Use Cases
Real work. Real outcomes.
The following engagements represent the type of work Maclear delivers. Details have been generalized to protect client confidentiality while preserving the substance of the challenge, approach, and outcomes.
Case 01 — AML Controls
AML Control Assessment — Global Bank
Challenge
A global bank’s AML compliance team faced growing regulatory scrutiny over control effectiveness. Prior assessments had identified surface-level issues, but the bank lacked a systematic view of where its transaction monitoring models were generating noise vs. meaningful alerts, and where structural vulnerabilities existed in its customer risk rating methodology.
Approach
Maclear conducted a comprehensive data quality review of transaction monitoring inputs, performed network analysis to identify entity relationship gaps, and built behavioral profiling to characterize alert populations. A scoring framework was developed to rank control weaknesses by regulatory exposure, and a detailed remediation roadmap was produced with prioritized actions for the compliance and technology teams.
Outcomes
Case 02 — Fraud & Expense Analytics
Expense Anomaly Detection — Government Agency
Challenge
A government agency managing a large procurement portfolio had limited visibility into spending anomalies across divisions. Manual audit sampling was resource-intensive and frequently missed emerging patterns. Leadership needed a systematic approach to detect unusual expense behavior without increasing audit headcount.
Approach
Maclear built division-level spending profiles using historical procurement data, developed clustering models to identify behavioral groups, and applied anomaly scoring to surface outliers for investigative review. The system was designed to be interpretable by analysts without data science training, with explainable flags and transparent scoring logic.
Outcomes
Case 03 — Risk-Based Audit Planning
Intelligent Audit Planning — Major Canadian Bank
Challenge
A major Canadian bank’s internal audit team was planning branch-level audits using a largely subjective, relationship-driven process. This created inconsistency in coverage, exposed the function to criticism from regulators, and left high-risk branches under-audited relative to their risk profile.
Approach
Maclear designed a data-driven risk scoring model incorporating branch-level operational, financial, and complaint data. Feature engineering surfaced leading indicators of audit risk, dimensionality reduction was applied to handle high-cardinality branch attributes, and a geospatial dashboard was built to give audit leadership a visual planning tool with explainable risk scores for each branch.
Outcomes
Case 04 — Data Infrastructure
Data Pipeline Modernization — Financial Institution
Challenge
A financial institution with a legacy ETL architecture was experiencing recurring data quality failures that were propagating into regulatory reports and model inputs. The data engineering team lacked the capacity to redesign the pipeline while maintaining daily operations, and past remediation attempts had created additional technical debt.
Approach
Maclear mapped the existing data lineage to identify root causes of quality failures, designed a phased migration to a modern pipeline architecture with embedded quality controls, and built a data quality monitoring dashboard to enable ongoing detection before issues reached downstream consumers.
Outcomes
Case 05 — Cross-Border AML
AML Analytics for Cross-Border Payments
Challenge
A financial institution processing high volumes of cross-border payments identified weaknesses in its transaction monitoring coverage for cross-jurisdictional layering typologies. Existing rules were generating excessive false positives on low-risk corridors while missing behavioral patterns associated with structured movement across multiple entities.
Approach
Maclear built a graph-based entity network to map payment flows and counterparty relationships across jurisdictions. Typology-specific behavioral features were engineered for layering, structuring, and round-trip patterns. A tiered alert scoring model was developed to stratify alerts by risk level, enabling triage prioritization and reducing investigator time on low-value alerts.
Outcomes
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