fix: remove StandardScaler from fraud detection vectorization

StandardScaler inverts relative distances when most claims share identical
features (e.g. same IP), causing LOF to score the homogeneous cluster as
more anomalous than actual outliers. Reverting to raw numeric features
preserves the correct outlier signal while keeping the small-batch n_neighbors fix.

Author: amberly-d

app/ai-service/services/fraud_detection.py

@@ -9,7 +9,7 @@
 from typing import List
 
 import numpy as np
-from sklearn.preprocessing import LabelEncoder, StandardScaler
+from sklearn.preprocessing import LabelEncoder
 from sklearn.neighbors import LocalOutlierFactor
 
 from schemas.fraud import ClaimMetadata, ClaimFraudResult
@@ -26,7 +26,7 @@
 
 
 def _vectorize(claims: List[ClaimMetadata]) -> np.ndarray:
-    """Convert claim metadata into a normalised numeric feature matrix."""
+    """Convert claim metadata into a numeric feature matrix."""
     ip_enc = LabelEncoder()
     hash_enc = LabelEncoder()
     loc_enc = LabelEncoder()
@@ -40,19 +40,13 @@ def _vectorize(claims: List[ClaimMetadata]) -> np.ndarray:
     hash_enc.fit(hashes)
     loc_enc.fit(locs)
 
-    raw = np.column_stack([
+    return np.column_stack([
         ip_enc.transform(ips).astype(float),
         hash_enc.transform(hashes).astype(float),
         loc_enc.transform(locs).astype(float),
         np.array(amounts, dtype=float),
     ])
 
-    # Standardise every column so Euclidean distance in LOF is not
-    # dominated by whichever column has the largest range (e.g. raw
-    # token amounts vs. small integer codes).
-    scaler = StandardScaler()
-    return scaler.fit_transform(raw)
-
 
 def detect_fraud(claims: List[ClaimMetadata]) -> List[ClaimFraudResult]:
     """