Use Cases & Integration Patterns

Discover how organizations integrate SanctionsWise API into their compliance workflows.

Manual to API Migration

The Problem: Manual Screening

Many organizations still rely on manual screening processes:

Step	Manual Process	Time
1	Download OFAC list	5 min
2	Open in Excel	2 min
3	Ctrl+F search	3 min
4	Manual logging	5 min

Issues with manual screening:

• ❌ Stale data (lists update frequently)
• ❌ Miss spelling variations
• ❌ No audit trail
• ❌ ~60% accuracy
• ❌ 5+ minutes per entity

The Solution: SanctionsWise API

Metric	Manual	API
Time per entity	5 min	0.2 sec
Accuracy	~60%	99%+
Automation	None	Full
Audit trail	Manual	Automatic

Why Manual Screening Misses Matches

Scenario	Manual Search	OFAC Has	Manual Result	API Result
Spelling Variation	"Mohammed"	"Muhammad", "Mohamad"	❌ MISS	✅ Phonetic Match
Entity Structure	"Bank Rossiya"	"JOINT STOCK BANK 'ROSSIYA'"	❌ MISS	✅ Fuzzy 0.78
Transliteration	"Vladimir Putin"	"Владимир Путин" (Cyrillic)	❌ MISS	✅ Alias Match
Name Order	"Kim Jong Un"	"Jong Un Kim"	❌ MISS	✅ Token Match

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Integration Patterns

Pattern 1: Real-Time Transaction Screening

Best for: Wire transfers, payment processing, customer onboarding

Wire Request → SanctionsWise API → Decision
                     ↓
         ┌──────────┼──────────┐
         ↓          ↓          ↓
      CLEAR    POTENTIAL    MATCH
         ↓          ↓          ↓
    Process    Queue for   Block &
    Transfer    Review      Alert

Implementation:

import requests

def screen_wire_transfer(beneficiary_name, beneficiary_country=None):
    response = requests.post(
        "https://api.sanctionswise.orchestraprime.ai/v1/screen/entity",
        headers={"Authorization": f"Bearer {API_KEY}"},
        json={
            "name": beneficiary_name,
            "country": beneficiary_country,
            "threshold": 0.85
        }
    )

    result = response.json()

    if result["status"] == "clear":
        return {"action": "process", "screening_id": result["screening_id"]}
    elif result["status"] == "potential_match":
        return {"action": "review", "matches": result["matches"]}
    else:  # match
        return {"action": "block", "matches": result["matches"]}

Pattern 2: Nightly Batch Screening

Best for: Portfolio screening, periodic customer review, large migrations

Scheduled    Export         Chunk into        Parallel
Trigger  →  Customer  →    Batches of   →   API Calls  →  Morning
(2AM)       List           100                            Report

Implementation:

import requests
from concurrent.futures import ThreadPoolExecutor

def screen_batch(entities):
    """Screen up to 100 entities in a single batch"""
    response = requests.post(
        "https://api.sanctionswise.orchestraprime.ai/v1/screen/batch",
        headers={"Authorization": f"Bearer {API_KEY}"},
        json={"entities": entities, "threshold": 0.85}
    )
    return response.json()

def nightly_portfolio_screen(customers):
    # Chunk into batches of 100
    batches = [customers[i:i+100] for i in range(0, len(customers), 100)]

    results = {"clear": 0, "review": 0, "match": 0}

    # Process batches in parallel
    with ThreadPoolExecutor(max_workers=5) as executor:
        futures = [executor.submit(screen_batch, batch) for batch in batches]

        for future in futures:
            batch_result = future.result()
            for result in batch_result["results"]:
                results[result["status"]] += 1

    return results

Pattern 3: Event-Driven Microservices

Best for: Modern architectures, real-time triggers, serverless

# AWS Lambda handler for EventBridge events
import json
import requests

def lambda_handler(event, context):
    # Extract entity from event
    entity = event["detail"]

    # Screen against sanctions lists
    response = requests.post(
        "https://api.sanctionswise.orchestraprime.ai/v1/screen/entity",
        headers={"Authorization": f"Bearer {API_KEY}"},
        json={
            "name": entity["name"],
            "entity_type": entity.get("type", "individual"),
            "threshold": 0.85
        }
    )

    result = response.json()

    # Route based on result
    if result["status"] == "match":
        # Publish to blocking queue
        return {"statusCode": 200, "action": "block"}
    elif result["status"] == "potential_match":
        # Publish to review queue
        return {"statusCode": 200, "action": "review"}
    else:
        # Continue normal flow
        return {"statusCode": 200, "action": "continue"}

Pattern 4: Hybrid Caching Strategy

Best for: High-volume applications (50,000+ screens/day)

Status	Cache TTL	Rationale
CLEAR	24 hours	Low risk, reduce API calls
POTENTIAL_MATCH	1 hour	May be resolved by analyst
MATCH	Never cache	Always escalate immediately

Benefits:

• 80%+ cache hit rate
• Under 50ms cached responses
• 70% cost reduction

---

Industry Solutions

Banking & Finance

Touchpoint	API Usage	Trigger
Account Opening	POST /v1/screen/entity	KYC submission
Wire Transfer	POST /v1/screen/entity	Before execution
Loan Origination	POST /v1/screen/batch	Application review
Trade Finance	POST /v1/screen/batch	LC/BG issuance
Portfolio Review	POST /v1/screen/batch	Nightly batch

Cryptocurrency Exchanges

Event	Screening	Action
User Registration	Real-time screening	CLEAR → Approve / MATCH → Reject
Withdrawal > $10K	Re-screen user	Check for list updates since signup
High-risk jurisdiction	Lower threshold (0.60)	Enhanced due diligence

E-Commerce & Export Control

Check	Entities Screened	Lists
Order Screening	Customer name, shipping address	OFAC SDN
Export Control	End-user organization	BIS Entity List
Seller Verification	Marketplace sellers	OFAC SDN

Shipping & Logistics

Function	Entities	Frequency
Freight Forwarding	Shippers, consignees	Per shipment
Vessel Chartering	Vessels, owners (IMO #)	Per charter
Customs Brokerage	Importers, exporters	Per declaration

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Compliance Workflows

Compliance Officer Daily Workflow

Time	Activity	API Usage
08:00	Review overnight batch	Dashboard summary
09:00	Process potential matches	GET /v1/entities/{id} for details
11:00	Handle real-time escalations	Approve/reject with audit trail
14:00	Respond to audit requests	Retrieve by screening_id
16:00	Generate daily report	Coverage, false positive rate

AML Analyst Investigation Workflow

1. Alert Received - Initial screening hit notification
2. Comprehensive Screening - Lower threshold (0.60), include identifiers
3. Relationship Screening - Batch screen associates, employers, family
4. Evidence Collection - GET /v1/entities/{id} for source authority, programs
5. Decision - True Match → File SAR / False Positive → Document

---

Threshold Configuration Guide

Risk Profile	Threshold	False Positives	Best For
Conservative	0.60	Higher	High-risk regions, crypto, sanctions-heavy corridors
Balanced	0.85	Medium	Standard banking, payment processing
Aggressive	0.95	Lower	High-volume, low-risk transactions

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ROI Calculator

Manual vs. API Comparison (100 screens/day)

Metric	Manual	API	Savings
Time per screen	5 min	0.2 sec	99.9%
Daily time	8.3 hours	20 sec	8.3 hours
Annual time	2,166 hours	1.4 hours	2,165 hours
Cost @ $50/hr	$108,300	$3,588	$104,712
Accuracy	60%	99%+	39% gain
Audit coverage	Partial	100%	Complete

Pricing Tiers

Tier	Price	Screens/Month	Rate Limit	Best For
Free	$0/mo	500	1/sec	Testing & POC
Starter	$99/mo	5,000	5/sec	Small business
Professional	$299/mo	25,000	25/sec	Mid-market
Enterprise	$999+/mo	100,000+	50/sec	High volume

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Migration Timeline

4-Week Transition Plan

Week	Phase	Activities
1-2	Parallel Running	Sign up for Free tier, run both manual AND API on same names, compare results, tune threshold
3-4	Primary API	API becomes primary screening method, manual only for spot-checks
5+	Optimized	Integrate with core systems, automate batch screening, upgrade tier as volume grows

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Next Steps

1. Sign up for free tier
2. Review API documentation
3. Explore SDK examples
4. Contact sales for Enterprise pricing