I have heard across the debt market that APRA has made two small but significant changes to how banks manage securitisations via APS 120. This may just kill Australian Lending FinTech.

End of Fintech

Two adjustments to the regulatory guidance have created new issues for those looking for warehouse financing or securitisation capability:

  1. APRA will limit any tranche retention in a securitisation to 20% notional of a senior tranche. In other words, a 20% senior exposure cap on what a bank can retain in a securitisation. There is a particular focus here because APRA doesn’t believe retained senior AAA and selling junior mezzanine tranches is a suitable risk transfer (ie. capital relief securitisation is not suitable).
  1. There is a loan warehouse growth cap of the lower of a bank’s investment home loan growth and the 10% APRA cap applied to investment home loans.

Major issue:

This will significantly impact all Lending FinTech at a later stage. I can’t see anyone scaling without either a banking licence or securitisation or probably both.

Funding Growth:

Lending FinTechs don’t have direct access to deposits, so if they want to scale and be cost effective, their only options are to tap into capital markets or other lenders’ balance sheets. And the most cost-effective way to do this is through bank warehousing facilities or loan securitisation. Further:

No “Unicorn” Lending or P2P FinTech exists (with the exception of RateSetter) without a publicly announced securitisation program.

See our table if you need further evidence:


Latest Valuation (USD) Securitisation Name


$1bn+ KABB


 $1.9bn+ CHAI
Funding Circle  $1bn+


Avant Credit

$2bn+ AVNT




OnDeck  $1.5bn


Lending Club  $7.2bn*


Zopa $760m+


Given lack of local Australian senior tranche or AAA buyers via capital markets (just 8% of super fund assets goes into non-bank fixed income, source: OECD), FinTechs would be forced towards the 4 Aussie majors. Overseas wholesale is just too much for a young local FinTech with a local product, plus currency swaps are an extra cost and complexity.

But now a growing FinTech cannot obtain funding from a major bank, as the capital relief type securitisation is now blocked. And don’t think you can try to run a book with 10 different senior tranche investors: new deals need to be especially “clubby”.

What about warehousing?

Warehousing is another option that will be constrained, although if there is not going to be a longer term securitisation market, the demand for warehousing might also fall. Warehousing is a stop-gap: helping non-banks get to scale before a securitisation. If this is now capped, then FinTech growth is now capped. I would also expect Australian banks to service existing clients with scale over high growth yet still small FinTech who might have longer-term payoff.

What Else? No deposits. No Collaboration.

Looping back, APRA has not showed any a desire to open up access to banking licences to FinTech (ie. direct deposit access) and the Federal Government has not got the appetite to provide support in the same way as the UK government did via buying loans or credit guarantees.

This, coupled with a low appetite for banks to engage with FinTech, has made life significantly harder.

What’s left?

In my opinion, not much. Small FinTechs with small warehousing facilities may exist but the ability to scale has now gone. Overseas FinTechs, who have already scaled, can come and then distribute securitisations given the bigger brand name and their local markets, notably the US, China and Europe.

Now APRA will have other reasons for its actions, so this isn’t a criticism of them. They need to manage Australia’s banking system. But this current approach appears to be pushing against where other global regulators and governments are going.

As it stands, the point of Lending FinTech was to bring technology and data into finance, which includes risk management and credit scoring processes for better borrower outcomes. This would push major banks harder, increase competition and, ultimately for Australians, create a financial services industry that we can export globally in the same way we consume a huge amount tech from Silicon Valley technology companies.

But it looks like APRA has now taken the Tech out of FinTech.



*We all know about Lending Club’s struggles over the last 2 years.

Read More

Beating the Robots


As a FinTech founder and mathematician, I should probably favour algorithms over human assessment. I often do. However I am getting tired of hearing about yet another algorithm that is able to predict, forecast and act on data to make financial decisions. Ultimately we’ve seen it before. Will the algorithms and robots win or does human analysis shine through for another business cycle?

This story starts with a cornerstone of basic AI and automated credit decisioning. Algorithms are built that take a lot of consumer data and perform analysis called logistic regression to develop a “good-fit” model. The observed data is then used to produce an equation that prescribes an expected behaviour. For lending, this is a probability of default forecast.

My first skepticism is due to my mathematics background. I loved engineering mathematics and fluid dynamics, when pure mathematics is used to derive equations that are fundamental to the development of aircraft, cars and all kinds of other high technology equipment we have today.

This mathematics saved time: a single equation developed through algebra meant we had ways to describe what would happen. The human mind equipped with paper was able to forecast without calculation.

As time went on, computational ability improved. In non-linear dynamics, we became interested into when and where systems went from calm to chaos. In effect, we wanted to know when our derived equations became unstable. Computational methods appeared that were able to map out these cases and build up smart eco-systems which then formed a more accurate forecast of reality than the human generated equations.

In the 1970s we started applying the initial human derived models without computational power to start predicting really annoying things like traffic jams. Over time, computers were introduced and we learnt that the computational part was much more influential that the initial models we derived: the human equations mattered less than the rapid ability for a computer to estimate all the potential ways the system would break down into a traffic jam – and then alleviate those risks. My last piece of research on this was in 2005 and computer power has move on exponentially.

Then in 2006 I did what all reasonably good engineers did: I went to work for Wall Street. Same equations and a real application of the mathematics (I failed to mention that the traffic jam research was mostly focused on driverless cars, something that will take another decade to become mainstream). Correlations, causations and logistic regressions where everywhere. In fact there was an attractive mix of human-built equations and computer generated ones. But the data source was bad: a long stretch without a recession and notably no significant house price corrections meant the instability was never tested. We know what happened next.

Moving forward to today, we see far less human developed equations. In any case, they have become far too complicated for many and I believe the variation of data in the world is significantly more important than the fine-tuning of the human equations, just like the traffic jam models.

But my concern now is about asking if these models are correct this time round? Can we trust the goodness of fit? Are sufficiently good stressed data sets used? Are people caring about the what-ifs? In my mind, the opportunity to beat the algorithms and robots is just as relevant as ever:

  • Australian banks view investor loans as less risk than owner-occupier loan. The data they use shows this. International evidence suggests this is not the case.
  • Data shows that SME lending is not worthwhile (and many banks have pulled back from this). But have they missed out on how new relationships can be formed or more suitable products can be developed.
  • Banks continue to adore their credit card businesses yet we all believe this rewards-based value capture will end at some point.

At the same time, we must also be aware that there will be human interference. Investor loans benefit from a tax break, SME lending will always be politically important and credit card use has been sticky.

The key to all this is not only capturing vast amounts of data but then finding ways​ that it can be effectively used to test your human or computer-generated equations but also how an artificially intelligent machine can adapt its equations when there will be known human interference, such as political and legislative changes.

This is how we can beat the existing robots.



Read More