feat: add hybrid GNN/ML models and improved investigation UI

fix: replace shap.TreeExplainer with XGBoost native pred_contribs

fix: build SHAP input as numpy float64 array, not pandas DataFrame

fix: remove simulated XGBoost metrics; harden SHAP against string dtypes

fix: build SHAP input DataFrame from pre-coerced dict, not chained indexing

fix: coerce feature columns to float before XGBoost/SHAP, fix alert enrichment skipping score=0

Stabilize investigation SHAP handling

Harden investigation numeric coercion

feat: decouple frontend and fix investigation 500 error

feat: Implement caching for API responses and SHAP explanations to improve performance and reduce redundant computations.

Fix investigation view rendering, fix STR PDF generation (unicode/limit), and add graph checkpoint for fast server restart

changed

Implement ML fraud detection pipeline and interactive graph visualiser

Initial commit: Fund Flow Tracker AML Dashboard