About Maclear
Maclear was built on a straightforward premise: smaller financial institutions deserve the same quality of data and AI capability as larger ones — practical, defensible, and fit for real operating environments.
The Problem We Solve
Small and mid-sized financial institutions face growing pressure to use data, analytics, and AI to manage risk, improve efficiency, and meet regulatory expectations. The challenge is not only technical.
System integration, cloud infrastructure, model design and validation, governance and compliance, adoption and change management — these problems compound each other. Without a structured, right-sized path from assessment to implementation and sustained support, initiatives stall, introduce unintended risk, or fail to deliver lasting value.
Most available solutions are built for enterprises with large internal teams and significant infrastructure budgets. Smaller institutions are left adapting tools, methodologies, and frameworks that were never designed for their scale, operating model, or regulatory environment.
Maclear was founded to close that gap — with practical execution, not abstract strategy, and with substance over hype at every step.
Our Direction
Maclear helps small and mid-sized financial institutions use data, machine learning, and AI to achieve measurable business results — by aligning business objectives with proven technical solutions that improve efficiency, manage risk, and enable teams through transparent delivery and long-term partnership.
Advanced data and AI capabilities should be accessible based on institutional needs and complexity — not the size of a balance sheet. Smaller and mid-sized financial institutions should be able to compete effectively through responsible, proven, and scalable data solutions built for their operating reality.
What We Believe
The following positions shape how Maclear approaches every engagement.
The right solution is the one that works in your environment, with your data, your team, and your regulatory constraints — not the most technically interesting one. Fit-for-purpose beats impressive-on-paper every time.
Model validation, data lineage, and AI oversight aren't compliance box-ticking — they are the mechanisms that make data and AI trustworthy and sustainable in regulated institutions. They belong in the design from day one.
A model that isn't used, a dashboard that isn't trusted, or a pipeline that only one person understands — these are failures, regardless of technical quality. Enablement and change management are core delivery responsibilities, not afterthoughts.
Solutions scaled to your institution's actual complexity, operating model, and risk appetite are better solutions — more maintainable, more defensible, and more likely to deliver lasting value. Complexity that isn't justified is a liability.
Clients deserve complete scope, honest estimates, and direct communication — especially when something is harder than expected or when a different approach would serve them better. Trust earned through transparency is more durable than trust earned through polish.
The most valuable engagements are those where Maclear understands your institution deeply enough to flag risks before they materialize and adapt as your priorities evolve — not ones that end at the handoff meeting.
How We Work
Maclear's values are not aspirational statements — they describe specific behaviors and commitments that shape how every engagement is delivered.
Why Maclear
Maclear does not adapt enterprise frameworks for smaller clients — our methods, scoping, and delivery are designed from the ground up for institutions that need right-sized solutions, not scaled-down versions of something else.
Model risk management, data governance, and AI oversight are part of the engagement design — not separate workstreams added after the fact. This reduces downstream regulatory and operational risk.
Maclear does not introduce complexity that isn't justified by business need. Right-sized solutions require disciplined scope decisions — and the credibility to make them rather than maximizing engagement scope.
Clients work directly with senior expertise across quantitative finance, machine learning, data engineering, model risk, and regulatory interpretation — not layers of account management and junior delivery staff.
Scope, pricing, constraints, and realistic expectations are established upfront. There are no scope surprises, no hidden escalation paths, and no cost ambiguity midway through an engagement.
Solutions are designed to be understood and used by your team — with documentation, training, and governance built into every engagement rather than treated as optional add-ons.
Leadership
Jesus Calderon founded Maclear Data Solutions to bring practical, rigorous data and AI capabilities directly to the financial institutions that need them most — those operating without large internal analytics teams, navigating complex regulatory environments, and making significant decisions under data and governance constraints.
His expertise spans quantitative finance, machine learning, data engineering, model risk management, AML analytics, and independent validation — built through direct delivery across banks, government agencies, credit unions, and regulated financial institutions in North America.
Clients who work with Maclear work with someone who understands both the technical detail and the institutional realities of regulated financial services — not a team that has to translate between the two.
Schedule a conversationMaclear has delivered work for major Canadian banks, large government agencies, and financial institutions across banking, insurance, and fintech sectors.
Engagement Model
Maclear's staged model is designed to meet institutions where they are and move them forward without unnecessary complexity or premature investment.
Entry-point services for institutions exploring their options: self-assessments, maturity diagnostics, strategy development, and education. Accessible, structured, and designed to establish clarity before committing to larger investments.
Explore Assess services →Expert-led delivery of working solutions: data engineering, model development, analytics platforms, AI engineering, and governance design. Every engagement scoped transparently and delivered with measurable outcomes in mind.
Explore Build services →Ongoing support that keeps solutions effective and controlled: model monitoring, pipeline maintenance, AI oversight, and flexible co-sourced or outsourced Data & AI team arrangements scaled to your operating needs.
Explore Operate services →Get Started
Whether you know exactly what you need or are still defining the problem, Maclear can help you find the right starting point. Transparent scope. No unnecessary overhead. Real outcomes.