Add the five Ledgerline application metrics

- ledgerline.transactions.total (counter: kind, status, currency) and
  ledgerline.transaction.duration (histogram, seconds: kind) in gateway,
  the only place that sees the final outcome, failures included. currency
  is client input, so values outside the seeded set are recorded as
  "other".
- ledgerline.fraud.score (histogram, buckets in 0.1 steps to 1.0) in fraud.
- ledgerline.stream.lag (observable gauge: consumer_group) in audit and
  notifier, read from XINFO GROUPS through a separate sync Redis client,
  because callbacks run on the scrape thread; the call is untraced so
  scrapes don't create traces.
- ledgerline.db.pool.in_use (observable up-down counter: pool) in every
  service with a DB engine. The SQLAlchemy instrumentation's own pool
  metric names a pool by DSN, which merges the normal and starved engines.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

services/fraud/app.py

@@ -2,7 +2,7 @@ import httpx
 from fastapi import FastAPI, Request
 from fastapi.responses import JSONResponse
 from pydantic import BaseModel
-from opentelemetry import trace
+from opentelemetry import metrics, trace
 from opentelemetry.trace import StatusCode
 from opentelemetry.instrumentation.fastapi import FastAPIInstrumentor
 
@@ -27,6 +27,13 @@ FastAPIInstrumentor.instrument_app(
     exclude_spans=["receive", "send"],
 )
 
+meter = metrics.get_meter(__name__)
+fraud_score = meter.create_histogram(
+    "ledgerline.fraud.score",
+    description="Risk score per scoring call (0-1; gateway rejects above 0.8)",
+    explicit_bucket_boundaries_advisory=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],
+)
+
 
 class ScoreRequest(BaseModel):
     account_id: str
@@ -110,6 +117,7 @@ async def score_transaction(body: ScoreRequest):
         risk_score = score(body.category_id, matched)
         span.set_attribute("ledgerline.sanctions.matched", matched)
         span.set_attribute("ledgerline.risk.score", risk_score)
+        fraud_score.record(risk_score)
         logger.info(
             "transaction_scored", account_id=body.account_id, risk_score=risk_score, matched=matched
         )