
    • Model deployment
    • ABM API
    • Data scoring
    • Adding, running and deleting projects
  • Event Engine [user]
    • Engine description
    • How the engine works
    • Events
    • Aggregate module
    • Metadata
    • Components of metadata
    • Off-line processing and modeling
    • Examples of API operations
    • Visualisation
  • Event Engine [administrator]
  • Scoring.One
    • Engine description
    • Panels overview
    • Implementation of scoring models
    • Creating and testing a scenario
    • SCE Tracking Script
  • Advanced Miner
    • Documentation
    • How to install license key
  • DataQuality [web app]
  • Algolytics APIs
    • DQ for Python API
    • Scoring Engine WEB API
    • ABM Restfull API
    • Other APIs
  • Privacy policy
  • GDPR
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  • Architecture
  • Functionalities
  1. Scoring.One

Engine description

Architecture

Scoring Engine is a software for real-time scoring and rating of customers using various business rules or models. The software works with data obtained from external systems (e.g. CRM, BPM, activation/debt collection system). It automatically validates data and calculates scores or ratings for credit risk, churn prediction, fraud detection, marketing activities, etc. Its architecture allows a low cost and a short time implementation (2-4 weeks) with a very high performance (over 1 000 requests per second per node). Scoring Engine ensures adequate reliability and scalability, which is very important when the amount of data collected and processed grows extremely fast and competition in the market is becoming more and more demanding. Low cost, as well as repeatable deployment process and its automation, enable the implementation of the Scoring Engine even in small and mid-sized companies.

Functionalities

  • defining various processes of verification and data evaluation

  • implementation of expert rules, scoring and rating models for risk assessment

  • application of cross-checking mechanisms to check and compare data

  • defining expert rules at various stages of decision-making process

  • remote execution of assessment through various communication methods (e.g. web services)

  • saving all information about results and processed data

  • report parameterization

  • use of WWW interface to enter and evaluate applications

  • integration with any system operating decision-making processes and loan applications

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Last updated 6 months ago