About Maclear
Substance over scale. Precision over hype.
Maclear Data Solutions was founded on a simple observation: smaller financial institutions face the same data and AI challenges as large ones — but rarely have access to the same caliber of specialized help. We exist to change that.
Founder
Jesus Calderon
Founder & Principal Advisor
Jesus Calderon brings over a decade of experience in data science, machine learning, and quantitative risk management across financial services — spanning banks, insurers, credit unions, and regulatory bodies in Canada and internationally.
He has led engagements in AML model design and validation, credit risk modeling, AI governance framework development, and data infrastructure modernization. His work is characterized by a commitment to analytical rigor, regulatory alignment, and solutions that work in practice — not just on paper.
Jesus founded Maclear because he saw a consistent gap: institutions that lacked the internal depth to evaluate, build, or govern data and AI systems properly, and a market that wasn’t serving them at the right scale or price point. Maclear is his answer to that gap.
Background
Mission
Make high-quality data and AI work accessible to the institutions that need it most
Smaller and mid-sized financial institutions deserve access to the same quality of data science and AI advisory as large enterprises — scaled to their reality and delivered without unnecessary overhead.
Vision
A financial system where data and AI are assets, not liabilities
A future where financial institutions of every size can harness data and AI confidently — governed well, deployed responsibly, and aligned with the trust their clients place in them.
Our Values
Point of View
What we believe about data and AI in financial services
These aren’t talking points. They’re the convictions that shape how we design engagements, recommend solutions, and push back when something isn’t right.
Governance is not optional
AI governance isn’t bureaucracy — it’s the infrastructure that makes AI safe to use. Institutions that treat it as an afterthought face compounding risk as adoption scales.
Most institutions don’t need more data — they need better data
Collecting more data rarely solves the underlying problem. Cleaner pipelines, tighter definitions, and disciplined data management almost always unlock more value than new data sources.
Small and mid-sized institutions are underserved
Enterprise AI products are built for enterprise-scale problems and budgets. Smaller institutions need advisors who understand their constraints — and build accordingly.
Explainability is a feature, not a constraint
A model that stakeholders and regulators can’t understand is a model they won’t trust. Explainability enables better decisions, better governance, and more durable adoption.
Vendor independence matters
Maclear has no commercial relationships with technology vendors. Our recommendations are based solely on fit for your institution — not on software partnerships or referral arrangements.
AI hype has a cost
Institutions that chase the newest capabilities without the right foundations end up with technical debt, governance gaps, and failed deployments. Sound foundations first — then capability expansion.
Work with Maclear
Ready to see if we’re the right fit?
Every Maclear engagement starts with a conversation. Book a 30-minute call to discuss your situation, and we’ll tell you honestly whether and how we can help.