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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Mondo Screens

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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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 https://en.wikipedia.org/wiki/Deadweight_loss

 

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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).

CML MARKET ENTRY

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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