Designing Automated Risk Scoring for Suspicious Caller IDs
What This Guide Covers
Configure a real-time caller ID risk scoring pipeline within Genesys Cloud CX that evaluates inbound SIP calls against STIR/SHAKEN attestation data, external threat intelligence APIs, and historical interaction patterns. The end result is an automated numeric risk score that dictates routing behavior, applies compliance hold patterns, or triggers fraud-specific IVR paths without manual intervention.
Prerequisites, Roles & Licensing
- Licensing Tier: Genesys Cloud CX 2 or CX 3 (required for full Architect flow capabilities, Data API access, and advanced telephony routing)
- Granular Permissions:
Telephony > Trunk > View,Telephony > Inbound Route > Edit,Architect > Flow > Edit,Data > Integration > Edit,API > Token > Create,Data > Data API > Edit - OAuth Scopes:
telephony:inbound:view,telephony:inbound:edit,architect:flow:edit,data:api:access,call:details:read - External Dependencies: STIR/SHAKEN-enabled SIP trunk provider, threat intelligence or number insight API endpoint, persistent data store (Genesys Cloud Data API, Snowflake, or PostgreSQL) for historical scoring and audit trails
The Implementation Deep-Dive
1. Inbound SIP Header Parsing and STIR/SHAKEN Extraction
The foundation of caller ID risk scoring is cryptographic attestation. Genesys Cloud CX exposes STIR/SHAKEN verification data through SIP headers passed from your originating trunk provider. You must capture these headers at the exact moment the call enters the platform, before any IVR logic or queue routing occurs.
Configure your inbound trunk to forward P-Asserted-Identity, Identity, and Identity-Info headers. Genesys Cloud maps these to internal system variables. In the Architect flow, use the Start node to capture the initial call context, then immediately branch to a Set Data node that extracts the attestation level. The platform standardizes attestation into three tiers:
A: Full attestation. The originating carrier vouches for both the right to originate and the accuracy of the caller ID.B: Partial attestation. The carrier vouches for the right to originate but cannot verify the caller ID accuracy.C: Gateway attestation. The carrier only vouches for the right to originate from its network. No caller ID verification.
Extract the value using the system variable ${data.call.callTo.attLevel} or parse the raw SIP header via ${data.call.callTo.sipHeaders["Identity"]} if your trunk provider uses non-standard mapping.
The Trap: Assuming attLevel: A guarantees a legitimate caller. Attackers routinely purchase compromised carrier accounts or use compromised SIP endpoints that retain valid signing certificates. A Level A attestation only proves the call originated from a trusted network boundary. It does not verify caller intent, number ownership, or compliance with TCPA consent requirements. If you route Level A calls directly to agents, you expose your workforce to vishing, social engineering, and automated robocall campaigns that bypass cryptographic checks.
Architectural Reasoning: We treat STIR/SHAKEN as a baseline trust signal, not a pass/fail gate. Cryptographic attestation prevents trivial number spoofing but cannot stop sophisticated fraud. The scoring engine must weight attestation level alongside behavioral signals and external threat data. We capture the raw header early because intermediate carriers sometimes strip or modify SIP headers during transit. If the header arrives malformed, the flow must degrade gracefully rather than drop the call.
2. External Threat Intelligence API Integration and Latency Management
Real-time scoring requires external validation. You will integrate a threat intelligence or number insight API using the Architect Invoke HTTP Request node. This call must resolve within 800 milliseconds to prevent IVR timeout or caller abandonment.
Configure the HTTP request node with the following parameters:
- Method:
POST - URL:
https://api.threatintel.example.com/v2/lookup/callerid - Timeout:
1500ms (Genesys Cloud enforces a hard timeout; calls exceeding this value fail the node) - Headers:
Authorization: Bearer ${system.token},Content-Type: application/json,Accept: application/json - Payload:
{
"phone_number": "${data.call.callFrom.number}",
"request_id": "${system.transactionId}",
"timestamp": "${system.currentTime}",
"attestation_level": "${data.call.callTo.attLevel}"
}
The API must return a structured threat score. Expect a response payload similar to:
{
"risk_score": 78,
"threat_categories": ["spoofing", "high_frequency_dialing"],
"confidence": 0.92,
"last_reported": "2024-11-15T08:22:00Z",
"carrier_match": true
}
Map the response to flow data using ${data.httpResponse.body.risk_score}. Cache the result locally within the flow context to avoid redundant lookups if the caller triggers multiple IVR paths.
The Trap: Configuring the HTTP timeout to match standard web API expectations (3 to 5 seconds). Voice media streams cannot sustain idle waiting periods beyond 2 seconds without triggering carrier-side circuit resets or Genesys Cloud media server disconnections. A 4-second timeout guarantees call drops during peak load or network jitter. Additionally, failing to implement a fallback response when the API returns a 429 or 503 status code forces the flow into an error state, which Genesys Cloud routes to the default error handler or drops entirely.
Architectural Reasoning: We enforce sub-1.5-second timeouts because conversational UI standards dictate that callers perceive delays over 2 seconds as system failures. The HTTP node must include a parallel fallback path that reads from a locally cached threat database or defaults to a neutral risk score of 50 when the external service is unreachable. We design for network variance, not ideal conditions. The scoring engine must remain deterministic even when external dependencies degrade.
3. Architect Flow Design and Risk Score Calculation Logic
The risk score calculation combines cryptographic attestation, external threat intelligence, and behavioral heuristics into a single numeric value between 0 and 100. You will implement this logic using a Set Data node with Genesys Cloud expression syntax.
Define the scoring formula before building the flow. A production-ready weighting model looks like this:
- STIR/SHAKEN Level A: 0 points
- STIR/SHAKEN Level B: 15 points
- STIR/SHAKEN Level C: 30 points
- External API risk score: multiplied by 0.6 weight
- Call frequency heuristic: 10 points added if the number has called more than 3 times in the past 60 minutes
- Time-of-day heuristic: 5 points added if the call arrives outside registered business hours
Implement the calculation in Architect using JSONata-compatible expressions or built-in function syntax:
${
let attScore = if(data.call.callTo.attLevel == "A") 0 else if(data.call.callTo.attLevel == "B") 15 else 30;
let apiScore = if(data.httpResponse != null) data.httpResponse.body.risk_score else 50;
let freqScore = if(data.call.callFrom.callCountLastHour > 3) 10 else 0;
let timeScore = if(system.currentTime.hour < 7 || system.currentTime.hour > 21) 5 else 0;
let total = attScore + (apiScore * 0.6) + freqScore + timeScore;
if(total > 100) 100 else if(total < 0) 0 else total
}
Store the final value in ${data.call.riskScore}. Use a Switch node to evaluate the score against defined thresholds:
0 to 39: Low risk40 to 69: Medium risk70 to 100: High risk
The Trap: Using linear scoring without weighted decay or threshold normalization. Adding raw API scores directly to attestation penalties creates score inflation. An API returning 85 combined with a Level C attestation (30 points) yields 115, which breaks downstream routing logic that expects a 0-100 range. Additionally, hardcoding thresholds in the flow prevents business stakeholders from adjusting risk tolerance without redeploying the entire Architect version.
Architectural Reasoning: We normalize all inputs to a bounded scale and apply multiplicative weights to external signals. This prevents score inflation and maintains predictable routing behavior. We store thresholds in a Genesys Cloud Configuration Profile or external key-value store rather than embedding them in the flow. This allows compliance teams to adjust risk tolerance during active fraud campaigns without requiring platform administrators to pause and redeploy flow versions. The expression engine evaluates synchronously, ensuring the score is available before any media playback or queue transfer occurs.
4. Routing Enforcement and Compliance Fallback Handling
The routing layer translates the numeric risk score into actionable telephony behavior. You will use a Transfer node for low-risk calls, a Play Prompt and Wait sequence for medium-risk calls, and a specialized fraud handling path for high-risk calls.
For low-risk scores (0-39), transfer directly to the primary skill-based queue. Preserve the original caller ID and attach the risk score as a custom attribute for WEM and Speech Analytics tagging:
- Transfer Type:
Queue - Queue ID:
${data.queue.primary} - Attributes:
{"riskScore": "${data.call.riskScore}", "attestationLevel": "${data.call.callTo.attLevel}"}
For medium-risk scores (40-69), route to a verification IVR. Play a compliance prompt that requests caller intent confirmation without revealing the risk assessment. Use a Gather Input node to capture DTMF or voice response. If the caller fails verification or hangs up, log the disposition and disconnect.
For high-risk scores (70-100), route to a fraud isolation queue or trigger a silent recording path. Do not drop the call immediately. FCC and TCPA guidelines require documented handling of suspected robocalls. Play a neutral hold prompt, enable Record Call with cloud storage, and transfer to a dedicated fraud investigation team. Tag the interaction with fraud:high_risk for downstream reporting.
Implement a Set Data node before every routing decision to append audit metadata:
{
"riskScore": "${data.call.riskScore}",
"routingDecision": "${data.call.routingPath}",
"timestamp": "${system.currentTime}",
"flowVersion": "${system.flowVersion}"
}
The Trap: Disconnecting high-risk calls without recording or logging disposition metadata. Regulatory bodies classify abrupt call termination without audit trails as potential evidence tampering. Additionally, routing high-risk calls to the same queue as verified customers contaminates agent performance metrics and increases speech analytics false positives. Agents cannot distinguish between malicious callers and legitimate customers experiencing network issues.
Architectural Reasoning: We isolate high-risk traffic into a separate routing domain to protect agent productivity and maintain clean analytics datasets. Every routing decision generates an immutable audit record stored via the Data API. This satisfies compliance requirements for TCPA consent verification and FCC robocall reporting mandates. The flow never drops a call without first capturing disposition data, because legal defense relies on documented handling procedures, not automated blocking.
Validation, Edge Cases and Troubleshooting
Edge Case 1: STIR/SHAKEN Header Stripping by Carrier Intermediaries
The Failure Condition: The Architect flow receives null for ${data.call.callTo.attLevel} despite the trunk provider advertising full STIR/SHAKEN support. The scoring engine defaults to a neutral score, allowing spoofed traffic through.
The Root Cause: Intermediate carriers, VoIP gateways, or session border controllers strip P-Asserted-Identity headers during SIP re-INVITE or REFER transactions. Some providers replace standardized headers with proprietary equivalents like X-Orig-CallerId or P-Asserted-Number.
The Solution: Configure the trunk provider to preserve end-to-end SIP headers. In Architect, add a fallback parsing sequence that checks multiple header names using a Switch node. If all attestation headers return null, assign a default Level C score and append a header_stripped flag to the audit metadata. Enable SIP trace logging on the trunk to verify header propagation during testing.
Edge Case 2: External API Rate Limiting and Circuit Breaker Failure
The Failure Condition: The threat intelligence API returns HTTP 429 responses during peak inbound volume. The HTTP request node fails, the flow falls back to a static score, and routing decisions become inconsistent.
The Root Cause: External APIs enforce per-second request limits. Genesys Cloud does not implement automatic retry logic for HTTP nodes. Concurrent calls from the same caller ID or distributed inbound traffic exceed the vendor rate limit.
The Solution: Implement a local caching layer using Genesys Cloud’s Data API or an external Redis instance. Cache threat scores for a 5-minute TTL. Before invoking the HTTP request, check the cache using a Query Data node. Only call the external API when the cache is empty or expired. Configure the HTTP node with a 200 ms retry on 429 responses, then fall back to the cached value. Monitor API latency via Genesys Cloud Observability and adjust TTL based on threat intelligence update frequency.
Edge Case 3: Spoofed Local Numbers Bypassing Threshold Logic
The Failure Condition: Attackers spoof high-value local numbers that match your enterprise dial plan. The scoring engine assigns low risk due to matching area codes and clean attestation, routing the calls directly to agents.
The Root Cause: Local number spoofing exploits trust heuristics. Attackers clone legitimate business numbers or use ported numbers that retain clean carrier reputations. The scoring model overweights geographic matching and underweights behavioral anomalies.
The Solution: Integrate a call frequency analysis module that tracks inbound volume per number hash. Use Genesys Cloud’s Analytics API to pull historical call volume for the past 24 hours. If a local number exceeds 5 inbound attempts within 60 minutes, override the attestation score and apply a 25-point penalty. Add a DTMF verification step for all calls matching enterprise dial patterns. Cross-reference with WFM scheduled agent coverage to detect after-hours spoofing campaigns targeting unstaffed queues.