Model Development
Register, document, and version models from development through deployment.
Build, deploy, monitor, and govern AI-driven decision systems across acquisition, underwriting, and portfolio management.
Register, document, and version models from development through deployment.
Track performance, detect drift, and generate alerts before problems become incidents.
Enforce validation workflows, tiering frameworks, and audit-ready documentation trails.
Controlled deployment pipelines with traceability from model to production decision.
Typical clients: Banks • Credit unions • Fintech lenders • Financial services companies
SLERA combines business decision applications with model lifecycle governance in a single integrated platform.
Customer acquisition and marketing decisioning. Automate and optimise pre-screening, campaign targeting, and offer generation using governed AI models.
Credit risk decisioning and underwriting support. Integrate risk models, bureau data, and decisioning rules to support automated and augmented underwriting.
Portfolio monitoring and risk strategy optimisation. Track portfolio performance, detect emerging risks, and adjust strategies based on real-time analytics.
Model development and experimentation. Structured environment for developing, testing, and documenting analytical models with full version control and audit trail.
Model inventory and validation workflows. Maintain a complete model inventory, manage validation pipelines, and meet SR 11-7, OCC, and Basel model risk requirements.
Continuous performance monitoring and drift detection. Automated monitoring of model performance, data drift, and population shifts with alerting and reporting.
From Data → Models → Decisions → Governance
Structured and unstructured data from core banking, bureau, and transaction systems.
Machine learning and statistical models trained and validated on that data.
Automated or augmented acquisition, underwriting, and portfolio decisions.
Continuous performance tracking, drift detection, and alert workflows.
Audit-ready documentation, validation workflows, and regulatory evidence.
Traditional analytics stops at models. Decision intelligence connects models to live business decisions, with governance and monitoring built in from the start.
Pre-screening, targeting, and offer generation using AI-driven acquisition models. Improve approval rates and reduce cost per acquired customer across all channels.
AI-augmented application decisioning with full explainability for adverse action compliance. Combine bureau data, behavioural signals, and policy rules in governed decisioning workflows.
Early warning signals, segment analytics, and strategy adjustment capabilities. Detect emerging risk concentrations and behavioural shifts before they become material losses.
Real-time anomaly detection across transaction and behavioural data. Identify unusual patterns and flag high-risk events before they result in financial loss.
Financial institutions operating under SR 11-7, OCC 2011-12, and Basel model risk frameworks are required to maintain documented model inventories, conduct independent validation, and produce audit-ready evidence of model controls. As AI adoption accelerates, regulators are extending these requirements to encompass all models used in credit and risk decisions — including machine learning systems.
AI governance pressure is intensifying across all major markets. The expectation that financial institutions can demonstrate how decisions are made — including the role of automated models — is now baseline regulatory hygiene, not an edge case. Institutions that cannot demonstrate governance of their AI-driven decision systems face increasing supervisory scrutiny.
Managing a portfolio of analytical models across development, validation, deployment, and monitoring is operationally complex. Teams responsible for model risk management face increasing model counts, shorter deployment cycles, and the need to maintain governance standards across models that vary widely in complexity, use case, and regulatory materiality.
Operational decision systems — where models directly drive acquisition, underwriting, or portfolio management outcomes — require a different standard of governance than analytical reporting. SLERA provides the infrastructure to connect data, models, and decisions while maintaining the governance and monitoring standards that regulators and internal risk functions require.
This leads to:
SLERA becomes the system of record for all analytical models used in decisioning.
Compliance with OCC 2011-12, SR 11-7, and Basel model risk guidance through:
Continuous tracking of model performance with automated alerting:
Structured evidence trails enabling faster regulatory response:
SLERA is built in three integrated layers — a foundation of data and AI infrastructure, a model lifecycle and governance layer where models are built, validated, and controlled, and a business decision layer where intelligence drives live credit and risk outcomes.
AI-driven acquisition scoring, marketing response models, and pre-screening decisioning. Optimise offer targeting and approval rates at the top of the funnel.
Application scoring, underwriting policy enforcement, and credit decisioning with full explainability. Compliant with fair lending and adverse action requirements.
Behavioural monitoring, early warning signals, concentration risk, and portfolio stress indicators. Continuous risk visibility across the book.
Structured model development registry with versioning, experiment tracking, documentation capture, and approval workflows from development through production.
Model inventory, tiering, validation scheduling, findings management, and evidence repository aligned to OCC 2011-12, SR 11-7, and Basel model risk frameworks.
Continuous tracking of KS, AUC, and PSI metrics with automated drift detection, performance alerts, and monitoring evidence for ongoing regulatory compliance.
Responsible for model governance, regulatory compliance, and validation oversight.
Develop and deploy models with structured documentation and deployment workflows.
Conduct independent validation with structured workflows, findings, and evidence capture.
Ensure regulatory adherence across the model portfolio with clear evidence and reporting.
SLERA adapts to your infrastructure. Whether your organisation operates in a public cloud environment, a private cloud, or requires on-premise deployment, SLERA can be configured to meet your security and data residency requirements.
Reduced regulatory risk and faster regulatory response
Faster, more controlled model deployment cycles
Improved model transparency across the portfolio
Automated governance workflows replacing manual processes
Reduced manual reporting overhead for risk teams
Single system of record for all analytical decision models
SLERA becomes the operating system for analytical decision systems in your financial institution.
See how SLERA connects data, AI models, and governed decisions across acquisition, underwriting, and portfolio management for financial institutions.
See SLERA in action and discuss your requirements with our team.