Tax evasion goes far beyond legal tax avoidance. It is the practice of illegally hiding income or profits to avoid paying tax, costing countries over $500-billion a year. In many cases tax evasion depends on opaque ownership structures that hide who really owns assets, where transactions are flowing, and other patterns of criminal behaviour.
Bradley Elliott, CEO of RelyComply
With tax filing season in full swing in South Africa, it is an issue that’s top of mind for the country’s financial institutions (FIs). They are under pressure to step up anti-money laundering (AML) measures to uncover hidden ownership, complex company structures, and offshore accounts designed to hide taxable profits.
For regulators, this is not simply about chasing down individual offenders. It is about changing weak compliance systems that let these crimes happen and closing historical counter-intuitive gaps between AML and tax compliance. Every FI is liable, with regulators and governments demanding advanced efforts to address evolving tax evasion methods.
How tax crime links to money laundering
This trend is powered by developed understanding into where tax evasion and other fincrimes like money laundering connect. Before 2012, tax crime and money laundering were seen as separate issues. That changed when the Financial Action Task Force (FATF) made tax crime a recognised trigger for AML reporting.
It makes sense when the methods employed for tax evasion and money laundering often overlap:
- Hiding the source of assets
- Creating complex layers of corporate ownership
- Moving small amounts through multiple deposits
- Mispricing goods
- Using intermediaries, offshore havens, and cross-border loopholes
As these tactics exploit differences between countries’ tax laws, it’s hard for traditional AML processes to detect them. Poor AML checks and weak regulation can’t uncover and hinder hidden ownership structures that are growing in sophistication in today’s interconnected global economy.
The role of hidden owners (UBOs)
Even though FIs around the world meet the requirements of FATF’s frameworks for bettering financial institutions’ know your customer or business (KYC/B) processes, ultimate beneficial ownership (UBO) structures are often opaque and not thoroughly interrogated.
A UBO is the person who truly benefits from or controls a company. In South Africa, the Companies Act defines a UBO as anyone with at least 5% ownership or control.
They may choose not to reveal their identities for multiple reasons, such as to avoid cyber or physical threats or to hide partnerships and business strategies from industry competition. They do so in a variety of ways:
- Using nominee directors or shareholders
- Setting up shell companies in secretive jurisdictions
- Reporting activities in one place and ownership in another
- Creating complex trusts with shifting beneficiaries
Rules for UBO transparency vary widely worldwide, from public registers in some countries to private databases in others. South Africa has a central but non-public registry, accessible to regulators and law enforcement for instance, where these differing requirements can enable tax evaders to operate across multiple jurisdictions and avoid being found.
How regulators are responding
FATF and the OECD are pushing for better UBO transparency at the KYB stage, and for more sharing of information between tax authorities and AML bodies to lower risk in areas such as tax evasion or money laundering. Regulators are now checking if companies declare UBOs and whether those declarations are truthful.
Unearthing shell layers and flagging operations in known tax havens takes the kind of advanced data processing and intelligence sharing that are integral to institutions’ modern AML capabilities. Advanced AI-driven technology can help spot suspicious patterns, and is fast becoming a cornerstone at tax authorities:
- The IRS in the US flags error-prone returns
- HMRC in the UK detects mismatches between income and lifestyle
- SARS in South Africa uses machine learning to recover billions in unpaid tax and find unregistered taxpayers
Why the fight against tax crime is failing
While this tech adoption for tax may highlight a positive outlook, we’re still far from solving the UBO conundrum.
Tax crime and money laundering are no longer separate problems. FATF made sure of that when it recognised tax crime as a predicate offence for AML. However, even with that knowledge issues around UBO data, shell companies, and complex entity structures continue to undermine compliance efforts. While some UBO data exists, its integrity is shaky at best because the very companies being monitored are often the ones self-reporting it. It’s the equivalent of marking your own homework, and criminals know it.
Solutions on the market still struggle to reliably identify shell entities. In practice, this leaves buyers exposed to more manual review and greater compliance risk, while dealing with false positives, cross-border verification challenges, and difficulty in UBO identification. Confidence in managing this risk is therefore strikingly low.
These problems will be expected to escalate when the reality is that today’s Business Entity Verification processes are outdated and ill-equipped to meet the next wave of compliance and identity verification challenges. The industry often points to progress with more registers, rules, and even more reporting, but the core problems remain unchanged: incomplete data, fragmented oversight, and a system that reacts rather than anticipates. These blind spots aren’t accidental but severe weaknesses actively exploited by bad actors.
True progress goes beyond incremental fixes which demands cross-border collaboration, and harnessing tools that cut through the noise instead of adding to it. Of course, technology has a role here, not as a silver bullet with a “future-proof” label slapped to it – but as a way to interrogate what’s really happening behind the structures and patterns that don’t make sense.
If the industry wants to be serious about tackling the intersection of tax crime, money laundering, and shell companies, it needs honest recognition of the weaknesses in current data and to stop pretending the current system works. That critique may be uncomfortable, but it’s the only place where real solutions can begin to take form.