Op-Ed: Why Lending Needs A New Trust Model

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This story features COMMONWEALTH BANK OF AUSTRALIA.
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Traditional documentation checking practices are becoming increasingly more unreliable, posits Digidentity's MD Fred Slikker, so what's the Finance industry's response?

The end of “show me the document”; why lending needs a new trust model 

By Fred Slikker, Managing Director at Digidentity 

The alleged $1bn mortgage fraud linked to Commonwealth Bank ((CBA)) is not just a case of bad actors slipping through the cracks.

It points to something more structural, a growing mismatch between how financial information is verified, and the environment in which that verification now operates. 

At its core, modern lending still relies on a simple premise. If a borrower can produce the right documents, payslips, tax returns, accountant letters, then those documents can be used to assess income, affordability and risk.

That premise is beginning to break down. 

For decades, verification has been built around documents that are assumed to be hard to take. While fraud has always existed, it typically required time, effort and a level of sophistication that limited its scale.

Generative AI, however, has changed that equation. 

Today, highly convincing financial documents can be produced quickly and at scale. The issue is not just volume, but quality. Synthetic documents are increasingly indistinguishable from genuine ones, even under close inspection.

This creates a fundamental problem. Traditional verification methods, whether manual review or automated checks, were never designed to detect perfectly fabricated content. They rely on identifying inconsistencies or anomalies.

But what happens when those inconsistencies disappear? The outcome is a system that is still asking the question; does this document look real? 

From appearance to provenance

In an environment where appearance can be engineered, trust needs to be anchored elsewhere. The more relevant question is not whether a document looks legitimate, but whether its origin can be proven. 

This is where cryptographic verification begins to shift the model.

When a document is issued with a qualified electronic seal (eSeal), it carries a digital signature that confirms who created it and whether it has been altered.

Any tampering invalidates that signature.

This changes the nature of verification entirely. Instead of interpreting a document, the recipient can verify its provenance.

It’s a subtle shift, but a critical one. Trust moves from visual assessment to mathematical certainty. 

Pressure from regulation and scale 

At the same time, the regulatory landscape in Australia is evolving. Anti-money laundering obligations are extending beyond banks to a wider network of intermediaries; mortgage brokers, accountants, legal professionals and others involved in financial transactions. 

This expansion, however, introduces a practical challenge. Many of these organisations were not built to run compliance-heavy verification processes at scale. They lack the infrastructure and resources of large financial institutions, yet are increasingly expected to operate with similar levels of assurance. 

Extending existing document-based processes across this wider ecosystem risks increasing friction without solving the underlying issue. More parties collecting and reviewing the same documents does not make those documents more trustworthy.

If anything, it exposes the inefficiencies of the current model. Information is duplicated, stored across multiple systems and repeatedly requested from customers, while the quality of that information becomes harder to verify.

A shift toward verified data 

A different model is beginning to emerge, one that shifts verification upstream. Instead of relying on documents provided by individuals, verified information can be issued directly by trusted authorities, such as tax offices or financial institutions, and held by individuals in secure digital wallets.

These digital credentials are cryptographically signed, allowing any authorised party to verify their authenticity instantly, without needing to assess the document itself.

Importantly, they can also be shared selectively, meaning only the data required for a decision is disclosed.

This reduces both operational burden and data exposure. Intermediaries can meet their obligations without building complex verification systems or storing sensitive information, while customers retain greater control over what they share.

What comes next for lending 

In lending, the implications are significant. Many credit decisions today are still based on self-reported data supported by documents that can, in principle, be manipulated.

As synthetic fraud becomes more sophisticated, this introduces both financial risk and additional friction for legitimate applicants.

A credential-based model changes the starting point. Instead of verifying claims after the fact, lenders receive information that is already verified at source.

There are early examples of this model beginning to emerge. In the Netherlands, for instance, a government service is piloting how verifiable credentials can be used to share sensitive but high-value documents, such as income data, in a more secure way.

Individuals can obtain a declaration of income directly from the tax authority, issued as a digitally signed credential and shared with lenders or landlords when needed. While still in pilot, it points to how verification could become more immediate and reliable, reducing the risk of document fraud at the point of application.

Australia is not yet at this stage, but the direction is clear. As regulatory expectations increase and the limitations of document-based verification become more visible, the need for a more robust trust framework will continue to grow.

Documents will not disappear overnight. But their role as the primary mechanism for establishing trust is weakening.

In a world where anything can be made to look real, the future of lending will depend on something more fundamental: the ability to prove it.

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