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Selected Engagements

Real outcomes from real engagements.

The following cases illustrate how Maclear has helped financial institutions improve control, reduce manual effort, and build practical data and AI capabilities — with measurable results.

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Selected Engagements

Case Studies

How a Global Bank Uncovered Structural Weaknesses in AML Controls

Anti-Money LaunderingModel RiskData QualityBanks · Fintechs

A global financial institution needed an independent, data-driven assessment of its AML control effectiveness. Legacy rule-based systems were generating excessive false positives, with limited visibility into performance gaps and no structured evaluation process. Maclear performed a comprehensive control review to surface structural weaknesses and provide a defensible, evidence-backed remediation roadmap.

Challenge
Static AML rules producing high false positive rates, limited monitoring of system effectiveness, and growing regulatory scrutiny around transaction monitoring coverage and typology alignment.
Approach
Data quality review of transaction monitoring inputs; network analysis to identify behavioral clusters; behavioral profiling of account activity patterns; rule logic testing against known typologies; fuzzy matching to surface potential evasion patterns.
Outcomes
Complete operational view of AML control performance; evidence-backed audit findings with documented data lineage; prioritized remediation roadmap aligned to regulatory expectations; reduced ambiguity for compliance and risk teams.
Services Used
Data & AI Strategy · ML Model Development · Model Risk Management · Data Engineering

How a Large Government Agency Uncovered Hidden Spending Leaks

Expense AnalyticsAnomaly DetectionSelf-Service AnalyticsInsurers · Banks · Fintechs

A large government agency was facing high-friction manual review processes, inconsistent anomaly detection, and limited spending visibility across divisions. Business analysts lacked direct data access, and cost-saving opportunities were being missed due to the volume and complexity of transactional data. Maclear designed and delivered an analytics solution that automated detection and gave teams direct access to insights.

Challenge
Fragmented expense data across divisions, no consistent anomaly detection methodology, high manual review burden, and absence of self-service reporting for business stakeholders.
Approach
Data consolidation and pipeline development; division-level spending profile construction; clustering analysis to establish behavioral baselines; anomaly scoring model for transactions and vendors; automated alert logic; dashboard and reporting layer; team enablement and documentation.
Outcomes
Targeted cost-saving opportunities identified; faster anomaly detection with consistent methodology; freed analyst capacity previously spent on manual review; transparent spending oversight for leadership; vendor billing irregularities surfaced and escalated.
Services Used
Data Engineering · ML Model Development · Self-Service Analytics · Education & Enablement

Intelligent Risk-Based Planning for Retail Branch Audits at a Major Canadian Bank

Audit AnalyticsRisk ScoringFeature EngineeringBanks · Credit Unions

A major Canadian bank needed a more rigorous and defensible method for targeting retail branch audits. Existing selection relied on subjective judgment, making it difficult to demonstrate risk coverage to oversight functions. Maclear designed a data-driven audit prioritization model using multiple risk dimensions and delivered it as an interactive tool for audit planning teams.

Challenge
Audit targeting based on incomplete or inconsistent criteria, limited ability to demonstrate systematic risk coverage, and no structured tool for annual planning or cross-functional coordination.
Approach
Feature engineering across financial, operational, and human capital dimensions; dimensionality reduction to construct three composite risk indicators; geospatial mapping of branch risk profiles; interactive web-based planning tool for audit team self-service; documentation and training.
Outcomes
Data-backed audit targeting with transparent methodology; improved risk coverage documentation for oversight functions; efficient annual audit planning process; geospatial dashboard enabling cross-regional coordination; self-service access for audit leadership.
Services Used
ML Model Development · Self-Service Analytics · Data Engineering

Modern Data Ingestion Pipeline for Legacy Banking Systems

Data EngineeringPipeline ModernizationData GovernanceBanks · Credit Unions

A financial institution operating across multiple legacy systems was struggling with manual data reconciliation, inconsistent reporting outputs, and fragile spreadsheet-based workflows. Teams had lost confidence in the accuracy of operational data. Maclear designed and built a modern, product-centric data ingestion architecture to unify fragmented sources and restore data trust.

Challenge
Disconnected databases, manual reconciliation across spreadsheets, lost confidence in reporting accuracy, no documented data lineage, and a fragile dependency on individual team members' tribal knowledge.
Approach
Structured discovery and assessment of data sources and quality issues; product-centric pipeline architecture design; user-driven validation rule framework; phased implementation using modern data stack tooling; automated data quality controls and governance documentation; analyst enablement sessions.
Outcomes
Eliminated manual reconciliation for key reporting workflows; reduced data errors and reporting delays; documented data lineage for audit and regulatory purposes; established foundation for self-service analytics; automated data governance controls with user-configurable validation rules.
Services Used
Data Engineering · Data Governance Implementation · Analytics Platform Support

Modernizing AML Controls for Cross-Border Payments

AMLCross-Border PaymentsModel ImprovementBanks · Fintechs

A financial institution operating cross-border payment corridors was using static rule-based AML controls that produced excessive false positives and limited coverage of emerging risk typologies. There was no formal performance evaluation process and growing uncertainty about regulatory alignment. Maclear delivered a two-phase assessment and roadmap engagement to establish a defensible improvement path.

Challenge
Rule-based system generating high false positive volumes, limited visibility into emerging risk typologies, no model performance evaluation framework, and regulatory examination pressure around transaction monitoring program effectiveness.
Approach
Phase 1: rule logic analysis, false positive root cause review, gap identification against current regulatory typologies and SWIFT-level risk profiles, internal alignment facilitation. Phase 2: improvement roadmap design with sequenced priorities, governance framework draft, and documentation aligned to regulatory expectations.
Outcomes
Documented system performance baseline with quantified false positive rates; prioritized improvement roadmap with sequenced, defensible next steps; reduced regulatory ambiguity and improved internal alignment; draft governance framework for ongoing monitoring; foundation for Phase 2 model development engagement.
Services Used
Data & AI Strategy · Model Risk Self-Assessment · Model Risk Management · Governance Framework Maintenance

By Industry

What Maclear delivers for your institution

Banks

Practical data and AI capabilities that modernize risk management, improve AML controls, streamline reporting infrastructure, and support audit readiness — without requiring a large internal analytics team.

AML analytics · Risk-based audit planning · Data pipeline modernization · Model risk management

Credit Unions

Improve operational efficiency, cost visibility, and decision quality through right-sized analytics solutions that fit your governance structure, member service model, and regulatory obligations.

Self-service analytics adoption · Data infrastructure modernization · Expense monitoring

Insurers

Support underwriting accuracy, pricing model governance, and claims analytics with structured, validation-forward approaches designed for actuarial rigor and regulatory defensibility.

Quantitative model development · Model validation · Expense anomaly detection · Data governance

Fintechs

Build durable data and AI foundations that scale with your business — managing technical debt, governance obligations, and model risk without slowing product velocity or accumulating regulatory exposure.

AML analytics · AI engineering · Data engineering · Self-service analytics

Regulatory Bodies

Strengthen supervisory data capabilities, improve analytical consistency across examinations, and build governance-forward platforms that support better risk-based oversight decisions.

Data platform development · Analytical tool design · Risk-based planning models

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Facing a similar challenge?

Most data and AI problems in financial institutions follow recognizable patterns. Maclear can help you identify the right approach for your specific situation, scale, and regulatory context.