Musings of a Fintech: Huffle 10 Year Fixed Target Pricing Drops to 4.49%

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The RBA cut the cash rate to 1.75% this week and further rate cuts are expected. This sent the team at Huffle back to the loan lab to tinker with our model. Adjustments are required in our modeling, not only in the yield curve but also the pricing of interest rate derivatives.

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The daily feed of Aussie government bonds show the recent decline in borrowing costs but also a slight increase in steepness in yield curve. This is indicated by the 2-10 Spread, which currently sits around 72bps. On a very simplistic level, this indicates that our expected pricing for a fixed rate, hedging and capital aside, is likely to be 72bs higher than where the variable rate home loan market is.

Updating our models and with a Return on Equity hurdle at 20%, we have reduced our expected pricing down from 4.99% to 4.49%. The main gains are due to the current derivative pricing and volatility.

Why is this the case?

As rates compress towards zero, the embedded call option in fixed rate prepayables has a reduced upside value to the borrower. The result is that this has a reduced sale value that would naturally be added into the price of the mortgage. The overall impact is a bigger reduction in the expected fixed rate loan price compared to the observed rate cut and decline in the yield curve.

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Musings of a Fintech: Single-Product Single-Brand Strategy

One of my guilty pleasures is watching Gordon Ramsay blow off steam. It recently occurred to me that his TV show, Kitchen Nightmares, has a nice insight into bank product offerings, marketing and product development.

In Gordon Ramsay’s Kitchen Nightmares there is one specific action he takes in almost every episode. He looks at their huge menus, usually multiple pages of every option possibly available, and culls it to a 1-page daily menu of the best items. He does this for a few reasons:

  1. Customers can’t decide, it is too hard, and then they pick something they don’t really want or the most popular default item.
  2. The restaurant needs to keep a lot more ingredients and perishables. This has a substantially higher cost in terms of wastage and inability to bulk buy.
  3. The restaurant doesn’t have a clear identity. Does it sell pasta, pizza, fish and chips, burgers or a sub-set of them.

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On the opposite end of the spectrum a great example is one of my favourite eateries in London, Burger & Lobster (no Ramsay link, as far as I know). As evidenced by their name, they only serve 2 things. This offers a great advantage:

  1. They clearly show what they do. Customers understand this. When they want burger or lobster, guess who is the first choice restaurant.
  2. They can buy in bulk. Lots of lobster and beef. This means they can offer more competitive pricing and their staff can become specialized at cooking specific dishes. Margins increase.

Pr. Mark Ritson of Melbourne Business School and advisor to many world leading brands, also believes killing brands or items is more important that creating them. It becomes harder to sell so many products under a house of brands or branded house and also to keep them all relevant. It is also a headache on stocking and matching demand and supply. Large businesses need to reduce first.

So what does this mean for banks and Fintech? We suggest a few key points:

  1. Whilst large banks should be culling SKUs (financial products), FinTechs should fill the void and be product specific. We call this “Single-Product Single-Brand” strategy.
  2. Single brand for a single product: the Fintech becomes specialist in a specific area and customers who want a product go to that fintech first. This creates marketing and sales efficiency as the brand becomes synonymous with that product.
  3. Navigating through most bank websites is like the old restaurant menus. See for yourself – try to find CUA’s credit card products from their home page (hint: you’ll probably have to use the search function as they aren’t there!).
  4. Lack of product differentiation between the banks means customers simply can’t decide. So in the case for home loans, they defer to a mortgage broker for fulfillment – creating an unnecessary expensive cost to acquire new customers.

So where does that leave us? If a bank had to pick what its single product would be, I expect it is going to be deposits. This is the item they are regulated on and have strong risk systems in place, particularly over capital and liquidity. One way this could go is the concept of “utility” banks, which sit as the underlying layer of financial services. On top of this core utility layer there could be a number of “single brand, single product” FinTechs acting as satellites with unique products and customer experience tailored to specific segments.

The major challenge to banks specializing in core deposits? This is the areas that Google, Apple and the large tech giants are likely to go after in coming years, but we’ll save that for another blog.

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Technical Notes: Behavioral Risk Frameworks

At Huffle we’re big fans of investigating behavioural risk models. Largely because improving the understanding behaviour is easier through Big Data gathered from social media than other types of risk.

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Behavioural risk can be split into separate areas: internal risk and external risk.

Internal risk is created by employees, where we start to see companies monitoring employee activity to estimate a behavioural risk that may be associated with fraud or processes that lead to poor customer outcomes.

External risk in the form of customer behaviour is also important. Marketing has always investigated behaviour of customer’s purchasing. In finance we need to look at customer behaviour over the life of transactions as there is a risk of default, churn, fraud or additional leveraging.

Many parts of behaviour can be understood through statistical modelling – and procedures to understand, segment, assign probabilities are well understood and continually investigated. However, non-linear dynamics and bifurcation theory may be a better avenue*.

Why bifurcation theory?

Bifurcation theory looks at system changes as certain assumptions or inputs change. We would look for indications that would adjust one of our expected outcomes, which may include willingness or ability to service debt or likelihood of finding and switching loan provider. The advantage over stochastic modeling is that we can use historical data but also forecast what could lead to changes beyond probability likelihoods. The obvious difficulty is developing a reliable non-linear dynamic model and testing it.

We also note that there are interactions in a system. Banks will be selling agents and create internal shifts in dynamics depending on their willingness to lower margins to gain market share. Ultimately, we would also be able to use these models to manage risk as well and measure it: understanding which reactions lead to optimal outcomes.

Starting Models: How Uber may change traffic jams

One of the easiest behavioral dynamic models is traffic. We’ve all sat on the freeway, stop-starting in a traffic jam only to eventually end up moving without any accident in sight. This is a result of human inefficiencies and behavior**. Traffic flow modeling has been investigated for decades but can be summarized in a few rules:

  1. A person wants to follow the car in front at the speed limit
  2. A person doesn’t to get too close. There is a minimum headroom between cars. If they get too close, they will hit the brakes
  3. Each person has a delay in reacting to the car in front

With these rules, we can start to build interactions between cars, with the simplest example being a single-lane freeway. Cars accelerate to meet the speed limit and brake when they get too close to the car in front.

If we build this (non-linear) system we can start to observe a few things:

  1. The system can converge to where all cars move smoothly at the speed limit
  2. If the first car stops, all cars behind it will eventually stop
  3. If the first car slows down, all cars behind it will eventually meet that speed

Perturbation Theory:

The next aspect is to add a small amount of noise. Here, noise would mean a single car suddenly brakes for a reason, perhaps the driver got too close to the car in front after looking at their Facebook feed on their smartphone.

This would lead to a sudden braking. The interesting aspect to take from here is that subsequent cars, if they were travelling at the speed limit and close enough to the car suddenly braking, will be forced to brake themselves. Furthermore, if they were at the headroom, they would encroach on the safety zone and need to brake more sharply to replenish that zone.

There is a potential knock-on impact that subsequent cars need to brake substantially more if they were all travelling close to the headroom distance behind the car in front and at the speed limit.

Without modeling this, we can understand a potential amplification of the initial noise, and subsequent cars can potentially be forced to stop. At this point, we can create the traffic jam.

What factors impact the potential for a traffic jam?

  1. Car density: more cars increase the likelihood of cars travelling closer to the headroom distance as they cram onto the road in rush hour.
  2. Speed: faster motion means the encroachment into headroom can be higher (the delay in human reactions is a fixed time but the distance travelled in that time will be higher)

There are other smaller impacts. However, the above two are controllable on a system wide basis.

What are the risks?

Car crashes are a potential risk if the stopping propagates too quickly. Time delays are also possible as cars also have a delay in returning to the speed limit after they stop, and this can lead to significantly longer delays in traffic.

How do we control them?

Reducing cars is an easy one. This can be done through toll roads or pricing strategies. Ultimately, this is one aspect that makes Uber incredibly interesting as it is adopted more widely. Rush hour will become more expensive to travel.

Speed is another. We often see temporary speed limits. This diminishes the human delay and reduced the likelihood of the traffic jam propagating. The obvious cost here is that the journey times are less than optimal and may be higher on average. But dispersion and risk is reduced.

Moving back to lending

Without detailing too much, the non-linear model becomes more complex as the forces driving it are very different in the world of finance. First of all, if we are building a default model, what defines a default? We could classify it in a similar way to a car stopping in the traffic model and with a speed limit taken as repayments on a mortgage.

Multiple agents would exist: borrowers wouldn’t react with each other, instead they would react around a set of banks, gravitating towards them as better mortgage offers appear. In this system, we would have a borrower acting as a metallic ball in a large financial system that acts as a pinball machine bouncing off different lenders.

Managing risk then becomes a similar trick:

The inputs that then cause reactions in the system (similar to the car braking above) can then be changed by:

  1. Adjusting the price of loans by lowering interest rates, making debt easier to service. This can be performed by central banks. This also relates to the velocity of money.
  2. Modify the loans, which can be performed by the lenders.
  3. Restricting the new loans made available by increasing interest rates. Banks would do this if they want to reduce the risk on their books (hope for churn of “risky” customers).

However, we can also identify other ways to manage risk and measure potential changes, particularly around macro-economic noise that may lead to significantly higher defaults or churn, depending on the conditions.

Internal Risks?

The thing to note here is that behavioural operational risk comes in various forms. In an ideal world, a pricing and cost mechanism can be established. To manage the risk and control temperament if it overheats, or to retrospectively adjust if poor behaviour is not picked up in time.

However, it is hard to reclaim salaries following poor behaviour and it is also hard to pay employees fully in deferred shares. In many ways, outsourcing and deferring a payment is easier here.

Another key aspect could potentially be keeping each product to separate teams, or where we expect things to go – with a separate company (FinTech). If a particular product emerges with a problem, the relationship or business unit can be replaced or removed.

Again, our modelling aims to track what would potentially lead to a change in behavior, most notably when behavior begins to focus on commission rather than customer outcomes.

Once we have achieved this, we may become significantly better at estimating our “through the crisis” revenue streams and understanding risk driven by both borrower and competitor behaviour, enabling us to proactively manage behavioural risks.

*We prefer to move away from stochastic modeling, instead preferring to build non-linear models based more on particle theory. We think this allows us to investigate potential behavioural shifts through bifurcation theory.

**The phenomenon is a sinusoidal compression wave travelling in the opposite direction to traffic flow.

Picture from www.fatvat.co.uk

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Musings of a FinTech – Actionable Insights from Social Media

Being able to effectively mine data produced via social media is very topical, with the emergence of companies such as Thinknum providing metrics from social media and other sources to provide new insights into company performance.

However, there is a wider question here of what data can actually be harnessed to provide genuine insight and tangible value to both companies and/or individuals – the classic problem of extracting the signal from the noise.

Thinknum provides company metrics such as Twitter/Facebook followers, employees on LinkedIn and web site traffic which arguably could be useful indicators of a company’s health for investors. A recent FT article provides a good run down of some of the current crop of investor-offerings in this space.

 

In the area of housing, a recently published piece of research from Harvard, Facebook, NYU and the Bureau for Economic Research provides one such insight using data from Facebook. Entitled “Social Networks and Housing Markets”, it looks at how social media influences an individuals perception of the attractiveness of property investment.

The key takeaway from the paper is “Individuals whose friends experienced a 5 percentage points larger house price increase over the previous 24 months (i) are 3.1 percentage points more likely to transition from renting to owning over a two-year period, (ii) buy a 1.7 percent larger house, and (iii) pay 3.3 percent more for a given house. Similarly, when homeowners’ friends experience less positive house price changes, these homeowners are more likely to become renters, and more likely to sell their property at a lower price.”

It’s interesting to see how they combined the data sources for it – the model used Facebook user data along with market research data from Acxiom at its core to build rich demographic data.

One of the key uses of the Facebook friend data was the location of where an individual’s friends reside – specifically those that are within or outside of the Los Angeles county commuting zone (they surveyed homeowners all resided in LA county). This enabled the researchers to distinguish between local friend influences and biases, versus those further afield – the assumption being that house price movements experienced by friends outside the commuting zone would have been effected via social media channels (sec 1.4 p10).

This was supplemented with the relevant housing data, and a 4 question multiple-choice survey for testing the various hypotheses:

 

  1. How informed are you about house prices in your zip code?

[x] Not at all informed [x] Somewhat informed [x] Well informed [x] Very well informed

 

  1. How informed are you about house prices where your friends live?

[x] Not at all informed [x] Somewhat informed [x] Well informed [x] Very well informed

 

  1. How often do you talk to your friends about whether buying a house is a good investment?

[x] Never [x] Rarely [x] Sometimes [x] Often

 

  1. If someone had a large sum of money that they wanted to invest, would you say that relative to other possible financial investments, buying property in your zip code today is:

[x] A very good investment [x] A somewhat good investment [x] Neither good nor bad as an investment [x] A somewhat bad investment [x] A very bad investment

 

The ordering of the questions in 35% of the surveys was changed to avoid the framing effect with people’s responses, which was an interesting point to note (although they didn’t find participants were influenced by ordering of questions in this instance).

This survey & demographic data was then utilised alongside housing transaction data, and they created a number of regression models which supported the conclusions of the paper.

 

Given all of the talk about social media & mining this data, it’s a useful paper to be aware of and illustrates not only a potential use case for harnessing the power of social media to generate insights, but also how complex a task it is to do so.

Bearing in mind that the individuals in the survey were limited to those residing in LA county, and the measured impact of social media appears to influence individuals by up to ~3% which is pretty small in the grand scheme of things*, trying to apply a similar model to something similar like how social networks influence an individuals mortgage preference would be no small task!

 

*We are very excited overall that new data can not only lead to new ways of analysing risk but potentially be a strong leading indicator, allowing more time to rebalance portfolio risk. However making a judgment call on new data presents higher modelling risk.

 

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Musings of a FinTech: Same, Same but not all that Different

I was recently asked to take part in a focus group with one of the Big 4 banks who shall remain unnamed. They had flown a (presumably) very expensive consulting group out from San Francisco to help them to define their “purpose”. Whilst having a clearly defined purpose is a great idea, I couldn’t help but think that it was just another example of the big banks doing everything they can to differentiate themselves from their competitors except the thing that would truly allow them to.

What I’m talking about of course is product innovation.

Banks are trying to innovate on everything, bar the thing that matters most – their products. I mean think about it, without products we wouldn’t even need the banks. Their core service is to provide us with great financial products that allow us to make the most of our money and help us achieve our dreams. Be it buying a home, building wealth or starting a business (now it sounds like I’m the one defining their purpose).

Don’t get me wrong, banks are doing a lot of work to change themselves through innovation in their retail arm with new branch designs and services, improved customer service through social media etc. or better digital offerings with great apps and technology. All of this stuff is necessary and hugely important but the elephant in the room is that we still have a set of financial products that seem almost identical across the major banks.

Same, Same

In a recent Q&A at the FinTech Melbourne Meetup, new ANZ CEO Shayne Elliot stated that his bank would be looking to invest in and partner with startups to help improve their customer experience. We totally agree with his sentiment but once again the focus isn’t on their products. Funnily enough in the same session he also said “We make most of our money selling mortgages”, yet no mention was made of the fact that their product is barely differentiable with the other 3 majors and FinTechs could help them to build out a truly unique product proposition.

At Huffle, we believe that product innovation can, and should, come from sources external to banks. Here are 3 key reasons why:

  1. Bureaucracy and speed-to-market: Over the years, banks have built up a number of processes and programs which make getting things to market quickly virtually impossible. No such barriers exist for a small tech company which has recently been established.
  1. Ability to run a Minimum Viable Product (MVP) under a different brand: Making big changes to existing products does present a risk to banks, having smaller innovative brands to test these with limits the exposure to reputational damage and allows unique propositions to be tested and refined before being adopted en masse.
  1. Fresh, outside-the-box thinking: Whilst there are probably a million good ideas floating around in a large bank, often people who have been working there follow a similar pattern of thinking. Startups that bring people together from a broad range of backgrounds have the ability to attack a problem creatively, from an angle which might not have been thought of before.

I would love to know your thoughts;

  • Do you think the banks products are up to scratch?
  • What kind of product innovation would you like to see them offer?

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Musings of a FinTech: The Banking Revolution will be Digitised

Whilst it may not have made the news here in Australia, earlier this month Holvi, a completely digital bank catering to small business owners and entrepreneurs in Finland, was acquired by BBVA. It’s the latest in a string of acquisitions and investments by the Spanish banking giant in digital banks. It started back in early 2014 with a $117 million acquisition of Simple Bank in the US, and now there are a raft of new digital banks in the process of being launched around the world with backing from them.

 

Much like other established industries, the banking system is sitting on a complex patchwork of technology and platforms that have been built upon over many years. What this creates is a costly infrastructure to change and update, and often small, customer-centric enhancements are so costly that they get de-scoped. Whilst some banks have forged ahead with building out a “digital first” experience, in Australia I’m thinking ING Direct and UBank (powered by NAB). Because they are still built atop the existing bank frameworks, limitations exist as to how far they can push the envelope.

 

The UK is the country that looks set to benefit the most from this new push into digital banks. With a low barrier to entry (you only need £1 million capital to get your banking licence vs $50 million here in Australia), and a nation that has adopted doing things online faster than most (the UK are some of the most prolific online shoppers in the world). There is now a slew of digital or mobile banks set to launch imminently.

 

Mondo Screens

Image credit: getmondo.co.uk

The most well known of these banks is Atom, they started 2 years ago with a vision to be a mobile bank with a heavy focus on personalisation – one of their early campaigns was to get 1.4 million logos designed, so every member can choose their own. They are the first of these digital banks to get their licence (in 2015) and have received £135 million funding to date. Mondo is another UK digital bank that has a good news story with their funding – they famously raised £1 million in 96 seconds via crowdfunding platform, Crowdcube. Two other names to watch in that market are Starling and Tandem. Brits are soon going to be spoilt for choice with new digital alternatives to the traditional banks.

 

But what about here in Australia? We know conventionally there is a lag in new tech developments reaching these shores, and this coupled with the aforementioned high barriers to entry makes it harder to break into this market. But it’s not impossible. Whilst at Huffle we are initially looking to introduce new home loan products, there are some logical steps we could take to build this proposition out to become a digital bank. Without the constraints of complex legacy systems, and fresh thinking from founders with experience both within and outside of Financial Services, it’s not a stretch to see Huffle making moves to create one of Australia’s first truly digital banks… But one step at a time, first we want to shake up the home loan industry with our great new mortgages.

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Originate-to-Distribute in Finance: Part 2 (FinTech Lending)

In Part 1 of this post, I described where we currently are regarding originate-to-distribute risks. Australia has a heavy sales culture across financial services, run by manual processes that the industry admits “mostly does a fulfilment process”. In the long run, FinTechs need to prove they can do more than distribution.

In recent years we have seen wider adoption in the US and Europe for online lending. Peer-to-peer models are particularly powerful, as they don’t require bank balance sheets or regulatory capital and many cost savings can be passed back to lenders or borrowers.

The main risk we see in FinTech lending is that the fledgling industry is trying to replace a mostly reliable format with a model that may overlook risk management processes. Do remember that banks, as much as they try to redress themselves as technology companies, have a core function in risk transformation.

Lending FinTech still needs to consider risk transformation

Risk transformation is an interesting thing. Some FinTechs believe that the sharing economy can probably perform the task itself and this is the purest part of peer-to-peer lending. If consumers can capture the efficiency savings, or at least the reward previously captured by banks, then it serves a purpose. However, I doubt that individuals have the capability to perform all the required risk management functions, particularly the measurement of unexpected losses in recessions.

For FinTechs who have aspirations to stand taller than the past models, they need to develop strong risk assessment at a minimum but also address the potential for fraud or manipulation over time. There are several other mechanisms you can put in place to manage those risks but they will need adoption by incumbents too as existing constraints impact what is achievable.

Will we see a wider distribution model

The question is where do FinTechs go? Without large balance sheets they will either have to be ever more reliant on a retail distribution model (peer-to-peer) or look for alternative wholesale funding models and rely on being a sales entity.

Securitisation immediately springs to mind but I would suggest this will fail if start-ups don’t’ have deep expertise in this area. A type of government guarantee, similar to a deposit guarantee, would also work but needs the FinTechs to demonstrate their ability to manage risk and a standardised framework to be built by a regulator.

What could FinTech risk management look like

Better risk management tools do and can exist but may need system-wide re-design – but start-ups are free of some legacy constraints. Answers point towards new risk management frameworks and how they directly compete against distribute-to-originate risks that sales-only business models face. This might be in construction of the FinTech or by the relationships built with incumbents, and knowledge of regulations would fall upon FinTechs (we doubt incumbents would push this on behalf on fledgling start-ups unless they wanted to pivot to a FinTech centric utility bank).

A key component will be avoiding swathes of sales heavy teams extracting upfront value, even if venture capital investors demand this to de-risk as quickly as possible or to avoid later capital raising and dilution. Luckily tech driven approaches who achieve low customer acquisition cost have the capacity to offer this, as long as the customer can recognise the additional work being done in the background (and government guarantees or third party approvals are effective ways to communicate this).

What Huffle is doing?

We have created an entire risk management framework that covers the above and forms part of why we can bring more attractive home loans to the market. Partially driven by a credit supply chain re-design but also what processes we do:

  1. Actively managing credit risk and interest rate risk on a daily basis, driven by new data sets and in-house models based upon our prior professional experience
  2. Being capital additive to the banking system

However, if we truly want to enable these changes, we need to let go of our legacy models that have the originate-to-distribute risks embedded within them. Overall, we’ll have a stronger financial system that will be more resilient through business cycles.

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Originate-to-Distribute in Finance: Part 1 (What we have now)

Before we delve into variables and risks within FinTech and lending, we need to start with a simple view of what banks are.

Banks are a financial intermediary between sources of money, such as companies creating value and individual depositing their salaries and savings, and companies or people needing money, such as a loan.

The core basis of our financial system is that banks originate-to-hold to both sides of their equation (borrowers and lenders). A company borrowing from a bank owes the bank money, which is a clearly separate risk from a pensioner with their cash sitting in a savings account, for example. That pensioner receives interest payments that are unrelated to the risks the banks makes elsewhere.

Deposit holders (The Pensioner) then get government protection if the bank fails. If the bank fails, this will be due to incredibly severe losses in their lending books (insolvency) or a deep mismatch in the timing of loans (this is to do with liquidity). Banks are well regulated to cover these risks.

This model works well on a few fronts, most notably if the lenders (The Pensioner) and borrowers want 2 different things. This creates the requirement for banks to manage risks and perform maturity transformation. A bank’s core function is risk transformation.

Originate-to-distribute

The above model is a well-tested model and has functioned for many years. Bank regulators set capital to cover risks that has mostly worked but is also expected to fail at some points in time and this is where regulators, central banks or governments need to intervene.

Originate-to-distribute systems are slightly different and were originally about removing risk from the banking system: allowing banks to sell on some of their risk to investors who want that specific risk or require a higher return. This then frees up banks to make sequentially more loans rather than being full of legacy loans.

Problems pop up straight away

We have a few clear problems here. Firstly, this enables more lending. This is good if it allows more companies to exist or allows more people to borrow to achieve life goals. However, the expansion of lending pushes up asset prices. Further, the selling of loans by the bank puts a slight question as to what they care about: volume quickly surpasses quality.

This model works well if the risks are well-managed and consistent, however we have seen how this breaks down when fraud or manipulation is introduced. The originate-to-distribute increases the potential for fraud and manipulation as selling becomes a primary function for more of the credit supply chain.

Under the microscope

Now we must realise a few things: if we start to look at bank processes in a more granular way, originate-to-distribute appears in several formats in most financial intermediation systems. Sales functions themselves are originate-to-distribute, particularly if those functions are rewarded in a predominantly upfront manner or have little risk on the table.

The construction of the Australian mortgage industry brings up more examples of originate-to-distribute.

Firstly, most Australian banks under the standard variable home loan place several of the loan risks back with the borrower – notably bank credit spread risk and interest rate risk. Secondly, mortgage brokers are a large component of the mortgage industry.

Borrowers keeping the risk

The obscure part here is that if you take a loan from a bank as a borrower, the bank keeps the default risk but the borrower faces a number of other risks. The borrower keeps the interest rate risk and credit spread risk. What this means is Australian banks have manufactured a way to maintain margins and allow them to originate loans in a limited risk environment: they can simply increase interest rates to cover their margins.

Mortgage brokers are a direct originate-to-distribute model as a sales function. If they don’t’ originate loans, they don’t get paid.

Why we don’t like this

Returning back to the originate-to-distribute, as the sales function or bank is less inclined to hold risk themselves, they really only care about volume knowing that they can re-price loans at a later date. This isn’t a claim that it is occurring, rather than an observation that this has a potential weakness for fraud or manipulation. Variable rate loans and the attached credit supply chain have become lazy: risk transformation has reduced as borrowers keep more of the risk.

Now we have set out the problems in the current system, we can start to look at how they relate to risk management for FinTech and why a superior system is expected to emerge. Tune in for Part 2.

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Musings of a FinTech: Why “if it ain’t broke, don’t fix it” isn’t always true

In 1977 Bert Lance coined the oft-used phrase “if it ain’t broke, don’t fix it” – but you only have to look at the disruption that’s happened due to technology over the past decade to know this isn’t true anymore.

Extensively covered examples, like Uber and the taxi industry, Airbnb and hotels or even case studies from closer to home such as Xero and accountancy, have shown that left field thinking and broad access to technology have changed the game. It’s no longer good enough to rest on the laurels of existing business models, just because they bring consistent returns and customers appear to be content. Chances are there is a better way to do things which someone is working on quietly in their bedroom or a co-working space somewhere.

If it aint broke fix it

At Huffle Home Loans, this is what we are looking to bring to the home loan space. For years, banks in Australia have delivered record profits driven off the back of a mortgage industry where the products have stayed stagnant and the public’s appetite for property as the most popular investment option has kept demand high. To the banks, and most consumers, the system isn’t broken… So why fix it?

We think there is a better way, and it’s all about fixing it!

Huffle has built a new home loan model, which allows us to take on some of the risk that a customer would normally absorb (through their interest rate). This in turn, enables our partner bank to offer a lower interest rate, fixed over a long period of time. The loan also come with added features like; no penalties for paying out early, and flexibility over breaking out of the loan after a period of time (before the full term).

The biggest hurdle is getting the banks on board. As mentioned, they have a proven model that continues to drive increasing profits with the added bonus of high barriers and government protection preventing new entrants. But what about you – the customer? Are the current mortgages in the market what you really want and need? Or can they be delivered in ways that better serve you?

That’s why we need your voice and support to join together and help bring our products to life. We’ve built the model. We have the team in place. The last 2 things we need are a bank to supply the loans, and most importantly, the customers telling the banks that it may not be broke – but we can surely FIX it.

Sign up at huffle.com.au to keep informed about our launch and lend your voice to our movement.

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Architecture Evolution

Like most startups, Huffle’s website platform has undergone a number of changes during the past year. The path it has followed is pretty typical, with each platform evolution reflecting the increase in technical investment required to grow from one stage to the next.

I thought it may be useful to share this evolution, given it’s such a common sequence of phases, I hope it may save on some of the research you have to do yourself.

 

Phase 1 – The Hosted Landing Page

Huffle’s initial site contained a single page used as a lead generation tool. There are many different platforms that can be used here including WordPress, Wix, Instapage and Unbounce, which are some of the popular options. Each of these platforms provide online editors for designing and writing the relevant content you want to display. They also typically provide integrations with 3rd party services for capturing leads, such as MailChimp/Campaign Monitor for e-mail lists, Salesforce/Zoho for CRM.

Our very early site was hosted on Wix, but we preferred the landing page templates on Instapage, so moved across mid-last year.

Once created, you simply point your site DNS record to the hosting provider and away you go with your landing page.

 

Phase 1 - Instapage

 

You’re completely at the mercy of your landing page provider – if they go down, there’s very little recourse you can take, but at least you have a web presence.

 

Phase 2 – Platform as a Service (PaaS)

The landing page was never considered more then a temporary web presence. We were able to import chunks of HTML/CSS/JavaScript into the page via the hosting platform, but we simply couldn’t customise the look and feel as much as we wanted to. Additionally, we wanted to throw a database into the mix to start capturing real customer data, so we needed to start building out a proper customer site using a web framework.

The most common choices in this space tend to by with a dynamically typed language such as Ruby (Rails), Python (Django, Flask), PHP (Cake) or JavaScript (Node.js, React, AngularJS), as they tend to be good for getting something up and running quickly. You can go with a statically typed language (Go, Java, .NET, Scala, Haskell, …), but they tend not to be as fast to get something live out (unless you’re far more comfortable with statically typed languages).

The target deployment infrastructure is pretty straight forwards, consisting of web and database servers.

However, getting a nice automated deployment process up and running takes time, plus the underlying severs need to be managed, which is where Platform as a Service (PaaS) solutions came in. We used Heroku, as it provided a ready made platform for serving up applications in a number of different languages.

It provides a single command to deploy our latest code base out to their platform running on top of Amazon Web Services. Additional web servers (dynos in Heroku speak) can be freely added or removed to scale up/down your site as needs dictate, making it an ideal platform during the early stages of your startup.

Heroku also provides a marketplace for add-ins, making it really straight forwards to add additional functionality (sending email, hosting over SSL, application monitoring, …) with a minimal amount of effort. You can also make use of tools such as loader.io to easily see how your site performs under moderate loads (hundreds of requests per second) to ensure your site can handle those initial burst of publicity.

 

Phase 2 - Heroku

 

As great as Heroku was for getting our web application up and running quickly. There were some limitations that were frustrating to work with:

  • You cannot jump onto a server to have a dig around – everything is done via the Heroku logs command
  • Heroku runs on top of AWS across a limited number of regions – none of which are in Australia.
  • You cannot run a Heroku application out of multiple regions with duplicating your entire platform including the database server (which is expensive) in both sites. Plus you’ll need to find a way to synchronise your database. This means that when there is a problem in the AWS region your Heroku instance is running in or in Heroku itself, you have zero options for redundancy unless you duplicate your infrastructure.

Heroku does provide a status page which is useful, but if site availability is crucial to you, these issues are too great to rely on it as a solo hosting platform, which is why we made the move to AWS, which provided us with a greater degree of flexibility with our deployment/management options.

 

Phase 3 – Infrastructure as a Service (IaaS)

In the world of Infrasutructure as a Service (IaaS), Amazon Web Services is king. There are a number of other IaaS platforms to choose from, however, given its relative maturity, it being platform of choice for so many startup success stories, and it’s Activate Program for startups, it was a no-brainer for us.

Amazon Web Services provides resilience across multiple geographic regions. Within each of these regions there are multiple data centres (availability zones) you can deploy your application across. This flexibility of deployment met our needs by providing a hosting platform that provided availability across multiple physical sites, giving us the resiliency we required for running our main production site.

The up-front investment required to automate the provisioning and deployments of environments is high, requiring investment in:

  • The DevOps toolchains such as Ansible, Chef, Puppet or SaltStack for environments provisioning and ongoing management
  • Creating deployment/release tools, especially if you want to use immutable servers
  • Security – ensuring access points to your environment are minimised and communication between nodes is restricted to the bare essentials

The end result for us looks something like this, where we have full site redundancy across multiple data centres and are located within AWS’s Sydney region.

 

Phase 3 - AWS

 

If required a new copy of this environment could be brought up in a matter of minutes with our DevOps provisioning tools, should our AWS region fail, but for now it mostly meets our needs, and provides us with a great degree of flexibility going forwards.

AWS does provide Platform as a Service capabilities with it’s Elastic Beanstalk offering, however we wanted the flexibility to manage our own servers and support non-standard use cases such as hosting multiple sites over SSL on a single set of infrastructure, which does not play so well with Elastic Beanstalk.

They also provide OpWorks for managing cloud infrastructure, however, it does tie you to Chef which was less appealing for us compared with some of the other options out there.

 

Footnote – DNS Failover

One of the options that we looked at early on was DNS failover to provide resiliency between different hosting providers, should one of them fail. The issue with this approach is that most providers require you to work with IP addresses which is not feasible if you’re using a provider that only gives you a URL to point to.

Amazon’s Route 53 DNS record management service provides a failover mechanism with CNAME records, which we found was good for our use case.

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