FAQ's
1. How does Scam Detect differ from the security checks you may already run?
While standard payee validation checks confirm whether the payee name and BSB matches the destination account. Scam Detect provides an additional layer of intelligence by checking transaction details against a centralised, cross-sector database of historical scam activity. Additionally, it can evaluate unstructured data (like chat logs or SMS transcripts) for linguistic red flags, allowing platforms to help intercept or detect potential scam indicators during the "grooming" phase before the payment is ever sent
2. Do we have to rebuild our systems or install new infrastructure to connect?
Scam Detect is built directly on our Modern FraudCheck platform, meaning you can easily connect using real-time APIs or asynchronous bulk SFTP batch uploads. For existing Modern FraudCheck clients, integrating is as simple as creating a connection and adding scam-specific data fields to your existing endpoints.
3. How is sensitive customer data and privacy protected?
Privacy is the core foundation of our platform. All Personally Identifiable Information (PII),including names, bank accounts, and phone numbers, is permanently masked and encrypted in transit and at rest. Furthermore, the system never shares raw customer PII back to other businesses, it only returns a de-identified Scam Risk Score (ranging from Low to Highly Likely) and incident metadata to help you make real-time decisions.
4. What is a Scam Risk Score?
Think of the Scam Risk Score as a real-time safety rating. It looks at transaction or interaction details, such as payee names, BSBs, bank accounts, or chat transcripts, and compares them against our secure, central database of confirmed scam activity. The system then returns a quick, clear risk category, ranging from Low to Highly Likely, so you can flag or pause a suspicious transfer to help prevent funds from leaving an account.
5. How does Scam Detect help our organisation comply with the Scam Prevention Framework (SPF)?
Under the government's mandatory Scam Prevention Framework (SPF), regulated entities in the banking, telecommunications, and digital platform sectors face strict responsibility, potential arbiter liability and heavy fines failing to prevent scams. Scam Detect can help satisfy SPF obligations by moving your risk strategy from passive post-incident investigation to proactive, real-time blocking to help prevent financial loss.
6. Can Scam Detect evaluate unstructured data like chat logs or text messages?
Yes. Unlike legacy systems that rely strictly on structured fields or static lists, Scam Detect uses advanced AI models designed to evaluate unstructured data footprints, including chat transcripts, SMS text, and document screenshots. It detects conversational drift and subtle linguistic hallmarks of social engineering, such as fake urgency, platform-evasion tactics, or coaching against safeguards, during the grooming phase of a scam.
7. How does the cross-industry consortium data model work?
Scammers frequently hop across boundaries, using telco networks, social media platforms, and multiple banks to execute their schemes. Scam Detect aggregates real-time contributions from telecommunications companies, digital platforms, banks, and the National Anti-Scam Centre. If an account, phone number, or identifier is flagged as high risk in one sector, it can be recognised across the entire network.
8. Does Scam Detect replace our existing fraud prevention systems?
No. Scam Detect is not designed to replace your current fraud prevention processes. Instead, it acts as an additional, intelligent layer of security integrated directly into your existing infrastructure via real-time API or bulk SFTP batch processing to proactively alert you to high-risk interactions.
9. How does Scam Detect go beyond simple account name matching?
Traditional checks on payment rails often only perform basic name and BSB validation to check if details match what is on file. Scam Detect goes significantly further by cross-referencing destination details in real time against a centralised, cross-sector database of historical scam activity, flagging accounts previously linked to fraud such as crypto or romance scams.