When an Uber driver in Sydney successfully impersonated a law firm partner and defrauded a client of more than AUD $200,000, the story made headlines not just for its audacity, but for what it revealed about modern trust failures. The case highlighted how easily identity, credibility, and authority can be spoofed in systems that still rely heavily on informal verification and personal assumptions.
While the incident occurred in a legal context, the underlying issue extends across other high-stakes domains, particularly property and financial transactions, where decisions are infrequent, complex, and emotionally charged. Despite advances in digital platforms, the mechanisms for establishing who can be trusted and why remain surprisingly weak.
This gap presents both a risk and an opportunity for technology platforms operating in regulated, trust-sensitive industries.
Trust is not a feature – it’s infrastructure
Much of the digital economy has focused on efficiency: reducing friction, increasing access, and accelerating transactions. Marketplaces and platforms excel at matching supply and demand quickly, but speed alone does not create safety or confidence. In fact, when identity and credibility checks lag user experience design, efficiency can amplify risk.
High-stakes transactions, whether legal, financial, or property-related, require a different design philosophy. Trust cannot rely on branding, referrals, or static credentials. It must be verifiable, contextual, and continuously updated.
This distinction is critical. A licence, registration, or title confirms eligibility, not suitability. Yet most platforms still treat compliance as binary rather than as an evolving signal tied to outcomes and behaviour.
From listings and leads to decision infrastructure
Property technology offers a clear example of this shift. Early PropTech innovation focused on listings and price transparency, unlocking valuable market data for buyers and sellers. More recent platforms moved toward lead generation, monetising buyer intent by selling visibility to professionals.
While effective for scale, these models prioritise attention over outcomes. Buyers are left to infer trustworthiness from reviews, marketing language, or referrals, the same informal mechanisms that fail in other sectors.
A newer class of platforms is now emerging with a different goal: helping users make better decisions, not just faster ones.
These platforms aim to orchestrate the choice of professionals, mortgage advisors, real estate agents, conveyancers, by combining regulatory verification, contextual matching, and performance-linked signals. The intent is not to replace professionals, but to reduce information asymmetry before commitments are made.
AI’s real role: context, not automation
Artificial intelligence is often framed as a tool for automation or prediction, but in trust-sensitive environments its more powerful application lies in contextualisation.
Rather than ranking professionals by popularity or proximity, AI systems can assess fit, aligning a user’s financial profile, risk tolerance, timeline, and transaction complexity with professionals who have demonstrated capability in similar scenarios. This moves matching from generic recommendations to evidence-informed guidance.
Crucially, this approach also makes trust explainable. Users can understand why a recommendation surfaced, which inputs were considered, and how those signals relate to their specific circumstances. This transparency is essential in domains where accountability matters.
Why this matters beyond property
The relevance of trust infrastructure extends well beyond real estate. As more economic activity moves online, identity spoofing, credential misuse, and misrepresentation are becoming systemic risks. The Sydney fraud case is a reminder that digital convenience without verifiable trust creates vulnerabilities across sectors.
Globally, regulators and institutions are paying closer attention to how platforms intermediate trust. Expectations around auditability, explainability, and compliance are increasing, particularly where financial harm is possible.
Platforms that proactively embed these principles will not only reduce risk but also gain strategic advantage. Trust-first systems are harder to replicate, slower to commoditise, and more aligned with long-term market health.
Designing for confidence, not clicks
Technology companies entering trust-sensitive industries face a choice. They can optimise for growth metrics like clicks, leads, impressions or they can optimise for decision quality and outcome confidence.
The latter is more demanding. It requires deeper integration with regulatory systems, more nuanced use of data, and a willingness to accept slower initial growth in exchange for credibility. But as markets mature, these platforms are better positioned to become default decision layers rather than interchangeable intermediaries.
Looking ahead
As transactions become more digital and more complex, the cost of misplaced trust will continue to rise. Whether in legal services, finance, or property, the next generation of platforms will be judged not by how quickly they connect users, but by how reliably they help users choose.
AI and digital trust infrastructure together offer a path forward, one where technology strengthens accountability instead of eroding it. The opportunity now is to design platforms that make confidence scalable, rather than optional.
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 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.
Rather than operating as a listings or lead-generation portal, HBN-Tech focuses on connecting buyers with verified property professionals through suitability-based matching. Licensing verification, compliance checks, and secure digital workflows are embedded into the platform to reduce reliance on opaque referrals and informal trust signals.
The platform treats trust as a system, not a marketing claim. and positions AI as a decision-support layer that augments professional expertise. While still early, this model reflects a broader shift in how platforms are being designed for regulated markets.
TNGlobal INSIDER publishes contributions relevant to entrepreneurship and innovation. You may submit your own original or published contributions subject to editorial discretion.
Featured image: Max Böttinger on Unsplash
AI, drones, and renewable energy: Accelerating sustainable power in developing regions