In today’s evolving financial landscape, keeping up with regulatory changes is crucial—especially in Machine Learning (ML) models for credit issuance. Financial institutions must ensure comprehensive model traceability, thorough documentation, performance monitoring, and strict controls to prevent misuse of ML capabilities.
Introducing Model Factory 2.0
In today’s evolving financial landscape, keeping up with regulatory changes is crucial—especially in Machine Learning (ML) models for credit issuance. Financial institutions must ensure comprehensive model traceability, thorough documentation, performance monitoring, and strict controls to prevent misuse of ML capabilities.
Key Features of ModelFactory 2.0
Development Module: Integrated Model Development
Leverage a Jupyter Notebook environment for efficient and user-friendly ML model creation in risk modeling.
ModelRegistry: Robust Governance and Version Control
Automatically register models in a centralized repository, ensuring full traceability, version control, and regulatory alignment.
Workflow Automation: Streamlined Processes
Transform models into automated pipelines, reducing manual intervention and enhancing accuracy in risk assessments.
Monitoring Module: Real-Time Performance Tracking
Track ML model performance in real time, monitoring KPIs to detect degradation and anomalies.
Validation Module: Independent Model Validation
Conduct independent ML model validation before deployment, ensuring compliance and performance consistency.
BUSINESS CASE:
Success with Caja Arequipa
CHALLENGE
Caja Arequipa, Peru's leading financial institution, faced challenges complying with new regulations governing risk ML models. Their data science team developed ML models in isolation using traditional tools, making it difficult to control, govern, and trace models and their documentation for timely reporting to regulators.
SOLUTION
Accéder collaborated with Caja Arequipa’s risk data science team to tailor ModelFactory 2.0, achieving:
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Cloud-based ML model development with high computing power in Jupyter hub for remote access.
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Centralized model management for enhanced control and governance.
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Full traceability and streamlined regulatory documentation.
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Comprehensive monitoring to ensure optimal performance and timely updates.
BENEFITS
Caja Arequipa achieved:
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Increased Productivity & Efficiency – Weeks of work reduced to just hours.
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Enhanced Control & Governance – Easily activate, deactivate, and modify model pipelines.
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Improved Performance Monitoring – Direct business impact through better oversight.
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Regulatory Compliance – Prevented penalties, saving thousands of soles.
Why Choose ModelFactory 2.0?
Develop ML Models
in the Cloud:
Facilitating remote access and
high computational power.
Automate Pipeline
Execution:
Streamlining processes and
reducing manual intervention.
Ensure Full
Governance and
Traceability:
Maintaining comprehensive
oversight of ML models.
Achieve Regulatory
Compliance:
With seamless validation and
documentation processes.
Generate Instant
Analysis and
Reporting:
Powered by advanced
technology for timely insights.
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Key Features of ModelFactory 2.0
Development Module: Integrated Model Development
Leverage a Jupyter Notebook environment for efficient and user-friendly ML model creation in risk modeling.
ModelRegistry: Robust Governance and Version Control
Automatically register models in a centralized repository, ensuring full traceability, version control, and regulatory alignment.
Workflow Automation: Streamlined Processes
Transform models into automated pipelines, reducing manual intervention and enhancing accuracy in risk assessments.
Compliance Module: Comprehensive Documentation & Traceability
Ensure full traceability and automated documentation, facilitating seamless regulatory reporting and audits.
Monitoring Module: Real-Time Performance Tracking
Track model performance in real time, quickly identifying anomalies or degradation to maintain accuracy and compliance.
Validation Module: Independent Model Validation
Ensure regulatory compliance with independent validation before deployment, allowing only verified models into production.
Key Features of ModelFactory 2.0
Development Module: Integrated Model Development
Leverage a Jupyter Notebook environment for efficient and user-friendly ML model creation in risk modeling.
ModelRegistry: Robust Governance and Version Control
Automatically register models in a centralized repository, ensuring full traceability, version control, and regulatory alignment.
Workflow Automation: Streamlined Processes
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