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Juris Mindcraft

by Juris Technologies · Since 2003
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ActiveAvailable globallyCloud
Quick facts
VendorJuris Technologies
Year launched2003
StatusActive
LocationLevel 25, Tower B Vertical, Bangsar South, No. 8, Jalan Kerinchi, Kuala Lumpur, 59200, MY
Countries servedGlobal
Languages1
IntegrationsN/A
Free tierNO
Free trialNO
Contact salesYES
Last reviewed: May 2026

About Juris Mindcraft

Juris Mindcraft is an exceptional, enterprise-grade solution in the Financial AutoML and Prescriptive Risk Analytics marketplace.

Juris Mindcraft is an exceptional, enterprise-grade solution in the Financial AutoML and Prescriptive Risk Analytics marketplace. It completely excels at taking complex, messy transactional data alongside non-traditional alternative indicators and transforming them into highly accurate credit scores, early default warning signs, and transparent automated decisions. The platform's standout competitive advantage is its continuous self-learning loop combined with an accessible drag-and-drop interface—giving standard business experts the power to deploy sophisticated machine learning models that hit up to 96% accuracy in flagging non-performing loans six months ahead of time.

Pros & Cons

Pros
  • Incorporating alternative web and mobile data streams opens up safe lending options for credit-invisible consumers.
  • Generating highly transparent decision paths keeps models safe, compliant, and ready for internal corporate or external bank audits
  • Delivers exceptional real-world accuracy, achieving up to a 96% accuracy rate in early NPL detection during long-term bank deployments
Cons
  • Launching the workspace requires passing through a customized B2B enterprise review and configuration pipeline.
  • The underlying models still require a baseline volume of structured historical inputs to yield high-accuracy outputs.
  • Finding baseline costs requires scheduling a formal consultation, making it tough to evaluate upfront subscription fees.

Features

Key features

Automated Machine Learning

Uses autoML techniques to build powerful AI models for predictive analytics.

Explainable AI

Provides explainable models and decisions to help users understand and interpret predictions.

RESTful Service Deployment

Deploys AI models as RESTful services to eliminate discrepancies between programming languages.

Continuous Learning

Automatically maintains production models at peak performance by updating them with new data.

AIOps Operationalization

Focuses on operationalizing the full lifecycle management of all AI and decision models.

Additional features

Data Pre-processing

Includes feature engineering, selection, transformation, encoding, and missing value handling.

Model Stacking

Uses multiple algorithms to create a robust supermodel that outperforms single best-performing models.

Performance Monitoring

Tracks model performance over time and detects declines to trigger automatic updates.

Supervised Learning

Supports classification and regression problems for predictive modeling.

Business Value Analysis

Provides evaluation metrics, plots, and charts for model comparison and business impact analysis.

Champion Challenger Testing

Allows for comparing different models to ensure optimal performance.

Scalable Infrastructure

Designed to train and build machine learning models at scale.

REST API Integration

Enables easy deployment and testing of AI models within production systems.

Pricing

Free trial
Free version
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Countries & Languages

Global
Countries served
1
Interface languages
3
Billing currencies

Interface languages

English

Billing currencies

🇺🇸USD🇪🇺EUR🇬🇧GBP

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