Musings of a FinTech: Dynamic Pricing Part 2 (A Solution)

Earlier this week I presented a problem, that dynamic pricing was incredibly difficult within financial services where the transaction mattered. Here is one of our solutions. This is theoretical and multiple answers do exist.

Group Mortgages (Many Houses, Many Borrowers)

One of our long-term goals is a group mortgage, which straddles across both the sharing economy for wealth and cross-generational cashflow mismatches.

By creating groups of borrowers with a similar purpose (owning their homes), we naturally break down both the dead-weight loss segregation (we have created something unique to separate borrowers) and finite resource issues above (the group will have an open window with greater time constraints).

The final question here is does it add-value, either by reducing risks for lenders and lowering interest rates for borrowers? We think this is the case.

Why a group mortgage reduces the risk:

This is all about average loan-to-value ratios and resources (LVR or LTV, depending where you are from, and debt serviceability). In a portfolio of individual mortgages, average LVR is a bit false. The weakest borrowers, often with the higher LVR or high debt-load versus income, will usually drive the first losses as the riskier borrowers run out of financial resources first.

You can’t really look at average LVR and should look at the distribution of LVR, particularly when you tranche the portfolio for mortgages for a residential mortgage-backed securitisation, which ultimately drives the mortgage interest rate pricing if used as a wholesale funding source.

In a group mortgage structure, each borrower can cross-guarantee every other borrower. As such, the weakest borrower risk is removed as they can consume financial resources from the strongest borrower. The result is that you can use average LVR and average resources as a gauge for loss absorbing capacity. This will reduce the risk for the lender. The major question to answer here is, if the safer borrower will be willing to take part in this transaction with the other counterparties.

The answer to this is that they might, if either they can benefit from a reduced interest rate. The group of borrowers might need to organise the guarantee system to lower the borrower interest payments for the net guarantee providers, i.e. a payment for the having lower risk. Alternatively, the less risky borrower has the ability to acquire the financial resources from others if they provide support (in effect, covering mortgage payments buys into ownership of other homes). Here, we have outsourced some of the risk bearing a bank might take into the group (sharing economy). The natural alignment of a group mortgage comes into play when families or community groups band together to help each other out – in this scenario, we think people will be more open to share the risk.

Better without Banks?

If we can achieve this, we offer benefits to both lenders and borrowers and have found a more aligned method to create dynamic pricing, as risk has become a major influencing part of the transaction and other constraints become less of an impact.

The final delivery of this then reverts back to flight tickets. Whilst the window for a group is open, dynamic pricing can exist.

We expect this is unlikely to function within the banking system: rigorous definitions of residential mortgages exist and the transferability and cross guarantee start to look more like a commercial mortgage portfolio, which has significantly higher bank regulatory capital requirements due to default history.

However, achieving credit ratings and non-bank funding or equity is far easier if various parties can understand the reduced risk in this system, if sufficient diversification (or low correlation in borrowers) can be achieved. So the question regarding dynamic pricing: it might need to exist outside the banking system.

Caveats to this model:

  1. Borrowers would need to be willing and able to share financial risk and assets. This suggests fractional ownership of property
  2. Price growth assumptions, constant pricing data and commitments to service would be required – not trivial and better in a stable price environment (not high growth). This has a relationship to inflation but house prices are usually left out of those calculations.
  3. I’m still to decide if the higher portfolio concentration is a major negative: we remove the likelihood of a small number of defaults (ability to bail out a weaker borrower) but have increased the ability for an entire portfolio default (correlation goes to 1 in a crisis). Asset security helps here but most likely the mortgage debt would not be able to be irrevocable. Another positive to borrowers but a major problem for lenders. More work required.

Thoughts welcome.

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Musings of a FinTech: Dynamic Pricing Part 1 (The Problem)

Deadweight Loss (Wiki)

Dynamic pricing was one of our very first thoughts as an add-on: how do you develop mechanisms that create risk-specific loans prices. This also expands across to health insurance, wealth advisory and other financial services.

The Problem: charging different interest rates for similar loans based upon borrower risks isn’t easy. 

This sounds perfect. Use a Big Data engine to price loans that has parallels to Advanced IRB credit risk models (how banks measure risk themselves), insurance risk or other financial modelling on a per customer basis. Lower risk, by our own metrics, would lead to a lower interest rate or premium. However, we quickly decided it wouldn’t work and we have several reasons why:

  1. Customers will shop around

You are naïve to think that customers will not. They will, either through the lazy or delegated method (comparisons sites, mortgage brokers), deep research (gathering quotes from different lenders) or talking to people casually (this still happens!). Your risk specific pricing starts to fall apart as each lender has a separate and unique risk model. They do: look at the various bank Advanced IRB credit or statistical risk models and variation in what drives risk is evident.  As a lender, in a dynamic pricing universe, you’re mostly guaranteed to attract the customers where you have underestimated the risk (you offered them the cheapest loan).

  1. Financial resources are not finite

Price discrimination works well when the product or service runs out. Time is a great enabler (e.g. I must fly to Ireland on the 17th March), meaning you can discriminate. Loans, on the other hand, don’t always have time maturity, so the price doesn’t have to be monotonic increasing with an inverse time decay.

Borrowers can simply wait until prices fluctuate down or a lower offer comes along. All loans have mostly marginal cost (whereas flights or cinema tickets are mostly fixed and perishable).

  1. Customer segregation to remove the deadweight loss is harder in finance

Cinema ticket pricing is an easy starting point. Retirees and students, often with a lower ability and willingness to pay, obtain discounts whilst affluent professionals have to pay higher prices. This allows separate pricing. It is structurally much harder to split up pricing based upon risk: the customers cannot say to each other “hey, I’m a student, you’re not”. The division between low and high risk is often based upon wealth, which creates a dangerous PR mix and may not be legal. Friends also talk and they will quickly get annoyed with variability in offers as being unfair.

  1. People will think harder for the larger transactions

Mortgages and wealth products, where the accumulated interest charges, fees or potential outcomes are of a higher order than small items, are harder move pricing dynamically. Customers will do a greater level of research. Small items, such as annual car insurance premiums, are easier to play around with and may be purchased in one sitting. Less thought, more ability to be dynamic.

Look at the market

Aside from these above items, market structure can play a significant part. Is pricing determined on day one? Can it be changed over time by the lender (such as Australian home loans), by a formula (e.g. bank covenants) or do prices have annual adjustments (such as Australian health insurance)? These vary from country to country and have a similar impact as tax treatment does for wealth advisory.

Finally, financial products are also influenced by macro factors as well as micro. Long-term interest rates and business cycles play a very large part in this. Bank regulatory capital, a key part in loan pricing, should be measured on through-the-cycle metrics (such as through-the-cycle observed probability of default) whilst short term dynamic pricing may be heavily influenced by point-in-time. In other words, you shouldn’t be too dynamic as long-run trends are very important and pricing in the short-run will likely lead to problems if recession arrives.

There are several ways to break down the above problems. Deciding what you show your customers and what you don’t is a key part. You also need to pick the right market and right product to operate in.

Solution coming later this week…

Picture from


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Musings of a FinTech: Infrastructure and Market Entry

HBR Chart_effFront

I’m revisiting an old post in part due to reading an excellent article in HBR on disruptive innovation.

One particular point stood out and it was the chart showing how incumbents move from low profitability products (low-end) to focusing on high profitability products and customers (high-end). This makes complete sense: as you establish yourself and grow, your portfolio of customers should be streamlined to what makes the most money. Your business may have achieved the required scale to deliver the more profitable products. In lending, the obvious example is the mortgage product due the required scale for deposit financing. The profitability should be driven by Return on Regulatory Capital (RoRC), however other items always need to be considered.

I have said several times that SME or personal lending is not profitable through business cycles if the cycle times are short. During downturns, these portfolios experience heavy losses that take many years to recoup. I have always believed the magic number is 7 years (roughly derived from a 7%-12% loss in 1 year offset by 3%-5% pre-expected loss margin in other years). In the sense of thinking about lending on the efficient frontier, SME or personal lending is not on it whilst mortgages are close to the optimal point (see below, numbers in brackets are approximate net returns for a relative perspective only).


Due to this, the appetite to increase lending to this segment is limited: any additional lending is generally at the riskier end of this segment, meaning the time between recessions needs to be longer for it to be profitable.

On the other hand, mortgage lending is somewhat different. Highly collateralised loans, with loan-to-value ratios less than 70% need a hefty recession to really hurt. Think 40% decline in house prices as well as the recession’s severe default impacts. In other words, mortgages still make money in a modest recession.

This leaves a big opening for FinTech: SME or personal lending has a ready market. In a similar way to the HBR article, this is certainly the low-end of the market. Unlikely to be profitable through shorter business cycles or they have a terrible RoRC, hence why the opportunity is there. Peer-to-peer platforms have found an additional way to mitigate the recession cycle risk: decentralised risk taking.

Disruption occurs if FinTechs can climb up and offer products to the higher end of the market. But how can a FinTech entrant establish the infrastructure to offer more sophisticated lending products?

Why are mortgages more sophisticated?

This is less easy to explain but comes to liquidity. SME or personal loans are short term, such as a few month to a few years. This means the invested money is returned to the investor within a reasonable time and they are happy to leave it on the platform for a few years. Mortgages are very different: the time may be 25 or 30 years before it is a fully returns and with an average return time between 4 and 7 years (due to refinancing). This is a little too long for the crowd and requires more sophistication in the form of maturity transformation, an aspect of banking that is very important and a major failing through the financial crisis (credit crunch).

What can FinTechs Do?

  1. They will need to bulk up on skilled staff for one. If you don’t have a specialist risk and capital team, you will struggle to perform a maturity transformation.
  1. You can decide to not be a disruptor. Instead a FinTech can be a complimentary service to a particular bank and you attempt to disrupt together.
  1. Enter the higher end of the market (mortgages) with a low-profitability product (I have deliberately left out the description of this). Here you hope the infrastructure will develop around you or you have the ability to turn what looks unprofitable into something profitable through innovation (e.g. your data analytics capability).

Bigger questions come out of this:

  1. How does a FinTech diversify quickly enough. If it doesn’t, we may start to see a higher failure rate across FinTechs
  2. There is a shared incentive across lending FinTechs to build a shared deposit infrastructure to help them diversify

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Musings of a Fintech: Annuity Revolution

I woke up this morning full of inspiration. Forget mortgages, forget payments, we desperately need a new wave of annuity products. These fall under Life Insurance products, so we may need to widen our description from a Lending FinTech to an Insurance FinTech, if we actively pursue the creation of these products. Numbers below indicative…further modelling in progress!

 What are annuity products?

Simply, these offer a fixed payment (which can be inflation indexed…see our next post) until you die or a fixed period expires. As such, they are often used as post-retirement income products. The concept is simple: you want a fixed amount to live off but have no other sources of income. At retirement, you convert your accumulated wealth in your Super into an Annuity. Retirement is then determined by your Super’s size and your planned spending in retirement versus outlook on life expectancy and inflation.


Much of the above is driven by probability and Actuarial analysis. However, to keep things simple, lets assume you want a 30-year annuity paying $449 per month per $100,000. Much of the following is driven by assumptions, available investments, risk and financial models.

Other Assumptions:

  • Inflation will be ignored (it can be hedged with correctly priced equity)
  • Available fixed rate assets paying 4.8% per annum (net of expected losses and charges). Yes, they exist.
  • Protection capital of 10% (credit enhancement and regulatory capital)
  • Distribution and other upfront fees of 1.5% of the notional balance
  • Market rate fixed AAA RMBS yield of 3.5% (this feeds our fair-pricing of $449 per month per $100,000)


We find the above is possible to structure (woohoo). However, the variable component is the subordinated equity, otherwise known as the first-loss or equity position. Plugging in the above numbers into a residential mortgage backed security model (this isn’t simple), we obtain an equity return of 13.7% per annum.

In other words, this is fairly attractive dividend yield. Compare it to investing in major bank shares as it achieves similar returns for significantly lower risk (leverage of 10x on residential mortgages versus up to 40x for CBA, ANZ, Westpac and NAB). We could leverage up this 2x (20x overall) to get substantially higher returns.

Further, we can compare this model to Challenger Liquid Lifetime annuities. Without details on the actuarial modelling, I will assume the age 65 Nil inflation protection will pay for 30 years (the $4139 for males is $349 per month). This has a yield of 1.58% versus our 3.5% and we haven’t even included mortality rates in our model.

Finally, if we add in mortality, returns are stronger for the equity tranche. Taking 65 year old male but the expected longevity of a female to 87 years (i.e. 22 years), we adjust the above model and pay equity the remaining cashflows after year 22. The equity yield before leverage increases to 15.2%.

Put simply, we can get $100 more per month per $100,000 ($449 vs $349) for retirees PLUS significantly better Sharpe ratio returns for equity investors.

Next: more modelling, including inflation options, mortality curve and delayed drawdowns.


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Musings of a Fintech: Short Term Borrowing Growth Trap

If you want to learn about growth, neoclassical models, such as Solow-Swan , can a good starting point. You should also read about the Chicago School of thought. I’m not an economist. I’m a quantitative analyst, bond structurer turned entrepreneur. I usually take all the above and throw it out the window. Partly due to laziness but also from the endless arguments between economic camps – how come they both win the Nobel prizes but still can’t agree on a single item. However the following just seems to make sense to me

For me, growth is a function of population, inflation, innovation and enterprise, random processes and interest rates. This is in some undefined and ever-changing format.

We can roughly forecast population and eliminate random processes through averaging (not always the best thing). Inflation, even if poorly calculated (not including property) can be estimated and removed. Innovation is also random. This leaves me with interest rates and the focus of this article.

I fundamentally believe each person has the capacity to maintain a level of debt, and for arguments sake I will assume this is 30% of gross pay (or approximately 50% of net pay). This is a maximum level that I believe to be stable, any higher and the borrower will default with almost certainty. This isn’t a recommendation.

An interest rate reduction by a central bank, is, therefore an increase in the ability to service new debt if the old debt’s interest rates are floating liabilities (we move further away from my chosen limit). Fixed debt just takes longer to re-price as it matures (or is prepaid, depending on call options). Lower interest rates also suggest, often, that inflation might increase in the future – pulling forward consumption before prices increase. The natural incentive is put in place for people to spend today.

The Reflation Trade:

After the near-collapse of the financial system in 2008, many investors got on the reflation trade: monetary policy aimed to increase the prices of financial assets to not only spur bail-out bank balance sheets but to lower long-term interest rates to increase investment and consumption. This worked well and only really stopped in the US when the Federal Reserve raised rates in December. Japan, the UK and the ECB are still in an easing mode and Australia has more potential scope to cut rates than increase them.

In short this has several impacts:

You borrow to buy assets, the price increases. The obvious asset class is property but this also included in investment and consumption. Interestingly, there has been deflationary pressure at the same time as asset price increases (remember, property is not included in inflation, except for interest rate dependent debt servicing in some countries). Deflationary pressure comes in many forms but an increase in the supply of products and innovation have been factors (innovation including digital products and manufacturing processes). Also remember that inflation has a downward bias as the technology level increases (if the megapixels on a camera double, that is counted as a form of deflation).

Why is the US ahead of the curve?

Firstly, they were more aggressive and much earlier in monetary policy. Assets reflated much faster and some of the financial crisis damage was repaid quicker (e.g. bank balance sheets). In many ways, the US has a better balanced economy, with global tech businesses mostly being based there.

In my mind there is another reason: the consumer can borrow for 25 years with certainty and flexibility.

You can get a 25-year mortgage at a fixed (and very low) rate and still have the ability to refinance even lower or repay when you see fit. This allows the consumer to have greater confidence in their expenditure.

In the rest of the world, this doesn’t happen. You can fix for 5 or perhaps 10 years maximum in many countries but are unable to prepay without hefty fees. The result is that the consumer is then stuck on short-term floating debt and knows they cannot borrow to capacity as they need to be aware of potential interest rate increases (but this actually stops them happening, the fear is the important factor here). Animal spirits cannot occur.

Borrowing from the future?

In short, interest rate declines and locking in long-term low rates is borrowing from the future. This is the whole point: whilst we are low on innovation, population growth or something else random, we need a stimulus as a steady growth rate is better than unstable volatile swings.

Lehman versus Layman:

Interestingly, US banks failed not from their dodgy sub-prime assets directly. It was the short term borrowing that led to their downfall (i.e liquidity). Yes, liquidity was due to shitty assets but the short term borrowing was the reason. It appears the US has learned – albeit it was easier due to the structure of their consumer debt markets.

The rest of the world has passed on the short-term borrowing risk straight to the public.

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Musings of a Fintech: The Amazon Moment for Mortgage Brokers

My good friend Sharon Lu seeded an idea into my mind and now it is time to discuss!

Regardless of your views of Amazon, as a company it has caused some momentous shifts across multiple industries. I am calling these shifts an Amazon Moment and believe the same thing will happen to the Australian mortgage broking industry. Although Amazon hasn’t fully arrived in Australia, across the world it has changed many things:

  1. It forced many retail shops to close or change their business models
  2. It revolutionized online retail along with a small select group of firms
  3. It made books cheaper for the everyday consumer
  4. It allows people to buy thousands of items online with next or same day delivery from the comfort of their own home

The major problem with retail in the modern day is that there are natural mismatches that lead to obvious inefficiency:

  1. A large physical store where the consumer is only interested in a small selection of products (size, colour, use, brand are just some of the factors)
  2. A poor matching of supply and demand (shops open in the day, people’s free time is generally in the evenings) and seasonality.
  3. An inventory and working capital problem. People want to buy from a physical store if it is in stock, otherwise they see no advantage against ordering online (so physical stores need to hold more stock).

The overall result is that physical stores represent a form of sales and marketing but have limitations in that they are expensive and inefficient. The result is that a consumer will pay more to visit a physical store when they don’t know what they want to buy (spur of the moment), need deeper inspection (quality control) or want the product immediately (coffee).

Amazon did well as it identified a few things:

  1. People might have an idea of what book they want to buy: the latest trends (Harry Potter), the top ranked, the life-related (I’m currently reading about fatherhood)
  2. Physical quality inspection for books isn’t required in most cases: they are paperback (or were, digital is now here)
  3. They are not needed immediately (except the airport purchases). You can’t read a book in a day, so you can buy ahead of time in many cases.

The result was that a market for online or automated sales of books was there and consumers would buy it. Amazon won. Once it was able to achieve scale, the ability to cross into other areas made sense by reinvesting the money saved on physical stores into improving the supply chain: Faster delivery and better stock management.

The overall result: Traditional bookshops who were unable to add value where Amazon played soon lost out. Not every bookshop closed but they had to offer more – better spur of the moment locations and products, better research into what is a good read (quality advice) and older or rarer books (need physical inspection). This is creative destruction: a better service is offered by a new entrant and existing players also need to improve to keep up.

So how does this translate into an Amazon Moment for Mortgage Brokers?

Firstly, some basic facts about mortgage brokers:

  1. They sell a widely viewed commoditised product (although this is questionable for reasons below)
  2. They have a high cost of use at 1.3% on average of the loan balance (relatively high sales and marketing cost)
  3. Supply doesn’t match demand: it suffers from seasonality as well as poor conversion rates
  4. High salary/cash commission costs

So what would happen if the 1.3% cash commission (which is a sales cost similar to the physical store above) is used for something else: a better credit supply chain in the same way Amazon changed the physical product supply chain.

Supply chains in finance are less obvious. In the most simplistic form, someone somewhere usually has some cash. They invest it (buy shares) or save it (deposits). This feeds through the financial system into a bank and then gets loaned in the form of a residential mortgage.

The bank can make a few choices here. What a deposit holder wants is 100% certainty of their money back, whilst the home loan borrower has some risk: they may default. The bank equity usually holds the difference (is a risk bearer). However a number of other risks also exist and are simply not managed by the expert (the bank): interest rates change when a central bank alters rates or market sentiment changes them. These fluctuations are extra risks and are shoved onto the borrower, who doesn’t have the capability to manage the complex risk.

So what happens in Australia with these extra risks?

Australian loans are either variable rate or fixed for a period. There are very small but hugely significant differences in these loans to the rest of the world:

  1. Variable loans in Australia allow a bank to re-price loans at their discretion. Recent examples of new capital charges to banks leading to price increases straight away. Consumers get a bad deal.
  2. Fixed loans in Australia have only a limited fixed period (mostly up to 5 years). They also have high break costs if a borrower wants to repay early and interest rates have dropped (and banks won’t reward people who repay earlier if rates go up, so this is unfair!). Overall, borrowers face interest rate risk: they either get stuck in mortgage, have to pay to get out and the fixed period isn’t long enough to protect them for the life of the loan, say 20 years, if interest rates increase over the long term..

Both of these loans are then pretty risky to a borrower if something harsh was to happen to the economy: banks would shovel costs onto borrowers or their fixed periods would lapse (and you can’t lock-in rates before the expiry of old fixed periods as you would need to pay extra costs!).

The simple solutions: make loans better by redesigning the supply chain and adding in extra features that make these above risks disappear. The loans themselves aren’t a new invention either:

  1. A Tracker loan, which is defined as the central bank cash rate plus a fixed margin is a better floating rate loan for the borrower. Tracker loans exist in the UK.
  2. Longer fixed periods without break fees. 25-year fixed loans without prepayment fees exist in the United States and Japan.

So how is Huffle going to create the Amazon Moment for Mortgage Brokers?

We are looking to take that 1.3% cash commission and use it to creatively bring new, customer-centric mortgages to the Australian market.

We hope that the end result is not dissimilar to Amazon and traditional bookshops – with a more customer-focused and cost effective alternative that drives the traditional brokers to adapt and enhance their service for the benefit of all Australians.

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Musings of a FinTech: Will my bank screw me?

Inspiration for this comes from the statement “Australian Banks are unquestionably strong and much of this is due to their ability to screw the Australian punter”. I won’t say who said it.

Any bank can be unquestionably strong if a few conditions are met:

  1. It holds vast amounts of capital (global top quartile)
  2. It can borrow for virtually nothing (pay no interest on deposits)
  3. Shunt any increases in costs onto customers with the type of loans it offers (e.g. variable rate home loans)
  4. Generally price loans at higher levels as there is an overall lack of competition (oligopoly, a hint of price coordination)

In short: 0% deposits, generally high interest rates and shoving capital costs and other price increases directly onto customers as they appear. The banks will remain strong for a long time and the customer will lose out. The benefit: strong banks. The cost: a weak customer.

What does a weak customer mean and is it offset by a stronger economy?

A weak customer has weak returns on their savings, so retires with less. One response to this is that a saver should also buy bank shares. Win-win. I still haven’t come up with a reply to this but market forces should help: rubbish deposit returns should lead to people moving away until the deposit offering improves.

A weak customer might not get borrowing at the right rate. This one is trickier. It might prevent a business from being created or lead someone into financial hardship. But a strong bank has a healthier economy. This means more overall lending and no recession. So again I find this hard to dispute.

Does the customer face more risks?

So my last chance is to look at risk. What if a recession comes due to an external reason, say a China crisis or a severe commodity shock (both of which have potential)? Banks may face higher costs and interest rates could go up. Then what? In the hope of keeping banks strong, they can pass on a number of costs straight to consumers: higher variable interest rates. This would happen at the time of a weaker economy, so the debt might not be serviceable.

Now this might not happen. Banks may realise a spike in defaults at a time of a contracting economy may lead to higher losses.

So give a little back to the public and maintain their margins? This I find hard to believe: competition should reduce during a recession, so there are fewer alternatives. My gut tells me the punter would get squeezed and this is a type of manufactured bailout: customers will pay for their lender’s mistakes.

In short: if you want to have certainty in the future you need to find a way to detach the ability of your lender to screw you.

Much like my previous post, a recession may trigger a huge amount of innovation: punters will know what it is like to get screwed, opportunities will appear on the efficient frontier and the public will realise that bank employees have made off like bandits for decades. Just look at the unquestionably strong Royal Bank of Scotland circa 2006. However, until that recession, punters might never know and the opportunities may never appear.

What are Huffle doing?

We don’t want anyone to get screwed. We aim to bring better home loans to Australia, which will reduce the risks that borrowers face over the long term. Watch this space in 2016!


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Musings of A FinTech: Lending on the Efficient Frontier

Motivation for this post comes from a number of sources, including a conversation with a close friend on auto-leases and how new lenders have historically entered the market.

Entering lending markets is never easy. Established players have data, can price loans and generally deliver a solution to a set of borrowers. At the prime end of the market, assets with low probability of default and high asset coverage or security, it is virtually impossible to come in and disrupt: the game is about capital and funding. Large banks have both a capital advantage (lower risk weights, higher amount) and funding (e.g. deposit funding). A FinTech cannot win.

Strong and established lenders will naturally land on the efficient frontier of lending: low risk (volatility, capital) and reasonable return to get an optimal return per unit of capital. This can be viewed as either a point in time (where regulatory capital is determined as a through the cycle forecast) or as a through-the-cycle prediction: pick the stable annuity like returns and stay clear of the cyclical stuff. In other words, lend prime, ignore the sub-prime.

If a new entrant cannot win in the prime space (insufficient capital and funding), then all it can do is lend to those who don’t normally get loans (if you play on the efficient frontier, the established banks will out-scale you). This means lending to high default probability, low asset security, highly cyclical borrowers and you hope the credit cycle is long enough that you can establish yourself and diversify before the usually default cycle begins.

This has been achieved by a few FinTechs and new lenders:

Funding Circle entered the lending space at a time where nobody lent to small businesses as it was in the depths of the financial crisis but also the opinion that SME lending didn’t really make money through-the-cycle. Funding Circle identified a number of established businesses where banks refused to lend to: they couldn’t refinance easily as banks didn’t have the capital to lend to the riskier subset but were used to managing debt and had a good chance of survival. Next thing you know, they’re the 5th largest business lender in the UK.

Bear Stearns, for all its failings, really grew strongly from sub-prime and near-prime mortgage lending to become the world’s 5th largest investment bank. Its final failings were that is didn’t diversify away before the credit cycle blew it up: it was able to enter prime lending markets but had its deep exposers to its humble beginnings.

Dozens of other examples exist – from payday lenders, most peer-to-peer platforms (consumer loans with high stressed probability of defaults for those with interest of regulatory capital) all the way through to private equity or hedge fund owned non-bank lending platforms

Peer-to-peer also gives an interesting take: the initial losses are not an impact to the P2P platform. However, people will stop using a platform with high loss rates, so the platform would collapse in any case. But is the real play to hope the business cycle is long enough going to work for them? A recession now might also be a great thing for FinTech lending: it would provide an estimated longer time before the next cycle ends and open up more areas of lending the incumbents don’t want to lend. Timing is everything.

One final twist is lending itself: if incumbents lend in higher amounts and/or at lower interest rates, the business cycle will be longer (greater investment) and there will be fewer gaps on the efficient frontier. This means fewer FinTech lenders able to launch

Is this a problem? No, the consumer wouldn’t mind: they would be getting lower interest rates and more loans.

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Risk & Capital Management for a Digital Banking Ecosystem

This is written in conjunction with prior research into operating-company cooperatives and the formation of cell-like business models.

Many larger financial services conglomerates act across multiple areas and jurisdictions but are being forced to have a single-point of entry for capital. The point of this is that capital should be transferrable from sub-entity to sub-entity if it will be used as regulatory capital. If it cannot, then it isn’t really regulatory capital.

Multiple point of entry models exist for banking conglomerates that need to meet multiple jurisdictions. Banks are also split into bank and non-bank parts that make the web very complex (the non-bank must pay more to borrow than the bank). The major point: capital is needed in a centralised place whilst risk and effort often takes place elsewhere. Risk and reward also needs to be aligned as financial services move further towards return on regulatory capital as a key metric.

When building a banking ecosystem that could occur through a similar model to Apple’s App Store, caution should be noted. In the App Store model, external companies develop into Apple’s eco-system, allowing innovation to occur and profits to be shared. Whilst Apple has been hugely successful, more value has been created by companies in that ecosystem: the accumulated value of app-based businesses dwarfs Apple. Also note: Apple doesn’t face risk of failure in each app-based business.

A banking version of the App Store is much more difficult as there is a requirement to have a technology platform plus capital and funding.

Funding is easy to manage and is really a transfer pricing mechanism: how much do you need to borrow to fund your business and for how long. External funding is also possible or straight through referral and broking into external parties. Whilst there may be an interesting cost and flow of funds part here, the mechanism is easy to understand. The key aspect of lending is the capital that protects the funding.

Bank capital or any lender capital (bank or non-bank) is vitally important to protect funding. Capital reflects the level of security any loan has. For banks, this is regulatory capital as it protects depositors against losses and allows banks deposits to be insured.

If we want to build a banking ecosystem, sufficient capital is required. Now each entity could bring their own capital but it causes a problem: they would need to inject it into the holding company to protect the holding company capital and the attached deposits (single point of entry). If this is a non-bank, it will need to attach into the entity borrowing to fund business (security for the debt). Start-ups are also short on capital.

The problem this creates is that each agent in the ecosystem needs to be able to earn and create but the revenue, data and risk needs the correct alignment. In short, we need Fintegration.

Without this commitment, the talk of creating a banking ecosystem is a fallacy. It will lead to a poor alignment between risk and reward, or the banking ecosystem will be anything but: it will be neither a bank nor an ecosystem.

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Risk Retention Is Always Required As Risk is Always Here

At the end of the year in 2006, a group of senior bank executives got together to discuss the year. Included in this was the recent year’s performance, their forecast for the future and general chitchat.

The prior years had been blockbusters for them. Large house price gains, a collapse in the cost of financing (in the form of tighter credit spreads) and fantastic momentum in the economy created a feeling of invincibility. This lead to aggressive future growth estimates, assessment that if there was a contraction, a huge amount was required to even make a small dent into them. They thought the boom and bust cycle was gone forever, they had the best risk systems ever developed and economic profits would continue indefinitely.

Those looking for an alternative the new Star Wars film should consider The Big Short as it has a great explanation that whilst the self congratulation was coming out from one group of executives, others were merrily working away at alternative and very contradictory ideas. Harbinger, a hedge fund ran by Philip Falcone, was one firm to take a dramatically different view that catapulted the firm on the world stage.

So where are we now and what can we learn from this?

From an unknown unknown perspective, risk will always be there. It develops and changes over time and does become mis-priced and buffers for some people become insufficient. Right now, the outstanding $2trillion of debt financing resource companies has a strong parallel and CLO (collateralised debt obligations) that survived the financial crises incredibly well might now face a different and new risk if any form of contagion spills into the market.

Funding has changed dramatically and liquidity requirements reduce the risk of an outright credit crunch (liquidity was mis-priced in 2006). But what else could be mis-priced? Revenue could easily be overinflated and margins can erode so quickly across entire sectors. Within Australia, importers may face greater pressure as currency devaluation is used to support exporter (notably the resources sector). If they can’t pass on increased costs to a constrained consumer, margins will erode. If they try to pass on costs, revenues will fall. We will continue to look at the auto market as our leading indicator.

Can we do more to manage risk?

Returning to risk, my personal view on this matter has always been that the seller of investments or debt needs to have an associated through-the-cycle exposure to downside risk. Even short economic cycles of 5 years are enough time for risk to become poorly allocated by investors. A counter-balance is the skin-in-the-game or risk retention rules proposed in recent years. We are finally starting to see the legislation from the GFC form into live regulation.

This by no means suggests current incumbents don’t understand risk. It is a view that all current incumbents should align to the entire system’s through-the-cycle performance. And this includes sales.

So whilst we don’t know what the future will hold, we should continue to look at current incumbents who are creating large economic profit, are able to directly extract this from the financial system and are not directly impacted from cyclical impacts except for a potential decline in revenue. This means others are bearing the risk that should be allocated to them, a risk that people forget to notice.

Some investment banks proposed that staff should receive portions of the investments they create as a bonus rather than cash. This would cover many areas of insufficient capital. There are many more areas where this can apply and we will continue to discuss them in the 2016.

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