Maclear’s engagement model progresses through three stages: Assess, Build, and Operate. Each service is scoped to fit your institution’s maturity, regulatory environment, and team capacity.
Stage 1
Entry-point services that establish your current state and create a clear, actionable path forward.
A structured review of your institution's data capabilities, analytics maturity, AI readiness, and governance posture — delivered as a prioritized findings report with actionable recommendations.
Evaluate your institution's model inventory, validation practices, and governance controls against regulatory expectations and industry benchmarks.
Assess your institution's readiness for self-service analytics: data literacy, tooling, governance, and change management requirements.
Business-aligned planning, use-case prioritization, and target-state design. Delivered as a roadmap your institution can execute with internal resources or Maclear's continued support.
Tailored workshops and training programs for non-technical teams — building foundational data and AI literacy across risk, compliance, audit, and operations.
Stage 2
Expert-led delivery of working solutions — scoped transparently and built to perform in real, regulated environments.
Data models, warehouse architecture, pipeline development, and orchestration designed for financial data environments — structured for auditability, reliability, and scale.
Predictive models for credit risk, fraud detection, expense anomaly detection, AML, and operational decision support — developed with documentation and validation artifacts built in.
Target-state design, phased implementation, cloud deployment, and user enablement for business analytics. From BI tool selection to production delivery.
AI model evaluation, integration, monitoring, and governance practices for regulated environments. Covers LLM integration, RAG architectures, and AI risk controls.
Design and implementation of model risk policies, validation standards, inventory management, and governance controls aligned to SR 11-7 and OSFI E-23 expectations.
Data ownership, quality standards, lineage tracking, and access controls — implemented pragmatically for institutions without a dedicated data governance team.
Stage 3
Keep solutions reliable, controlled, and useful over time — with ongoing operational and governance support.
Third-party validation of models developed internally or by Maclear — covering methodology, data quality, documentation, and regulatory alignment.
Scheduled performance reviews, drift detection, and stability reporting for production models. Delivered as a recurring engagement with documented findings.
Ongoing updates to model risk policy, data governance standards, and audit-ready documentation as your institution evolves and regulations change.
Maintenance, enhancement, and user support for self-service analytics platforms deployed by Maclear or inherited from prior vendors.
Maclear embeds within your team on a part-time or project basis — filling capability gaps in data science, data engineering, or model risk without a full-time hire.
Full ongoing management of a defined data and AI function — covering delivery, governance, and reporting — for institutions that prefer an outsourced operating model.
Maclear Academy
Purpose-built workshops and training programs that improve your team's ability to use, evaluate, and govern data and AI solutions — delivered by practitioners, not instructors.
Private Client
Select institutions work with Maclear under a private client arrangement — a structured, longer-term engagement that provides direct access to senior expertise across data, ML, and AI.
Get Started
A 30-minute conversation is enough to map your situation to the right starting point. No obligation — just a clear picture of what's possible and what it takes.
Talk to Maclear