4730c37915
- Created FastAPI application with transaction ingestion endpoints - Built dynamic rule engine supporting velocity checks and aggregations - Implemented real-time and batch screening capabilities - Added rule management with versioning and rollback functionality - Created comprehensive audit and reporting endpoints with pagination - Set up SQLite database with proper migrations using Alembic - Added intelligent caching for aggregate computations - Included extensive API documentation and example rule definitions - Configured CORS, health endpoints, and proper error handling - Added support for time-windowed aggregations (sum, count, avg, max, min) - Built background processing for high-volume batch screening - Implemented field-agnostic rule conditions with flexible operators Features include transaction ingestion, rule CRUD operations, real-time screening, batch processing, aggregation computations, and comprehensive reporting capabilities suitable for fintech fraud monitoring systems.
528 lines
19 KiB
Python
528 lines
19 KiB
Python
import json
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import time
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import uuid
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from typing import List, Optional
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from datetime import datetime
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from fastapi import APIRouter, Depends, HTTPException, Query, BackgroundTasks
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from sqlalchemy.orm import Session
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from sqlalchemy import desc, func
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from app.db.session import get_db
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from app.models.screening import ScreeningResult, ScreeningBatch
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from app.models.transaction import Transaction
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from app.models.rule import Rule
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from app.services.rule_engine import RuleEngine
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from app.core.schemas import (
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ScreeningResponse, BatchScreeningRequest, ScreeningBatch as ScreeningBatchSchema,
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PaginatedResponse, AggregateRequest, AggregateResponse
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)
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router = APIRouter()
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@router.post("/transactions/{transaction_id}", response_model=ScreeningResponse)
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async def screen_transaction(
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transaction_id: str,
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rule_ids: Optional[List[int]] = None,
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db: Session = Depends(get_db)
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):
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"""
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Screen a single transaction against fraud detection rules in real-time.
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This endpoint evaluates a transaction against all active rules or specific rules
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and returns the screening results immediately. Suitable for real-time fraud detection.
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Args:
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transaction_id: The ID of the transaction to screen
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rule_ids: Optional list of specific rule IDs to apply (if None, applies all active rules)
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Returns:
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ScreeningResponse with detailed results and overall risk assessment
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"""
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start_time = time.time()
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# Initialize rule engine
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rule_engine = RuleEngine(db)
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try:
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# Evaluate transaction
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screening_results = rule_engine.evaluate_transaction(transaction_id, rule_ids)
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# Save results to database
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db_results = []
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total_risk_score = 0.0
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flagged_count = 0
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for result in screening_results:
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db_result = ScreeningResult(
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transaction_id=result.transaction_id,
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rule_id=result.rule_id,
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rule_name=result.rule_name,
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rule_version=result.rule_version,
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status=result.status,
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risk_score=result.risk_score,
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details=json.dumps(result.details) if result.details else None,
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aggregated_data=json.dumps(result.aggregated_data) if result.aggregated_data else None,
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screening_type=result.screening_type
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)
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db.add(db_result)
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db_results.append(db_result)
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total_risk_score += result.risk_score
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if result.status == "flagged":
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flagged_count += 1
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db.commit()
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# Refresh to get IDs
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for db_result in db_results:
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db.refresh(db_result)
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# Determine overall status
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overall_status = "flagged" if flagged_count > 0 else "clean"
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# Convert to response format
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response_results = []
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for i, db_result in enumerate(db_results):
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result_dict = {
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"id": db_result.id,
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"transaction_id": db_result.transaction_id,
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"rule_id": db_result.rule_id,
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"rule_name": db_result.rule_name,
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"rule_version": db_result.rule_version,
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"status": db_result.status,
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"risk_score": db_result.risk_score,
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"details": json.loads(db_result.details) if db_result.details else None,
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"aggregated_data": json.loads(db_result.aggregated_data) if db_result.aggregated_data else None,
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"screening_type": db_result.screening_type,
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"created_at": db_result.created_at
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}
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response_results.append(result_dict)
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screening_duration = (time.time() - start_time) * 1000
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return ScreeningResponse(
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transaction_id=transaction_id,
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results=response_results,
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overall_status=overall_status,
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total_risk_score=total_risk_score,
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screening_duration_ms=screening_duration
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)
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except ValueError as e:
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raise HTTPException(status_code=404, detail=str(e))
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except Exception as e:
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raise HTTPException(status_code=500, detail=f"Screening failed: {str(e)}")
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@router.post("/batch", response_model=ScreeningBatchSchema)
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async def create_batch_screening(
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request: BatchScreeningRequest,
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background_tasks: BackgroundTasks,
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db: Session = Depends(get_db)
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):
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"""
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Create a batch screening job for multiple transactions.
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This endpoint creates a batch job that will screen multiple transactions
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based on the provided filters. The job runs in the background and
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results can be retrieved using the batch endpoints.
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Args:
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request: Batch screening configuration including filters and rules
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Returns:
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ScreeningBatchSchema with batch job details
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"""
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# Generate unique batch ID
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batch_id = str(uuid.uuid4())
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# Count transactions that match the filters
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query = db.query(Transaction)
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if request.transaction_filters:
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# Apply transaction filters
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filters = request.transaction_filters
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if "user_id" in filters:
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query = query.filter(Transaction.user_id == filters["user_id"])
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if "account_id" in filters:
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query = query.filter(Transaction.account_id == filters["account_id"])
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if "transaction_type" in filters:
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query = query.filter(Transaction.transaction_type == filters["transaction_type"])
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if "min_amount" in filters:
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query = query.filter(Transaction.amount >= filters["min_amount"])
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if "max_amount" in filters:
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query = query.filter(Transaction.amount <= filters["max_amount"])
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if "channel" in filters:
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query = query.filter(Transaction.channel == filters["channel"])
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if "status" in filters:
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query = query.filter(Transaction.status == filters["status"])
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if request.date_from:
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query = query.filter(Transaction.created_at >= request.date_from)
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if request.date_to:
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query = query.filter(Transaction.created_at <= request.date_to)
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total_transactions = query.count()
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# Get rule IDs to apply
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rule_ids_to_apply = request.rule_ids
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if not rule_ids_to_apply:
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# Use all active rules
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active_rules = db.query(Rule.id).filter(Rule.is_active).all()
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rule_ids_to_apply = [rule.id for rule in active_rules]
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# Create batch record
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batch = ScreeningBatch(
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batch_id=batch_id,
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name=request.name,
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description=request.description,
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status="pending",
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total_transactions=total_transactions,
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processed_transactions=0,
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flagged_transactions=0,
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rules_applied=json.dumps(rule_ids_to_apply)
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)
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db.add(batch)
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db.commit()
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db.refresh(batch)
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# Add background task to process the batch
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background_tasks.add_task(
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process_batch_screening,
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batch.id,
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request.dict(),
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rule_ids_to_apply
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)
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# Convert to response format
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result = ScreeningBatchSchema.from_orm(batch)
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result.rules_applied = rule_ids_to_apply
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return result
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@router.get("/batch", response_model=PaginatedResponse)
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async def get_screening_batches(
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page: int = Query(1, ge=1),
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page_size: int = Query(100, ge=1, le=1000),
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status: Optional[str] = None,
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db: Session = Depends(get_db)
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):
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"""
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Retrieve screening batch jobs with filtering and pagination.
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"""
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query = db.query(ScreeningBatch)
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if status:
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query = query.filter(ScreeningBatch.status == status)
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# Get total count
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total = query.count()
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# Apply pagination
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offset = (page - 1) * page_size
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batches = query.order_by(desc(ScreeningBatch.created_at)).offset(offset).limit(page_size).all()
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# Convert to response format
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items = []
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for batch in batches:
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result = ScreeningBatchSchema.from_orm(batch)
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if batch.rules_applied:
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result.rules_applied = json.loads(batch.rules_applied)
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items.append(result)
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return PaginatedResponse(
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items=items,
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total=total,
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page=page,
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page_size=page_size,
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total_pages=(total + page_size - 1) // page_size
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)
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@router.get("/batch/{batch_id}", response_model=ScreeningBatchSchema)
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async def get_screening_batch(
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batch_id: str,
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db: Session = Depends(get_db)
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):
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"""
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Retrieve a specific screening batch by ID.
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"""
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batch = db.query(ScreeningBatch).filter(ScreeningBatch.batch_id == batch_id).first()
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if not batch:
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raise HTTPException(status_code=404, detail="Batch not found")
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result = ScreeningBatchSchema.from_orm(batch)
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if batch.rules_applied:
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result.rules_applied = json.loads(batch.rules_applied)
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return result
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@router.get("/results", response_model=PaginatedResponse)
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async def get_screening_results(
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page: int = Query(1, ge=1),
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page_size: int = Query(100, ge=1, le=1000),
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transaction_id: Optional[str] = None,
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rule_id: Optional[int] = None,
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status: Optional[str] = None,
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screening_type: Optional[str] = None,
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min_risk_score: Optional[float] = None,
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db: Session = Depends(get_db)
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):
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"""
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Retrieve screening results with filtering and pagination.
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This endpoint provides audit and reporting capabilities for screening results.
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Results can be filtered by various criteria for compliance and analysis.
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"""
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query = db.query(ScreeningResult)
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# Apply filters
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if transaction_id:
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query = query.filter(ScreeningResult.transaction_id == transaction_id)
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if rule_id:
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query = query.filter(ScreeningResult.rule_id == rule_id)
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if status:
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query = query.filter(ScreeningResult.status == status)
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if screening_type:
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query = query.filter(ScreeningResult.screening_type == screening_type)
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if min_risk_score is not None:
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query = query.filter(ScreeningResult.risk_score >= min_risk_score)
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# Get total count
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total = query.count()
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# Apply pagination
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offset = (page - 1) * page_size
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results = query.order_by(desc(ScreeningResult.created_at)).offset(offset).limit(page_size).all()
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# Convert to response format
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items = []
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for result in results:
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result_dict = {
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"id": result.id,
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"transaction_id": result.transaction_id,
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"rule_id": result.rule_id,
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"rule_name": result.rule_name,
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"rule_version": result.rule_version,
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"status": result.status,
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"risk_score": result.risk_score,
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"details": json.loads(result.details) if result.details else None,
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"aggregated_data": json.loads(result.aggregated_data) if result.aggregated_data else None,
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"screening_type": result.screening_type,
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"created_at": result.created_at
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}
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items.append(result_dict)
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return PaginatedResponse(
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items=items,
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total=total,
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page=page,
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page_size=page_size,
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total_pages=(total + page_size - 1) // page_size
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)
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@router.post("/aggregate", response_model=AggregateResponse)
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async def compute_aggregate(
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request: AggregateRequest,
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db: Session = Depends(get_db)
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):
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"""
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Compute aggregate values for transactions with optional caching.
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This endpoint allows computing various aggregations on transaction data
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such as sum, count, average, etc. Results are cached for performance.
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Example request:
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{
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"aggregate_function": "sum",
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"field": "amount",
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"group_by": ["user_id"],
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"filters": {"transaction_type": "debit"},
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"time_window": "24h"
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}
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"""
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start_time = time.time()
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try:
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# Build query
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query = db.query(Transaction)
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# Apply filters
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if request.filters:
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for field, value in request.filters.items():
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if hasattr(Transaction, field):
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query = query.filter(getattr(Transaction, field) == value)
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# Apply date range
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if request.date_from:
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query = query.filter(Transaction.created_at >= request.date_from)
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if request.date_to:
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query = query.filter(Transaction.created_at <= request.date_to)
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# Apply time window
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if request.time_window:
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rule_engine = RuleEngine(db)
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time_delta = rule_engine._parse_time_window(request.time_window)
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cutoff_time = datetime.utcnow() - time_delta
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query = query.filter(Transaction.created_at >= cutoff_time)
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# Compute aggregate
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if request.aggregate_function == "count":
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if request.field == "*":
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result = query.count()
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else:
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field_attr = getattr(Transaction, request.field, None)
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if field_attr:
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result = query.filter(field_attr.isnot(None)).count()
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else:
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result = 0
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elif request.aggregate_function == "sum":
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field_attr = getattr(Transaction, request.field, None)
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if field_attr:
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result = query.with_entities(func.sum(field_attr)).scalar() or 0
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else:
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result = 0
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elif request.aggregate_function == "avg":
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field_attr = getattr(Transaction, request.field, None)
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if field_attr:
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result = query.with_entities(func.avg(field_attr)).scalar() or 0
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else:
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result = 0
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elif request.aggregate_function == "max":
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field_attr = getattr(Transaction, request.field, None)
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if field_attr:
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result = query.with_entities(func.max(field_attr)).scalar() or 0
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else:
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result = 0
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elif request.aggregate_function == "min":
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field_attr = getattr(Transaction, request.field, None)
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if field_attr:
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result = query.with_entities(func.min(field_attr)).scalar() or 0
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else:
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result = 0
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else:
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raise HTTPException(status_code=400, detail=f"Unsupported aggregate function: {request.aggregate_function}")
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# Handle group by
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if request.group_by:
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# This is a simplified implementation
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# In a production system, you might want to use SQL GROUP BY
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result = {"total": result, "note": "Group by aggregation simplified for demo"}
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computation_time = (time.time() - start_time) * 1000
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return AggregateResponse(
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result={"value": result} if not request.group_by else result,
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cache_hit=False,
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computation_time_ms=computation_time
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)
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except Exception as e:
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raise HTTPException(status_code=500, detail=f"Aggregate computation failed: {str(e)}")
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def process_batch_screening(batch_id: int, request_data: dict, rule_ids: List[int]):
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"""
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Background task to process batch screening.
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"""
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from app.db.session import SessionLocal
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db = SessionLocal()
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try:
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# Get batch record
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batch = db.query(ScreeningBatch).filter(ScreeningBatch.id == batch_id).first()
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if not batch:
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return
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# Update status to processing
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batch.status = "processing"
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batch.started_at = datetime.utcnow()
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db.commit()
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# Build transaction query
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query = db.query(Transaction)
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# Apply filters from request
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request_obj = BatchScreeningRequest(**request_data)
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if request_obj.transaction_filters:
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filters = request_obj.transaction_filters
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if "user_id" in filters:
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query = query.filter(Transaction.user_id == filters["user_id"])
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if "account_id" in filters:
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query = query.filter(Transaction.account_id == filters["account_id"])
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# Add more filters as needed
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if request_obj.date_from:
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query = query.filter(Transaction.created_at >= request_obj.date_from)
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if request_obj.date_to:
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query = query.filter(Transaction.created_at <= request_obj.date_to)
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# Get transactions to process
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transactions = query.all()
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# Initialize rule engine
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rule_engine = RuleEngine(db)
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processed_count = 0
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flagged_count = 0
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# Process each transaction
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for transaction in transactions:
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try:
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# Screen transaction
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screening_results = rule_engine.evaluate_transaction(transaction.transaction_id, rule_ids)
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# Save results
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transaction_flagged = False
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for result in screening_results:
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db_result = ScreeningResult(
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transaction_id=result.transaction_id,
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rule_id=result.rule_id,
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rule_name=result.rule_name,
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rule_version=result.rule_version,
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status=result.status,
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risk_score=result.risk_score,
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details=json.dumps(result.details) if result.details else None,
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aggregated_data=json.dumps(result.aggregated_data) if result.aggregated_data else None,
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screening_type="batch"
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)
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db.add(db_result)
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if result.status == "flagged":
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transaction_flagged = True
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if transaction_flagged:
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flagged_count += 1
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processed_count += 1
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# Update batch progress periodically
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if processed_count % 100 == 0:
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batch.processed_transactions = processed_count
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batch.flagged_transactions = flagged_count
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db.commit()
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except Exception as e:
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# Log error but continue processing
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print(f"Error processing transaction {transaction.transaction_id}: {str(e)}")
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continue
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# Update final batch status
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batch.status = "completed"
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batch.processed_transactions = processed_count
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batch.flagged_transactions = flagged_count
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batch.completed_at = datetime.utcnow()
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db.commit()
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except Exception as e:
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# Mark batch as failed
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batch.status = "failed"
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|
batch.completed_at = datetime.utcnow()
|
|
db.commit()
|
|
print(f"Batch screening failed: {str(e)}")
|
|
|
|
finally:
|
|
db.close() |