Procureai: An Ai-driven Procurement Spend Intelligence Platform For Automated Anomaly Detection, Supplier Optimisation And Predictive Expenditure Forecasting

Abstract

- Enterprises Routinely Lose Between Three And Eight Percent Of Addressable Procurement Spend To Fragmentation, Duplicate Payments, Maverick Buying, Contract Price Leakage, And Unmanaged Tail Spend. This Paper Presents ProcureAI, A Full-stack, AI-driven Spend-intelligence Platform That Ingests Multi-source Procurement Data, Classifies It Automatically Using A Hybrid NLP Classifier Combining Multinomial Naive Bayes With TF-IDF Cosine K-NN Re-ranking, Detects Leakage Through A Six-detector Unsupervised Anomaly Ensemble, Segments The Supplier Base With K-Means++ Clustering Augmented By Herfindahl-Hirschman Index Concentration Analysis, Forecasts Future Expenditure Via A Four-model Inverse-MAPE-weighted Time-series Ensemble With Residual-quantile Prediction Intervals, And Synthesises All Signals Into A Ranked, Costed, Auditable Cost-reduction Portfolio. Applied To A 36-month Synthetic Enterprise Dataset Of ~8,000 Transactions And $85 Million In Total Spend, The Platform Achieves ~86% NLP Classification Accuracy (macro-F1 ~87%), Detects ~4,000 Anomaly Events, Achieves A Forecasting Ensemble MAPE Of ~18%, And Identifies $7.3 Million (8.4%) In Annual Run-rate Savings. All Algorithms Are Implemented From First Principles In TypeScript, Ensuring Full Auditability Without Opaque Model Binaries Or External ML Services.

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