Digital property marketplaces have transformed how homes are discovered, financed, and transacted. In Australia and the broader Asia-Pacific region, buyers and sellers now expect seamless online experiences supported by powerful search, negotiation tools, and digital document workflows. Yet, while platforms have excelled at usability and scale, trust and security architectures have lagged emerging fraud patterns, from traditional wire diversion schemes to AI-enabled impersonation and fake listings.
Recent Australian reporting has highlighted how devastating these risks have become. Surveys of recent property purchasers found that 97 percent failed to spot obvious markers of settlement scams in email communications (popular property scam on rise as nearly every Aussie fooled -realestate.com.au), even though such scams have resulted in hundreds of millions in reported losses and multiple six-figure individual cases, with criminals impersonating real estate agents and conveyancers to redirect funds.
To protect users while enabling continued digital transformation, proptech platforms must rethink their core security architecture, moving from reactive detection to integrated trust systems that anticipate and mitigate three modern fraud vectors: payment diversion (“wire fraud”), AI-enabled identity impersonation, and fake AI-generated listings.
1. Payment diversion: The “last-mile” attack on transactions
In Australia, settlement scams often exploit the moment when parties are about to transfer large sums. Criminals intercept email threads between buyers and conveyancers or agents, then supply falsified bank account details directing the funds into accounts they control. This payment redirection fraud is especially damaging because it occurs near the completion of a property transfer, with victims often unaware until after funds have left their accounts.
These attacks thrive on systemic trust assumptions: that email is a safe communication channel, that users will manually verify bank details, and that changes in payment instructions occur legitimately late in the process.
Traditional defenses – warnings, manual checks, or static fraud rules are inadequate against automated, targeted scams that mimic legitimate communications. To improve safety, marketplaces must embed payment integrity into their transaction flows:
- Cryptographically-signed payee artefacts: Treat bank details and payment endpoints as verifiable, immutable assets linked to authenticated profiles and signed by the platform.
- High-risk change gating: Regulate modifications of payment instructions as “high risk events” that automatically require multi-factor, cross-channel confirmation before any funds are released.
- Context-aware workflow binding: Tie payment instructions to specific transaction identifiers, restricting how and when they can be acted upon.
This approach shifts security from “detect then alert” to “prevention via enforced workflow integrity”. It ensures that the platforms themselves verify that funds are being directed to where they should be, rather than relying on emails or PDFs exchanged outside the platform.
2. AI-enabled impersonation: Identity as continuous assurance
Where static identity checks once sufficed, they no longer do. Advances in generative AI have made synthesized voice, video, and written impersonations accessible to fraudsters. A scammer can convincingly mimic an agent or a conveyancer, complete with signatures, messaging style, and context, without ever compromising backend systems.
The problem is that most platforms treat identity as a one-time affirmation at signup or occasional re-verification. Yet fraud often strikes at unpredictable moments, when a payment is due, a document is signed, or a deadline looms.
To address this, proptech marketplaces must adopt continuous and adaptive identity assurance:
- Step-up verification at risk events: Instead of one-off checks, identity verification should be invoked dynamically. For example, when a new device is used, when financial instructions change, or when sensitive documents are exchanged.
- Behavioral binding: Integrate device signals, behavioural patterns, and session histories into identity scoring so that deviations trigger increased scrutiny. • Secure, in-platform communications: Reduce dependence on external channels like email, which are easily spoofed, by enabling rich, audited messaging within the platform.
AI plays a dual role here. While it enables more convincing impersonation, it also enables agentic risk orchestration systems, AI modules that can assess multiple signals in real time, make contextual risk decisions, and enforce appropriate workflows before allowing a transaction to proceed.
3. Fake AI-generated listings: Provenance at the source
Fraud isn’t limited to the backend; it often starts at the front door. Fake or manipulated property listings, often generated or enhanced with AI, have proliferated alongside the growth of digital property platforms. These range from entirely fabricated rental or sale properties designed to extract deposits upfront, to subtly AI-altered photos and descriptions that mislead prospective buyers and renters.
Manipulated listings misallocate attention and trust, turning user discovery journeys into traps that funnel unverified users into conversations and transactions outside of monitored environments.
Combatting this requires proactive content verification and provenance systems:
- Source metadata and footprint analysis: Track the origin of listing media and text, including imaging metadata and creation context, to detect anomalies. • Cross-platform pattern detection: Use scalable analytics to identify duplicate images, repeated descriptions, or inconsistencies across listings that may indicate fraud.
- Verified lister identity: Assign trusted identity badges only to users who have completed rigorous verification, including licensing checks for agents.
Again, this isn’t about manual review. Advanced AI systems can assess visual and textual signals simultaneously, generating structured confidence scores that trigger further verification or automatic holding patterns before a listing is published.
Agentic AI as a trust control layer
The three challenges above, payment diversion, impersonation, and fake listings share a common theme: they exploit trust assumptions rather than technical vulnerabilities in encryption or protocol. Fraud isn’t breaking the system; it’s convincing humans and automated processes to act on false premises.
This is where agentic AI becomes a foundational control layer rather than a bolt-on classifier:
- Signal aggregation: Continuously ingest indicators from communications, identity events, payment data, and content metadata.
- Contextual risk evaluation: Score each event based on multi-modal data and historical patterns.
- Automated action: Enforce platform policies by gating actions (such as payment initiation, document signing, or listing) until required conditions are met. 4. Explainable auditing: Produce logs and rationales that support compliance and dispute resolution.
This approach transforms security from a reactive firewall into an embedded behavioral guardian, improving safety without degrading user experience for legitimate transactions.
Towards secure digital property markets
In Australia, the real estate sector’s increasing exposure to impersonation and settlement scams, often involving email hijacking and false billing requests, highlights the urgency of architectural reform. While APAC markets remain diverse, the underlying fraud mechanics
are consistent: opportunistic actors leverage convenience and urgency to disrupt high-value digital workflows.
Proptech platforms that embed trust as a product feature, enforcing identity checks, payment integrity, and content authenticity within their core architecture, will not just reduce loss; they will strengthen user confidence, compliance posture, and long-term ecosystem credibility.
In the future, the key differentiator won’t be who lists the most properties or collects the most users. It will be who can reliably answer:
Can I trust every communication, instruction, and every counterpart before I act?
By repositioning fraud defense as structural trust engineering, marketplaces can safeguard digital property markets while enabling the next wave of innovation.
Ravi Velampally is a technology founder and seasoned real estate investor working at the intersection of artificial intelligence, digital trust, and regulated marketplaces. He focuses on how AI-driven decision systems and verification frameworks can reduce risk and improve outcomes in high-stakes domains such as property, finance, and professional services. His work explores how platforms can move beyond lead generation toward a trust-first, outcome-oriented infrastructure.
Ravi is the founder of HBN-Tech (Home Buying Network), an Australian PropTech platform applying AI and digital trust principles to the home-buying journey. He has over a decade of experience across enterprise technology, financial systems, and digital transformation initiatives.
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Featured image: Parth Savani on Unsplash
Why digital trust Is becoming critical infrastructure for high-stakes transactions