Category: Digital Disruption

The 2016 Sterling Flash Episode

From The Bankunderground

In the early hours of the morning of 7 October 2016, the sterling-US dollar exchange rate fell by nearly 10% within around 40 seconds. Most of this movement was reversed within the ten minutes that followed. This was one of a series of such ‘flash’ episodes in major financial markets – that is sharp and short-lived movements in price, which vastly exceed perceived changes in economic fundamentals. It certainly didn’t fail to catch the eye of policymakers, or the media.

This post summarises a recent working paper by Bank staff to understand what happened and why.

What happened?

To get at this question, we took high frequency data from the Thomson Reuters platform. Day-to-day, this platform facilitates between five and ten per cent of trading in the sterling-US dollar spot market – one of the most liquid currency pairs in the world.

Chart 1 shows data taken from this platform around the episode:

The triangles show the prices at which individual transactions took place. Those in blue (pointing down) indicate transactions initiated by a participant seeking to sell sterling. Those in green (pointing up) indicate those initiated by an order to buy.

The shaded regions show the cumulative distribution of limit orders around these prices. Limit orders are unexecuted orders to buy or sell sterling posted by prospective traders.

The relative weight of the shading on the chart shows the quantity of limit orders between a given price and the limit orders to buy/sell at the highest/lowest prices (the ‘best bid/ask prices’). As might be expected, prices further from the best bid/ask are shaded in darker colours. This indicates that there lies a larger cumulative quantity of limit orders between them and the best bid/ask.

The black line shows the midpoint – or ‘mid-price’ – between the best bid/ask prices.

From this chart we can construct a rough narrative of events:

In the minute preceding the crash, between six and seven minutes past midnight (00:06:00 and 00:07:00 British Summer Time), there was quite a large depth of orders both to buy and sell sterling (£60 million of orders in the observed ten levels of price closest to the best bid and ask prices).

But at around 00:07:00, an imbalance started to develop – with the quantity of orders to sell sterling starting to exceed those to buy.

It was at this point that a rapid succession of trades took place in sterling, at rapidly declining prices.

This imbalance became particularly severe around 00:07:17 BST. This can be seen from the large white areas in the graph, which indicate there to be (close to) no orders to buy sterling. In the half minute that followed, market functioning was severely impaired, with large ‘gaps’ in price visible between trades.

The quantity of, and balance between, limit orders to buy/sell sterling recovered after about 30 seconds but deteriorated severely again after around one minute. Shortly after 00:09 BST, there was a further sharp reduction in orders to buy sterling (corresponding to another white area on the chart).

The order book started to increase in depth around 00:09:30 BST, around 150 seconds after the initial sharp movement in price.

Thankfully, the events of the night of 7 October 2016 were without lasting consequences for financial stability, or the integrity of the functioning of the market for sterling.  Higher than usual volumes were observed during the day that followed, and measures of illiquidity (including bid-ask spreads) remained slightly elevated; but broader spill overs were generally limited.

That, however – understandably – hasn’t stopped the search for answers.

…and why?

In its report on the episode, the Bank for International Settlements (2017) found the movement in the currency pair to have resulted from a confluence of factors. These included larger-than-normal trading (predominantly selling) volumes at a typically illiquid part of the trading day. There were also sales of sterling by some market participants seeking to limit the risk associated with their positions in options markets, and to execute client orders in response to the initial fall in the exchange rate.

One important outstanding question is the degree to which the change in price witnessed during the episode was in line with the imbalance between observed orders to buy and sell.

We’d expected an imbalance in order flow during an episode like this to increase the change in price that results from a trade (or trades) of a given size. An imbalance in orders might be interpreted as indicating that some market participants were party to superior, or more up-to-date, information. This might, in turn, widen the spread at which other participants were willing to buy/sell sterling.

To assess this empirically, we develop an estimate of the change in price that is likely to result from an imbalance in orders in foreign exchange markets. This is based on previous literature. It is robust to the possibility that a large order might be split up into a series of smaller orders. This is important, because such splitting of large orders is common in foreign exchange markets, because, by transacting in smaller size, market participants can obtain a better price.

The blue bar in Chart 2 shows the range of estimates of change in price given by this model, when calibrated to past movements in the sterling dollar exchange rate.  These imply that the observed orders to sell sterling during the flash episode are consistent with a decrease in the sterling-US dollar exchange rate of between 1.03% and 2.87%, depending on the precise choice of parameters.

Chart 2: The (in)consistency between observed changes in price and those expected given observed orders to buy/sell sterling

The dots in the right-hand columns compare these estimates with the observed decline in the exchange rate. The purple triangle shows that which took place early in the episode, between 00:07:00 and 00:07:15 (roughly step (2), above). The yellow square shows the peak-to-trough fall in sterling over the entirety of the episode.

From this we can see:

  • The initial fall in sterling, during the early part of the episode, of 1.8%, is consistent with the range of estimates based on the observed imbalance of orders. This suggests that the movement in price was consistent with the arrival of a large order to sell sterling.
  • But the larger change in price that occurred between 00:07:00 to 00:07:17 cannot be explained by expected price impact of trades alone.

Was there another factor at play?

That the overall fall in sterling so vastly exceeds that predicted by our model might suggest that some some other driver might have been at play.

The report by the BIS suggests a number of other factors that might have played a role in reducing available liquidity during the episode. These include the temporary withdrawal of some market participants from their role as market makers. This dynamic may have, in part, reflected the presence of staff with lower risk limits and appetite at some institutions at that time of day. An automatic pause in trading in sterling futures contracts may also have led to a reduction of liquidity in the cash market, because some market makers are thought to rely on futures as a guide to the price at which they offer to buy/sell currency in cash markets. It is, however, difficult to know what weight to place on these different explanations.

All else equal, such a reduction in liquidity would have increased the resulting fall in price beyond that estimated to be in line with observed trading volume.

Conclusion

The events of 7 October 2016 represented one of a series of flash events occurring in electronically traded markets. No such events have, as yet, had longer lasting consequences for market functioning or stability.

Nonetheless, policymakers have recently pointed to the clear onus on central banks and the regulatory community to understand developments in these markets, and how they behave during periods of stress.

This work represents one step in that effort.

Bank Underground is a blog for Bank of England staff to share views that challenge – or support – prevailing policy orthodoxies. The views expressed here are those of the authors, and are not necessarily those of the Bank of England, or its policy committees

APRA Introduces First Anti-Cyber Attacks Prudential Standard

The Australian Prudential Regulation Authority (APRA) has responded to the growing threat of cyber attacks by proposing its first prudential standard on information security.

APRA today released a package of measures, titled Information Security Management: A new cross-industry prudential standard, for industry consultation. The package is aimed at shoring up the ability of APRA-regulated entities to repel cyber adversaries, or respond swiftly and effectively in the event of a breach.

The proposed new standard, CPS 234, would require regulated entities to:

  • clearly define the information security-related roles and responsibilities of the board, senior management, governing bodies and individuals;
  • maintain information security capability commensurate with the size and extent of threats to information assets, and which enables the continued sound operation of the entity;
  • implement information security controls to protect its information assets, and undertake systematic testing and assurance regarding the effectiveness of those controls;
  • have robust mechanisms in place to detect and respond to information security incidents in a timely manner; and
  • notify APRA of material information security incidents.

Executive Board Member Geoff Summerhayes said the draft standard built on prudential guidance first released by APRA in 2010 and backed it with the force of law.

“Australian financial institutions are among the top targets of cyber criminals seeking money or customer data, and the threat is accelerating,” Mr Summerhayes said.

“No APRA-regulated entity has experienced a material loss due to a cyber incident, but a significant breach is probably inevitable. In a worst-case scenario, a cyber attack could even force a company out of business.”

Key areas where APRA is hoping to lift standards include assurance over the cyber capabilities of third parties such as service providers, and enhancing entities’ ability to respond to and recover from cyber incidents.

“Cyber security is generally well-handled across the financial sector, but with criminals constantly refining and expanding their tools and capabilities, complacency is not an option,” Mr Summerhayes said.

“Implementing legally binding minimum standards on information security is aimed at increasing the safety of the data Australians entrust to their financial institutions and enhance overall system stability.”

Submissions on the package are open until 7 June. APRA intends to finalise the proposed standard towards the end of the year, with a view to implementing CPS 234 from 1 July next year.

Copies of the consultation package are available on APRA’s website at: http://www.apra.gov.au/CrossIndustry/Consultations/Pages/Information-security-requirements-Mar18.aspx

The findings of APRA’s latest cybersecurity survey can be found in the December 2017 issue of Insight.

More Digital Disruption Hits The Mortgage Industry

Another new player has entered the contested mortgage origination sector. Just launched is Loanbid  which says it empowers borrowers with access, choice, and competition to secure a loan from one website.

Using an auction model, borrowers enter their details and requirements once. Lenders and brokers can then assess the information and enter a virtual auction to win the loan with their one time best bids. The platform currently has 18 lenders on its panel, including many of the usual suspects.

All offers are shown to applicants via dashboard and are ranked according to the total cost of the loan. Email alerts are also part of the process. The applicant is not committed to taking a loan, and Loanbid does not offer any advice.

The platform does not charge customers an upfront fee or take trail commissions from lenders. If the loan is settled, Loanbid receives a referral fee from the successful lender or broker. So they are essentially “clipping the ticket.”

Borrowers are only identified by a reference number, so remain anonymous throughout the process. Loan proposals are independently and individually assessed without any impact on their credit rating.  Normal loan verification is then undertaken by the successful lender or broker.

Via Australian Broker

“Borrowers need to wait for just 48 hours for the lenders and brokers to come back to them with a loan best suited to their needs and financial situation, with the borrower to choose the right loan,” said Loanbid partner Paul Dwyer, formerly a banker at St George.

“We will put the power of choice back in the hands of borrowers, who can potentially access thousands of loans based on the information they provide through an obligation-free bidding process,” said Dwyer.

“We have been fastidious in deciding on our partners on the platform, to give our borrowers the best opportunity to ensure they get the right loan for their requirements and lifestyle,” said former NAB banker Darren Roach, a partner at Loanbid.

 

Crypto Is Not The Future Of Money

Crypto-currencies do not stand up as a new form of money says Mark Carney, Governor of the Bank of England, speaking on “The Future of Money“. That said, the underlying technologies and capabilities, have potential.

The long, charitable answer is that crypto-currencies act as money, at best, only for some people and to a limited extent, and even then only in parallel with the traditional currencies of the users. The short answer is they are failing.

They are poor stores of value, an inefficient media of exchange and are virtually non-existent units of account.

Authorities need to decide whether to isolate, regulate or integrate crypto-assets and their associated activities.

This is probably the strongest statement on the subject so far from a Central Banker.

But, whatever the merits of crypto-currencies as money, authorities should be careful not to stifle innovations which could in the future improve financial stability; support more innovative, efficient and reliable payment services as well as have wider applications.

The underlying technologies and capabilities, have potential, given the right regulatory frameworks.

Their core technology is already having an impact. Bringing crypto-assets into the regulatory tent could potentially catalyse innovations to serve the public better. Indeed, crypto-assets help point the way to the future of money in three respects:

Decentralised peer-to-peer interactions

Crypto-assets are part of a broader reorganisation of the economy and society into a series of distributed peer-to-peer connections across powerful networks.28 People are increasingly forming connections directly, instantaneously and openly, and this is revolutionising how they consume, work, and communicate.

Yet the financial system continues to be arranged around a series of hubs and spokes like banks and payments, clearing and settlement systems. Crypto-assets are an attempt to create the financial architecture for peer-to-peer transactions. Even if the current generation is not the answer, it is throwing down the gauntlet to the existing payment systems. These must now evolve to meet the demands of fully reliable, real-time, distributed transactions.

Underlying technologies offer to transform the efficiency, reliability and flexibility of payments.

The technologies underlying crypto-assets, particularly distributed ledger, can:

  • Increase the efficiency of managing data;
  • Improve resilience by eliminating central points of failure, as multiple parties will share replicated data and functionality;
  • Enhance transparency (and auditability) through the creation of instant, permanent and immutable records of transactions; and
  • Expand the use of straight-through processes, including with “smart contracts” that on receipt of new information, automatically update and if appropriate, pay.

These properties mean distributed ledger technology could transform everything from how people manage of their interactions with public agencies, including their tax and medical records, through to how businesses manage their supply chains.

A Central bank digital currency (CBDC) accessible to all.

Crypto-assets raise the obvious question about whether their infrastructure could be combined with the trust inherent in existing fiat currencies to create a central bank digital currency (CBDC).

Currently only banks can hold central bank money electronically in the form of a settlement account at the Bank of England. To be truly transformative a general purpose CBDC would open access to individuals and firms.

The Bank has an open mind about the eventual development of a CBDC and an active research programme dedicated to it. That said, given current technological shortcomings in distributed ledger technologies and the risks with offering central bank accounts for all, a true, widely available reliable CBDC does not appear to be a near-term prospect.

Moreover whether it is desirable depends on the answers to a series of big policy questions. While these are largely for another speech, I will note that a general purpose CBDC could mean a much greater role for central banks in the financial system. Central banks may find themselves disintermediating commercial banks in normal times and running the risk of destabilising flights to quality in times of stress.

There are also broader societal questions (that others would need to answer) such as how society balances privacy rights with the extent to which the information in a CBDC could be used to fight terrorism and economic crime.

Money in the digital age: what role for central banks?

Where do Crypto-curriences fit it? A Central Banker’s view.

Via The Bank For International Settlements. Lecture by Agustín Carstens
General Manager, Bank for International Settlements House of Finance, Goethe University Frankfurt.

One of the reasons that central bank Governors from all over the world gather in Basel every two months is precisely to discuss issues at the front and centre of the policy debate. Following the Great Financial Crisis, many hours have been spent discussing the design and implications of, for example, unconventional monetary policies such as quantitative easing and negative interest rates.

Lately, we have seen a bit of a shift, to issues at the very heart of central banking. This shift is driven by developments at the cutting edge of technology. While it has been bubbling under the surface for years, the meteoric rise of bitcoin and other cryptocurrencies has led us to revisit some fundamental questions that touch on the origin and raison d’être for central banks:

  • What is money?
  • What constitutes good money, and where do cryptocurrencies fit in?
  • And, finally, what role should central banks play?

The thrust of my lecture will be that, at the end of the day, money is an indispensable social convention backed by an accountable institution within the State that enjoys public trust. Many things have served as money, but experience suggests that something widely accepted, reliably provided and stable in its command over goods and services works best. Experience has also shown that to be credible, money requires institutional backup, which is best provided by a central bank. While central banks’ actions and services will evolve with technological developments, the rise of cryptocurrencies only highlights the important role central banks have played, and continue to play, as stewards of public trust. Private digital tokens posing as currencies, such as bitcoin and other crypto-assets that have mushroomed of late, must not endanger this trust in the fundamental value and nature of money.

The money flower highlights four key properties on the supply side of money: the issuer, the form, the degree of accessibility and the transfer mechanism.

  • The issuer can be either the central bank or “other”. “Other” includes nobody, that is, a particular type of money that is not the liability of anyone.
  • In terms of the form it takes, money is either electronic or physical.• Accessibility refers to how widely the type of money is available. It can either be wide or limited.
  • Transfer mechanism can either be a central intermediary or peer-to-peer, meaning transactions occur directly between the payer and the payee without the need for a central intermediary.

In conclusion, while cryptocurrencies may pretend to be currencies, they fail the basic textbook definitions. Most would agree that they do not function as a unit of account. Their volatile valuations make them unsafe to rely on as a common means of payment and a stable store of value.

They also defy lessons from theory and experiences. Most importantly, given their many fragilities, cryptocurrencies are unlikely to satisfy the requirement of trust to make them sustainable forms of money.

While new technologies have the potential to improve our lives, this is not invariably the case. Thus, central banks must be prepared to intervene if needed. After all, cryptocurrencies piggyback on the institutional infrastructure that serves the wider financial system, gaining a semblance of legitimacy from their links to it. This clearly falls under central banks’ area of responsibility. The buck stops here. But the buck also starts here. Credible money will continue to arise from central bank decisions, taken in the light of day and in the public interest.

In particular, central banks and financial authorities should pay special attention to two aspects. First, to the ties linking cryptocurrencies to real currencies, to ensure that the relationship is not parasitic. And second, to the level playing field principle. This means “same risk, same regulation”. And no exceptions allowed.

 

Peer to Peer – Scale and Scalability

From BankUnderground.

Peer to Peer (P2P) lending is a hot topic at Fintech events and has received a lot of attention from academia, journalists, various international bodies and regulators.  Following the Financial Crisis, P2P platforms saw an opportunity to fill a gap in the market by offering finance to customers and businesses struggling to get loans from banks.  Whilst some argue they will one day revolutionise the whole banking landscape, many platforms have not yet turned a profit.  So before asking if they are the future, we should first ask if they have a future at all. Problems such as a higher cost of funds, or limited ability to scale the business, may mean the only viable path is to become more like traditional banks.

Present Scale and Profitability

P2P activity has now been around for over a decade. The fastest growth has been in China, followed by the USA and the UK (total new alternative finance provision in 2016: China – $243bn,US – $35bn, UK – £4.6bn). P2P lending platforms offer an online marketplace and depend on both the external supply of investment and the demand for loans. Currently, platforms lack product diversification with their revenue deriving from origination and servicing fees. As such, platforms are extremely reliant on continuously attracting and matching loans for investors and borrowers.

Despite having substantial lending volumes, many big UK and US platforms are still making operating losses despite their rapid growth.

In the following two sections we explore two ways in which P2P platforms could make money: first, by scaling up their existing business models; and second, by changing their business models altogether.

Scalability

It may be that some platforms have made a conscious decision to favour growth over profitability for now, with a view to realising economies of scale. Central to this business model is i) whether there is sufficient appetite for the P2P investments and loans for the platform to reach scale and ii) whether their revenues can exceed the costs, even if they achieve higher scale.

Appetite for P2P products

P2P lending is still relatively young in the UK. As matchmakers, P2P lending platforms need to keep attracting new customers from both sides of the equation in order to grow. This is not a straightforward task: for example, supply of funds might be available but there might be lack of quality borrowers.  Or alternatively, they might have a slew of willing borrowers but are unable to tempt sufficient investors to finances them. A slowdown on either side affects platforms’ growth.

In the UK, P2P lending to businesses (mostly small to medium sized) is more substantial than to consumers. By contrast, in China and the US, P2P lending is mostly consumer focused.  On the supply side, retail investors have dominated the market, but the role of institutional investors has been increasing.

In the past few years, the market in the UK has been growing very rapidly, with year on year new lending growth in the region of 100%. But new data from the Cambridge Centre for Alternative Finance suggests that although activity is still growing, the pace of that growth has slowed down considerably.  In 2016, total new lending grew by roughly 50% in the UK and 20% in the US.

This, coupled with indications from some lenders that they are running into borrower constraints (i.e. their lending activity is being restricted by the low amount of potential borrowers), suggests that the traditional P2P lending model, that is widely used in the UK, may be reaching its limits. If growth rates continue to slow, platforms will find it more difficult to achieve the size necessary to fully realise their economies of scale.

One way that individual platforms may attempt to tackle this issue is through consolidation but given that the industry appears to be quite concentrated already (currently there are 3-4 major platforms that are key players), there might be only limited room for further concentration.  At the end, it may be that sustainable profitability may only be achievable with a few large lenders on the market.  Parallels can be drawn with other tech industries, where initially there were a number of players, but they gradually consolidated down to one or two market leaders (e.g. Google, Facebook).

Cost Structure

However, it might be the case that even following consolidation and hence larger scale, platforms still will not make money. For example, Uber is still unprofitable despite being a market leader on a large scale. And the two largest lendingplatforms in the US, who are dominant providers of P2P loans and several times larger than UK platforms, are also loss making.

The answer may lie in examining platforms’ cost structure more closely.  In theory, P2P platforms have operating cost advantages; they have no legacy costs, no requirement for branch networks and lower regulatory costs. At face value, these should be lower than for traditional banks. And, unlike traditional banks, these should be largely unrelated to scale- because, like other tech disruptors, their main cost is setting up and operating a platform. That opens up the possibility of undercutting traditional banks if platforms can achieve high enough lending volumes to overcome their fixed costs and realise these cost advantages.

But we must also think about the P2P lenders’ cost of funding- i.e. the interest rate they have to offer lenders to induce them to invest.  If this cost of funding is sufficiently higher, this could undo the advantage of lower operational costs. In reality, banks are able to borrow money at a lower cost, so even if P2P lenders have the slimmer cost base they may not be able to undercut banks, despite offering higher interest rates to borrowers.  Just scaling up might not be enough and some platforms may need to adjust their business model altogether…

Becoming a bank-like P2P platform

In the UK, P2P lending platforms have generally begun with ‘traditional’ or ‘pure P2P’ business models. But what started as pure and simple has been continuously evolving. Platforms have already been experimenting with new business models and techniques.

Some “P2P” platforms have had success operating a ‘balance sheet’ lending model. These platforms’ business model is not pure peer-to-peer lending, because the bank itself co-invest with investors, putting their ‘skin in the game’.  To do this successfully, such platforms tend to also concentrate on a particular lending market (e.g. property lending).

Other platforms might turn to traditional banking (one of the leading UK platforms is already applying for a banking licence) perhaps to diversify range of services they offer. For example, platforms will be able to offer FSCS-protected deposit accounts for savers and personal loans, car finance, and credit cards for borrowers, alongside their P2P products. Another reason is that FSCS protected deposits mean lower funding costs. Guaranteed deposits also mean that platforms can be listed on “best buy” comparison portals for savings account, creating a new potential market to tap for funds.

A natural question that follows – is there a fundamental difference for customers between banks and P2P platforms? The answer is not straightforward. Undoubtedly, platforms offer some distinct benefits for investors compared to banks– an opportunity to lend directly to businesses and retail consumers with relatively small amounts of investment, and achieve a higher rate of interest than is available from traditional savings accounts. On the other hand, banks offer deposits that are covered by the FSCS and so are less risky to P2P investors (although less risk averse investors might find that a better place to be on the risk-return trade-off).

On the borrowers’ side, there may be less differentiation. Our internal analysis (Chart 2) shows that interest rates on personal loans arranged via P2P platforms are competitive but not significantly lower than the rates available from banks. The exception is low-value loans (i.e. around £1-2k), where banks’ manual processes and fixed costs, make it uneconomical for them to compete.

But P2P platforms are not the only ones adapting. As P2P lenders start to become more like banks, banks are starting to become more like P2P lenders in some respects.  To counter the possible competitive threat from P2P lenders, banks have started to offer quicker and more user-friendly loan applications services (including quick, all-digital SME lending services). For a potential borrower, the difference between a P2P platform and a bank becomes less obvious. So, to stay in the game, platforms will need to compete for borrowers’ attention by offering a wider range of bank-like services.

Ironically, it might be the case that as much as the platforms have wanted to disrupt the banking model, they might need to turn towards it to grow and to achieve profitability.

Conclusion

Ultimately, the feasibility of scaling up depends on the balance of the two factors: a continuous appetite for P2P investments and loans, and whether the revenues can be higher than the costs when they do scale up.

The answer could be that platforms will need to consolidate and adjust their business models if they wish to have a significant and lasting presence in the financial system. A key part of this may be turning to banking: whether via partnering with banks or by offering bank-like services themselves. Peer-to-peer lending might be changing the world, but perhaps it will have to change itself first.

Bank Underground is a blog for Bank of England staff to share views that challenge – or support – prevailing policy orthodoxies. The views expressed here are those of the authors, and are not necessarily those of the Bank of England, or its policy committees.

Banks Hamstrung by Digitisation

From InvestorDaily.

The global banking sector, which has benefited from its “inertia” for decades, is under cost pressure as it attempts to reconcile legacy IT systems with a newly ‘digitised’ front end, says Ariel Investments.

Speaking in Sydney, Chicago-based Ariel Investments director of research for international and global equities Chaim Schneider said banks are transforming “tremendously” as they shift from ‘offline’ bank branches to the online world.

“Banks are absolutely making a lot of investments in mobile, in adapting to the new paradigm, because clearly the new world around them is changing tremendously and rapidly,” Mr Schneider said.

“But the problem is that banks were not designed for this way,” Mr Schneider said.

Many banks were still using IT architecture that could be up to 50 years old in a “coding language which was not designed for an omni-channel world,” he said.

“They have these back ends, these core banking platforms, that are sub-optimally established, and then they have this front end, where they’re investing heavily in digital and mobile and online banking, and they need to bridge the gap between the two.

“But in doing so, this patchwork comes at a significant cost. Part of it has to do with simply the infrastructure costs with making these investments, which are huge.”

A further cost-related pressure on banks was the open banking regime and the increased competition this would bring to the sector, he added.

“Banks, more than anything else, benefit from inertia. And you can hope in some ways, this threat will be mitigated by other factors, but either way it’s a real threat challenging the costs of these institutions.”

Banks are also looking to “future-proof” their branch networks, he said.

“Effectively, banks around the world are taking steps to reshape the branch network to future-proof their branch network,” Mr Schneider said.

“But there are significant limits and constraints on their abilities to do so.”

He said the closure of bricks-and-mortar bank branches was often seen as a cost-cutting measure – but that this was in fact hurting banks in other ways.

“Strange as this may seem, the majority of people around the world in country after country look at that bank that they may pass on their way to work every day and believe their cash, their deposits, are inside the vault in that bank.

“We all know the way banking systems work these days doesn’t exactly work that way. But that is truly ingrained in [the] mindset of people around the world,” Mr Schneider said.

“And what that means is when that bank branch closes, banks in that region sometimes have a problem sustaining those customer relationships amongst both retail customers as well as small businesses who just like the presence, the comfort, of driving by their bank on a regular basis.”

He also pointed to the “very important role” bank branches played as “deposit-gathering frameworks for banks”.

“The raw material for any bank is deposits. Without deposits, banks can’t make loans. And the branches play a mission-critical role in deposit-gathering, in particular low-cost deposit gathering.

NAB Ventures invests in BRICKX

The fractional property investment property BRICKX has today announced that NAB Ventures, National Australia Bank’s corporate venture capital arm, has invested in the home-grown startup as part of  a $9 million (AUD) Series A funding round.

Here is an ABC segment on BRICKX from 2017, which discusses the concept.  It can either be seen as an innovative way to facilitate housing affordability, or the ultimate in the financialisation of property. You decide!

BRICKX is a revolutionary new and affordable way for Australians to invest in residential property by buying ‘Bricks’ in a BRICKX property. This unique approach, called fractional property investment, means people can invest in quality residential properties for as little as a few hundred dollars.

NAB Ventures – which supports businesses and entrepreneurs in their quest to build leading technology companies – saw the investment potential in democratising property ownership in the Australian market.

The value of BRICKX in providing Aussies with a financial stepping stone to achieve the Australia Dream of home ownership was another driver for investment.

Anthony Millet, BRICKX CEO, said: “Housing affordability continues to be a priority for Australia’s banks, so the alignment between BRICKX and NAB Ventures has a strong and unified purpose. BRICKX is expanding rapidly and this high profile and experienced group of investors will help us in our goal to assist millions of Australians to get their foot onto the property ladder.

“The prohibitive costs and high deposits needed to gain access to the property market, has left many out in the cold. However, BRICKX has opened up the residential property asset class as an alternative investment to any of the traditional investment options.

“Considering the recent volatility in the cryptocurrency and stock markets, Australians are recognising the longer-term stability of property prices.”

Todd Forest, Managing Director of NAB Ventures, said he was excited to invest in BRICKX and help support a new way of approaching property ownership.

Forest said: “The aspiration for property ownership has long been the Australian dream, however buying a property often has challenges for many consumers – such as the rise in house prices in some areas and trying to save for a deposit.

“BRICKX is disrupting this journey, in how it provides consumers with access to the property market and engages with them in creating a pathway to property ownership.

“NAB Ventures has scanned the market globally, to identify a range of companies and business models that attempt to solve for this, particularly as NAB looks to support its customers along the full home ownership journey.

“NAB Ventures chose BRICKX as the leading opportunity, based on its unique business model, exceptional user experience and the opportunity to leverage the platform to innovate and help more Australians with their home ownership goals.”

Founded in 2014, BRICKX launched to retail investors in September 2016 and has grown to offer 14 properties across Sydney, Melbourne and Adelaide along with more than 9,000 members.

The NAB Ventures investment in the ‘Series A’ funding round for BRICKX follows on from Westpac’s Reinventure investment in late 2017. Since the Reinventure investment, the number of BRICKX members has grown by nearly 25%.

Artificial intelligence (AI) in finance: Six warnings from a central banker

Prof. Joachim Wuermeling Member of the Executive Board of the Deutsche Bundesbank spoke about AI.  Consumers may be rated by AI when applying for a mortgage. Pooling data points from internal transactions, social networks and other sources provides a more meaningful picture of banks’ borrowers. But if too much trust is put in “intelligent” systems, the stability of financial markets may be at stake.

1.  Don’t miss out on the opportunities of AI in finance …

AI in finance could impact on the functioning of our financial system in a profound way. Some suggest that AI is enhancing the power of the human brain in the same way that electricity enhanced the power of the body 150 years ago. Hence, it could become a big thing in finance.

Artificial intelligence and big data are currently the strongest and most vivid innovation factors in the financial sector. Using AI in finance may trigger dramatic improvements in many businesses. AI elevates the role of data as a key commodity. Used wisely, big data make outcomes more reliable and may improve financial mediation. Process chains can be organised in new ways. “The scope and nature of banks’ risks and activities are rapidly changing,” as a recent Basel Committee analysis puts it.

This evolution towards increased use of non-human intelligence is not something that has just occurred in the last few years. The first invention of neural networks, a central pillar of most AI systems, dates back to the year 1943.

Until a few years ago, the main users of big data and AI in the area of finance were certain hedge funds and high-frequency trading firms. In recent times, the application of AI in finance has begun to spread widely, via “normal” banks, FinTechs and other financial service providers, to the general public.

Since 2011, HFT has accounted for about 45–50 % of all trading in US equities. The figures for the main European indices are in the same region (with about 40 % for German DAX futures). Taken together with all other “normal” algorithmic trading activities, we currently estimate the amount of algorithmic trading to be in the realm of 80–90 % of the entire trading volume for equities (and somewhat less but still very high in other market segments).

A single normal trading day generates about 3–6 million data points about prices, order deletions and modifications in DAX futures alone. No human can analyse these amounts of data simply by looking at them in an Excel spreadsheet. More sophisticated and sometimes also AI-driven techniques are necessary to do the job.

AI profoundly changes the functioning of our financial system in at least three areas: products, processes and analysis. This is true for both front office functions (eg customer business, trading) and back office functions (eg executing trades, risk management, market research). Special-purpose AI can solve specific problems, eg in customer engagement, financial management or cybersecurity.

Applications focused on market operations cover various core areas eg trading, portfolio composition, backtesting and validation of models, market impact analysis, modelling trading of large positions and stress testing. Dynamic portfolio adjustment, depending on the macro environment, may be strengthened by AI.

With the help of AI, various human shortcomings in dealing with finance can be mitigated. As behavioural finance has taught us, biases, inertia and ignorance lead to the malfunctioning of markets. AI allows humans to reach out beyond their intellectual limits or simply avoid mistakes.

2 … but beware of the risks

But opportunities are always accompanied by risks. As regards the financial system, if too much trust is put in “intelligent” systems, the stability of financial markets may be at stake. The workings of AI can be a mystery; it can trigger loss of control, make fatal errors, and have a procyclical effect due to its mechanistic functions. Pattern recognition has its limits. This can be dangerous particularly in crisis scenarios. An autopilot would never have been able to land a jet on the Hudson River. Nor can algorithms stabilise in periods of financial stress.

Looking at the recent turbulence in equities and the market for VIX-related financial products, it can be concluded that the events of 5 February share many similarities with a “flash crash”. Unfortunately, as with the original flash crash of May 2010, we have only limited knowledge about the direct drivers that triggered the event. It can be assumed that algorithmic market participants were quite active during the relevant period. But as to which strategies were applied and to what effect, we have no knowledge so far. The rise in volatility in the S&P 500 then nearly instantly affected the VIX industry, making it not the cause but more the first victim of this market event, with losses up to 95 % on assets. We do not expect this phenomenon to disappear in the future. On the contrary, more of these flash events are to come.

AI is still in its infancy. Continuous processes for the entire AI lifecycle still have to be defined and scaled for business needs. That means that AI must be embedded in the process of acquiring and organising data, modelling, analysis and delivering analytics. The skills gap, particularly with regard to data science and machine learning expertise, is the foremost challenge. At this stage, non-human intelligence is far from replacing the human brain in any respect. Computers are like school pupils dividing numbers mechanically without having understood what they were doing.

3. Consumers should take care: they remain the risk-takers

What makes this development so significant is the fact that it is not just occurring at the level of systemic institutions, markets and stock exchanges. With robo advisers, for example, AI can directly influence and control the daily financial decisions of customers and ultimately their personal wellbeing. Society has barely begun to understand the economic, ethical and social implications of AI.

While client interaction is made more convenient by mobile banking, chatbots or virtual customer assistants, banks can find out more about customer habits and provide them with tailor-made financing.

Consumers may be rated by AI when applying for a mortgage. Pooling data points from internal transactions, social networks and other sources provides a more meaningful picture of banks’ borrowers. But denials may be hard to understand. It may become even harder to challenge a decision made by algorithms.

The proper functioning of the applications is not a given. Simple flaws, cyberattacks and criminal behaviour render the systems extremely vulnerable. Consumers should be cautious. They need to be protected. Laws may have to be modified to cover new threats. Responsibility and liability in the case of malfunctioning machines have to be clarified.

4. FinTechs should not ignore the legitimate concerns of society and supervisors

Agile tech companies are driven by an admirable energy and inspiration. By nature, they take risks. They create an idea, build a prototype and try it out immediately in the real world. Regulation, supervision, obligations and requirements must make them extremely nervous.

But the wellbeing of society depends on rules. The public demands cybersecurity, data privacy, consumer protection and financial stability. FinTechs should not brush aside the concerns of their stakeholders. Business can only flourish if it is broadly accepted by citizens.

FinTechs usually pick up specific elements of the work chain of finance or create new features. Using technology, they modularise and customise products as a third party or standalone provider.

FinTechs are part of the finance sector but are not necessarily supervised. As long as they carry out tasks for supervised entities, these institutions are responsible for the behaviour of the FinTech.

5.  AI needs new forms of supervision

“Artificial intelligence” may sound glamorous from a technological perspective, but in banking supervision, the well-established principle of “same business, same risk, same rules” has so far proved to be a sound standard for innovations. Whether they employ AI themselves or outsource it to FinTechs, from the supervisors’ point of view responsibility remains entirely with the bank.

For German supervisors, IT governance and information security nowadays are equally as important as capital and liquidity requirements.

All financial institutions should address the risks posed by new technologies. Banks have to implement effective control environments needed to properly support key innovations. This includes the requirement to have appropriate processes for due diligence, risk assessment and ongoing monitoring of any operations outsourced to a third party.

The European MiFID II includes the requirement that firms applying algorithmic models based on AI and machine learning should have a robust development process in place. Firms need to ensure that potential risks are considered at every stage of the process.

Regulators increasingly have to apply AI-supported analytical methods themselves to recognise vulnerability patterns, scan lengthy reports or analyse incoming data.

In any case, we must strike a balance between financial stability and avoiding barriers for potential new entrants, products and business models. Alongside technological progress, regulators have to constantly reassess the current legal framework, supervisory models and resources.

6. Central banks should embrace AI

Central banks have access to huge amounts of very valuable data stemming from market operations, supervision, payments and statistics. They are well positioned to tap the benefits of AI so they can enhance their ability to fulfil their mandate for price stability and the stability of the financial system.

Machine learning is already being used at the Bundesbank in different narrow segments. The experiences of all users have been good without exception. While monitoring the technical progress, we are currently discovering further use cases and defining our AI foundation, strategy, organisation and processes.

Here is a list of examples, which is by no means exhaustive:

In risk management, neural networks assess and evaluate the financial soundness of the markets. Market research is supported by adopting web mining techniques and machine learning in content analysis, topic modelling and clustering of relevant articles. In statistics, machine learning enables new methods for data quality management, eg in the context of securities holdings or the classification of company data. Furthermore, the informational content of seasonality tests is assessed by a random forest machine learning technique. For our IT user help desk, the handling of routine requests via automated chatbot responses could be a useful support measure. We use social media data to detect trends, turning points or sentiments. Machine learning methods can be applied for variable selection purposes in econometric models.

ANNEX: Use case – monitoring of real estate markets

An interesting data source is internet platforms. For example, some rental and housing platforms have the potential to improve the analysis and monitoring of real estate markets via the provision of information such as list prices and structural and locational characteristics of the property market at a disaggregated level.

This is mainly based on the assumption that these data contain information on the expectations and interests of economic agents with respect to future decisions. In such contexts, a wide range of topics or “search strings” are often potentially relevant. This can result in many different, highly correlated time series.

Furthermore, the “textual analysis” method is increasingly applied in research, as large amounts of “unstructured” information on businesses and the economy are available electronically on the internet. In order to operationalise textual data for econometric analysis, machine learning algorithms can be helpful. Learning methods can be applied to classify textual documents into different categories which can then be used to draw statistical inferences.

 

ANZ partners with Data Republic to speed up innovation

ANZ has announced a strategic investment and partnership with local start-up Data Republic to speed up innovation through secure data-sharing environments.

The partnership will provide ANZ access to the Data Republic platform, which delivers a ‘data sharing control centre’ for organisations to store, categorise and share data while maintaining strict governance and auditing frameworks. ANZ will be able to use the platform to share data with trusted third parties in a secure and well-governed environment.

Announcing the partnership, ANZ Chief Data Officer Emma Gray said:

“Using data analytics and insights to deliver better customer outcomes more often is an essential part of how we need to operate in the digital economy.

“This partnership allows us to get more out of the data we already have, but in a safe and secure environment that provides the highest levels of governance.

“Through the cloud-based platform we will now be able to access trusted experts and other partners to develop useful insights for our customers in hours rather than months,” Ms Gray said.

Data Republic CEO Paul McCarney said: “We are very excited to welcome ANZ as both a strategic investor and technology client.

“ANZ clearly understand the importance of secured data sharing practices in today’s data-driven economy.

“This partnership is about ANZ investing in the right technology to future-proof their data collaboration capabilities and will ultimately position ANZ to overcome many of the challenges and potential risks associated with open data, data sharing and the Federal Government’s recently announced Open Banking reforms.”

ANZ will start using the Data Republic platform from late March to develop greater customer insights and a series of operational improvements.