{"id":216952,"date":"2026-05-04T11:49:26","date_gmt":"2026-05-04T15:49:26","guid":{"rendered":"https:\/\/www.hajim.rochester.edu\/senior-design-day\/?p=216952"},"modified":"2026-05-04T11:49:26","modified_gmt":"2026-05-04T15:49:26","slug":"rel8ed","status":"publish","type":"post","link":"https:\/\/www.hajim.rochester.edu\/senior-design-day\/rel8ed\/","title":{"rendered":"Rel8ed"},"content":{"rendered":"\n
\n

Corporate Linkage Detection<\/h2>\n\n\n\n

\u201cA Three-Stage Stacked Machine Learning Pipeline for Detecting and Classifying Company Ownership Relationships.\u201d <\/p>\n\n\n\n

Group 6 \u00b7 Rel8ed Alvin Yao \u00b7 Grayson Gong \u00b7 Alex Li \u00b7 Frank Peng University of Rochester \u00b7 In collaboration with Rel8ed.to<\/p>\n<\/div><\/div><\/div><\/div>\n\n\n\n

\n
Meet the Team<\/a><\/div>\n\n\n\n
Data Overview<\/a><\/div>\n\n\n\n
Methodology<\/a><\/div>\n\n\n\n
Results<\/a><\/div>\n\n\n\n
Challenges\/Next Steps<\/a><\/div>\n<\/div>\n\n\n\n

Meet the Team<\/h2>\n\n\n\n
\n
\n

Alvin Yao<\/p>\n\n\n\n

Model Training & Evaluation<\/p>\n\n\n\n

Developed performance diagnostics and visualized stage-by-stage model behavior. Authored the next-steps and challenge discussion.<\/p>\n<\/div>\n\n\n\n

\n

Grayson Gong<\/p>\n\n\n\n

Pipeline Architect<\/p>\n\n\n\n

Designed the three-stage hierarchical classifier, engineered pairwise relational features, and built the controlled negative-sampling scheme.<\/p>\n<\/div>\n\n\n\n

\n

Alex Li<\/p>\n\n\n\n

Error Analysis Lead<\/p>\n\n\n\n

Diagnosed the binary-stage bottleneck, identified the most-confused class pairs, and translated the confusion matrix into actionable insights.<\/p>\n<\/div>\n\n\n\n

\n

Frank Peng<\/p>\n\n\n\n

Data Engineering<\/p>\n\n\n\n

Improved the preprocessing pipeline, designed the overall system architecture, and ensured consistent train\/validation\/test partitioning.<\/p>\n<\/div>\n<\/div>\n\n\n\n

Vision & Goals<\/h3>\n\n\n\n

Recent suspension of U.S. beneficial-ownership enforcement has weakened centralized transparency, leaving fragmented public registries as the main lens into hidden corporate structures used for tax evasion, money laundering, and shell companies.<\/p>\n\n\n\n

    \n
  • Detect relationships<\/li>\n\n\n\n
  • Classify relationship type<\/li>\n\n\n\n
  • Refine hierarchy depth<\/li>\n<\/ul>\n\n\n\n
    \n\n\n\n

    Data Overview<\/h2>\n\n\n\n

    The GLEIF LEI Dataset<\/h3>\n\n\n\n
    \"\"<\/figure>\n\n\n\n

    EDA \u00b7 Missing Data Profile<\/h3>\n\n\n\n
    \"\"<\/figure>\n\n\n\n

    Our dataset came in with 100 raw columns<\/strong>, but the missing data was extremely skewed.<\/p>\n\n\n\n

    73 columns had over 90% missing values<\/strong> \u2014 54 of them were XML-derived fields that were basically empty (99\u2013100% missing), and another 19 were optional regulatory attributes. We dropped all 73 noisy columns before modeling to keep things clean.<\/p>\n\n\n\n

    For the remaining features we used a simple, effective imputation strategy:<\/p>\n\n\n\n

      \n
    • Categorical variables<\/strong> \u2192 filled with \u201cUNK\u201d (preserves the \u201cmissing\u201d signal without inventing fake relationships)<\/li>\n\n\n\n
    • Numerical variables<\/strong> \u2192 filled with 0 or the median, depending on context<\/li>\n<\/ul>\n\n\n\n

      EDA \u00b7 Geographic Distribution<\/h3>\n\n\n\n
      \n
      \n
      \"\"<\/figure>\n\n\n\n

      Takeaway: geography alone carries real signal \u2014 same-country pairs are far more likely to be linked.<\/strong><\/p>\n<\/div>\n\n\n\n

      \n
        \n
      • Financial hub concentration<\/li>\n<\/ul>\n\n\n\n
          \n
        • Long tail of jurisdictions<\/li>\n<\/ul>\n\n\n\n
            \n
          • Offshore signals<\/li>\n<\/ul>\n<\/div>\n<\/div>\n\n\n\n

            EDA \u00b7 Relationship Types<\/h3>\n\n\n\n
            \n
            \n
            CONSOLIDATED 46.3%<\/summary>\n

            Directly + Ultimately
            Consolidated By<\/p>\n<\/details>\n\n\n\n

            INDIVIDUAL 53.4%<\/summary>\n

            Fund Managed By,
            Subfund Of<\/p>\n<\/details>\n\n\n\n

            OTHER_RARE. 0.3%<\/summary>\n

            Feeder To, International
            Branch Of<\/p>\n<\/details>\n<\/div>\n\n\n\n

            \n
            \"\"<\/figure>\n\n\n\n

            Severe class imbalance: IS_FUND-MANAGED_BY alone is 41.5% of edges, while IS_FEEDER_TO and IS_INTERNATIONAL_BRANCH_OF together account for just 0.3% \u2014 this drives our two-tier design.<\/p>\n<\/div>\n<\/div>\n\n\n\n

            EDA \u00b7 Network Structure<\/h3>\n\n\n\n
            \n
            \n
            \"\"<\/figure>\n<\/div>\n\n\n\n
            \n
            \"\"<\/figure>\n<\/div>\n<\/div>\n\n\n\n
            \n
            \n
            \"\"<\/figure>\n<\/div>\n\n\n\n
            \n

            \u2022 Power-law distribution \u2014 few hubs, many leaves<\/p>\n\n\n\n

            \u2022 ~72% domestic relationships, 28% cross-border<\/p>\n\n\n\n

            \u2022 Luxembourg acts as a major international intermediary for fund structures<\/p>\n<\/div>\n<\/div>\n\n\n\n

            \n\n\n\n

            Methodology<\/h2>\n\n\n\n

            System Architecture<\/h3>\n\n\n\n
            \"\"<\/figure>\n\n\n\n
            \n
            \n

            Stage 1: Binary Detection<\/p>\n\n\n\n

            Logistic Regression predicts whether any relationship exists between a company pair. If NO \u2192 pipeline stops.<\/p>\n<\/div>\n\n\n\n

            \n

            XGBoost assigns a coarse category (CONSOLIDATED \/ INDIVIDUAL \/ OTHER_RARE) to all positive pairs.<\/p>\n\n\n\n

            Stage 2: Coarse Type Classifier<\/p>\n<\/div>\n\n\n\n

            \n

            Stage 3: Direct vs Ultimate<\/p>\n\n\n\n

            Specialist XGBoost uses hierarchy-graph features to separate direct from ultimate consolidation.<\/p>\n<\/div>\n<\/div>\n\n\n\n

            Data Preparation & Train \/ Test Split<\/h3>\n\n\n\n
            \n
            \n
            PREPROCESSING PIPELINE<\/h5>\n\n\n\n
            \"\"<\/figure>\n<\/div>\n\n\n\n
            \n
            \"\"<\/figure>\n<\/div>\n<\/div>\n\n\n\n

            Feature Engineering<\/h3>\n\n\n\n

            Engineered 17 pairwise features combining company attributes with graph-derived signal. Every feature operates on a (Company A, Company B) pair, not single entities.<\/strong><\/p>\n\n\n\n

            \n
            \n
            \"\"<\/figure>\n<\/div>\n\n\n\n
            \n
            \"\"<\/figure>\n<\/div>\n<\/div>\n\n\n\n

            Stage 1 \u00b7 Binary Link Prediction<\/h3>\n\n\n\n
            \"\"<\/figure>\n\n\n\n

            The modeling process begins by constructing a binary classification dataset. Existing relationships are treated as positive samples, while an equal number of synthetic negative pairs are generated by randomly pairing companies that do not have an observed relationship, ensuring no self-pairs or duplicates. Both positive and negative samples are split into training, validation, and testing sets following a 70% \/ 20% \/ 30% structure, where the validation set is used for tuning.<\/p>\n\n\n\n

            Stage 2 \u00b7 Coarse Type Classification<\/h3>\n\n\n\n
            \"\"<\/figure>\n\n\n\n

            For pairs predicted as having a relationship, a second model is trained to classify the general type of relationship. This model uses only positive samples from the dataset. To simplify the classification task, rare relationship types are grouped into an Other Rare category, and both direct and ultimate consolidation relationships are combined into a single Consolidated class.<\/p>\n\n\n\n

            Stage 3 \u00b7 Direct vs Ultimate Consolidation<\/h3>\n\n\n\n
            \"\"<\/figure>\n\n\n\n

            A third specialized model is introduced to distinguish between direct and ultimate consolidation relationships within the subset classified as Consolidated. First, a directed graph is constructed using only direct consolidation relationships, representing the hierarchical structure of company ownership. From this graph, additional hierarchy-based features are extracted, including node degrees within the direct graph, depth to root (distance to the top-level par-ent), path length between entities, and indicators capturing relative positions in the hierarchy.<\/p>\n\n\n\n

            \n\n\n\n

            Results<\/h2>\n\n\n\n

            Final Stacked System Results<\/h3>\n\n\n\n
            \"\"<\/figure>\n\n\n\n

            The final stacked system combines all three models and achieves an overall accuracy of 0.852. The system performs exceptionally well on identifying non-relationships, with an F1-score of 0.98, showing that Step 1 effectively filters out invalid pairs. Among relationship types, Is Fund Managed By and Is Sub fund Of are predicted reliably with balanced precision and recall. However, the system inherits the bias observed in Step 3, with strong performance on ultimate consolidation (recall = 0.97) but weaker performance on direct consolidation.<\/p>\n\n\n\n

            Case Demonstrations \u00b7 Early Exits<\/h3>\n\n\n\n
            • \"\"<\/figure><\/li>
            • \"\"<\/figure><\/li><\/ul><\/div>\n\n\n\n
              \n

              Not every pair goes through all three stages. The stacked design saves compute by stopping early whenever it can commit to a prediction.<\/p>\n<\/blockquote>\n\n\n\n

              Case Demonstrations \u00b7 Full Pipeline<\/h3>\n\n\n\n
              • \"\"<\/figure><\/li>
              • \"\"<\/figure><\/li><\/ul><\/div>\n\n\n\n

                When all three stages run, the specialist Stage 3 model resolves the hardest distinction \u2014 direct vs ultimate parent control.<\/p>\n\n\n\n

                Query Interface \u00b7 Live Demo<\/h3>\n\n\n\n
                  \n
                • End users interact with the pipeline through a single function call \u2014 the three stages run sequentially and produce a final labeled prediction.<\/li>\n<\/ul>\n\n\n\n
                  \n
                  \n
                  \"\"<\/figure>\n<\/div>\n\n\n\n
                  \n
                  \"\"<\/figure>\n<\/div>\n<\/div>\n\n\n\n

                  Error Analysis \u00b7 Confusion Matrix<\/h3>\n\n\n\n
                  \n
                  \n
                  \n
                  \"\"<\/figure>\n\n\n\n

                  Errors concentrate in 3 difficult classes (consolidation + feeder). Fund-side prediction is near-perfect.<\/p>\n<\/div>\n<\/div>\n\n\n\n

                  \n
                  \"\"<\/figure>\n<\/div>\n<\/div>\n\n\n\n

                  Error Analysis \u00b7 Stage Bottlenecks<\/h3>\n\n\n\n
                  \n
                  \n
                  \"\"<\/figure>\n<\/div>\n\n\n\n
                  \n
                  \"\"<\/figure>\n<\/div>\n<\/div>\n\n\n\n

                  WHERE ERRORS COME FROM:
                  1. Binary stage filters out ~49% of ultimate and ~34% of direct consolidation pairs too aggressively.
                  2. At the type stage, DIRECT \u2194 ULTIMATE confusion dominates the remaining error mass.
                  3. Fund-side errors are minor; consolidation-side errors drive the macro-F1 gap.<\/p>\n\n\n\n

                  \n\n\n\n

                  Challenges & Next Steps<\/h2>\n\n\n\n

                  Challenges<\/h3>\n\n\n\n
                  \"\"<\/figure>\n\n\n\n

                  Next Steps & Future Work<\/h3>\n\n\n\n
                  \"\"<\/figure>\n\n\n\n

                  Acknowledgements<\/h2>\n\n\n\n

                  We would like to thank Professor Anand, Professor Caliskan, and for their guidance and technical assistance. We would also like to thank Team Rel8ed for their expertise with work, and their great help in developing our project pipeline.<\/p>\n\n\n\n

                  <\/p>\n","protected":false},"excerpt":{"rendered":"

                  Meet the Team Alvin Yao Model Training & Evaluation Developed performance diagnostics and visualized stage-by-stage model behavior. Authored the next-steps and challenge discussion. 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