Category: Digital Disruption

Super to become ‘hyper-personalised’: Bravura

From Investor Daily.

Superannuation funds are in the process of collecting “unprecedented” amounts of member data that will be used to create hyper-personalised services, says Bravura.

In a new report titled Super Megatrends, Bravura superannuation product manager Scott Kendall said big data and predictive analytics are becoming essential tools for super funds that are looking to retain members.

A better understanding of member data can also improve the risk management and compliance processes of the big superannuation funds, Mr Kendall said.

“Modern technology platforms are facilitating the collection of unprecedented amounts of member data, enabling detailed member profiling in an effort to deliver hyper-personalised service offerings,” he said.

Super funds are employing techniques such as behaviour modelling to identify members who are at risk of leaving the fund, Mr Kendall said – and scenario stress testing can help funds better manager investment risk.

“As their use of big data becomes more sophisticated, all kinds of businesses are drawing upon information from other external data sources, such as social media and data aggregators, to gain an even more intimate knowledge of their customers,” he said.

Mr Kendall pointed to Mercer’s Harmonise platform in the UK, which provides an aggregated view of members’ finances through their employer.

“Participating funds have access to far greater amounts of member information than ever before and the additional data captured via this loop can be applied to deliver more meaningful, hyper-personalised services to their members,” he said.

Super funds will also start investigating artificial intelligence services such as ‘cognitive computing’, Mr Kendall predicted.

“Various insurers are already employing the services of IBM Watson to process large amounts of data to achieve better underwriting, fraud detection and credit control,” he said.

“It’s only a matter of time before super funds follow suit.”

Why your bank will ask you to pick a ‘PayID’

From The New Daily.

From October this year, bank customers will be able to replace their clunky BSB and account number with an email address or mobile phone number, according to experts.

This ‘PayID’ will be a crucial part of Australia’s new payments system, which will allow almost instant bank transfers, 24 hours a day, 365 days a year.

The Reserve Bank, which operates the ageing system that clears payments between accounts, has been busily working for years with big players in the industry on the billion-dollar ‘New Payment Platform’ or ‘NPP’.

There’s plenty of complicated stuff happening behind the scenes, but one of the main things Australians should know is that, from October, they’ll be able to pick a PayID for each bank account they want to receive super-fast payments into.

NPP Australia CEO Adrian Lovney told The New Daily that the PayID concept will make account numbers easier to remember, and remove the risk of accidentally sending money to the wrong person.

“It makes payments more intuitive and simpler because users will be able to provide payers details which are easy to remember such as an email address or phone number,” Mr Lovney said.

“This offers greater peace of mind as people no longer have to rely on providing financial account information, such as a BSB and account number, to payers so they can receive payments.

“And services that use PayID may display a PayID name before you send a payment as an additional level of confirmation that you are sending money to the right person.”

So, if a family member wants to send you money or you’re splitting the bill at a restaurant with friends, you can simply tell them to type your PayID into their online banking and, in about 15 seconds, the money will be in your account.

The new system will be so fast and simple, it has been speculated that credit cards and cash will lose popularity.

It was built by the Society for Worldwide Interbank Financial Telecommunication (SWIFT), which also built Australia’s current payment system in 1998.

It will allow real-time processing for all ‘push’ payments (such as wages, welfare payments, bill payments or transfers to friends and family), but won’t speed up ‘pull’ payments made on debit and credit cards.

Payments expert Nathan Churchward, whose employer Cuscal is one of the 13 companies working on the new system, predicted that PayID could become as popular a brand name to Australians as Google and Uber.

He gave a real-world example: he recently bought $2500 worth of tickets for a group of friends and accidentally gave them all his wrong bank account number. Their payments bounced back and he was left wondering why no one was paying up.

“I work in banking. You’d think I’d be able to remember my account and BSB. But I can’t!” Mr Churchward told The New Daily.

“With PayID, if you get the mobile number wrong, it will ask you if you want to pay ‘Joe Bloggs’ and you’ll realise and won’t proceed.”

Businesses also won’t have to “splash” their bank details all over the internet, where fraudsters lurk, he said.

Cuscal’s hot tips on PayID

  • You can’t pick a random number. Banks will probably require a mobile phone number, email address, Australian Business Number (ABN) or Australian Company Number (ACN)
  • You can set multiple ‘PayIDs’ for the one bank account. For example, your mobile phone number and email could both be linked to the same transactions account
  • However, you can’t link the same PayID to multiple accounts
  • Every PayID will be changeable. So if you get a new phone number, you’ll be able to ask your bank to change your PayID to the new number
  • If you switch to a new bank, you’ll be to reuse an old PayID. But any direct debits you’ve set up won’t automatically transfer across
  • Some institutions may restrict PayIDs to specific account types. So you might be able to link to a debit card account, but not a mortgage offset or term deposit
  • Your institution may not offer PayID straight away in October

Harnessing machine learning in payments

Good article from McKinsey on the revolution catalysed by the combination of machine learning and new payment systems as part of big data. The outline some of the opportunities to expand the use of machine learning in payments range from using Web-sourced data to more accurately predict borrower delinquency to using virtual assistants to improve customer service.

Machine learning is one of many tools in the advanced analytics toolbox, one with a long history in the worlds of academia and supercomputing. Recent developments, however, are opening the doors to its broad-scale applicability. Companies, institutions, and governments now capture vast amounts of data as consumer interactions and transactions increasingly go digital. At the same time, high-performance computing is becoming more affordable and widely accessible. Together, these factors are having a powerful impact on workforce automation. McKinsey Global Institute estimates that by 2030 47 percent of the US workforce will be automated.

Payments providers are already familiar with machine learning, primarily as it pertains to credit card transaction monitoring, where learning algorithms play important roles in near real-time authorization of transactions. Given today’s rapid growth of data capture and affordable high-performance computing, McKinsey sees many near- and long-term opportunities to expand the use of machine learning in payments. These include everything from using Web-sourced data to more accurately predict borrower delinquency to using virtual assistants to improve customer service performance.

Machine learning: Major opportunities in payments

Rapid growth in the availability of big data and advanced analytics, including machine learning, will have a significant impact on virtually every part of the economy, including financial services (exhibit). Machine learning can be especially effective in cases involving large dynamic data sets, such as those that track consumer behavior. When behaviors change, it can detect subtle shifts in the underlying data, and then revise algorithms accordingly. Machine learning can even identify data anomalies and treat them as directed, thereby significantly improving predictability. These unique capabilities make it relevant for a broad range of payments applications.

What is machine learning?

Machine learning is the area of computer science that uses large-scale data analytics to create dynamic, predictive computer models. Powerful computers are programmed to analyze massive data sets in an attempt to identify certain patterns, and then use those patterns to create predictive algorithms (exhibit). Machine learning programs can also be designed to dynamically update predictive models whenever changes occur in the underlying data sources. Because machine learning can extract information from exceptionally large data sets, recognize both anomalies and patterns within them, and adjust to changes in the source data, its predictive power is superior to that of classical methods.

 

Machine learning has already established a strong foothold in credit cards, particularly in fraud management. PayPal’s Braintree Auth payments tool, for example, uses PayPal’s consumer transaction data in conjunction with software developer Kount’s fraud detection capabilities to authorize high volumes of transactions and verifications in near real-time. Each credit card transaction or verification is analyzed in milliseconds using hundreds of fraud detection tests.

There are several other areas in the payments value chain where machine learning is adding significant value:

  • Product sales: Machine learning can be a powerful tool for developing deeper insights about customers and sales prospects because it can draw upon a wider variety of internal and external data than marketers have traditionally used. It can more accurately cluster customers and prospects into segments according to their profiles and probable needs. This deeper insight can reveal new opportunities for cross-selling and up-selling among both customers and prospects. McKinsey finds that with machine learning payments providers can increase revenue from existing customers by 10 to 15 percent.
  • Customer retention: Companies typically monitor and forecast customer churn based on changes in account status; when churn rates rise they take steps to address the problem. Now, through machine learning, they can identify those customers they are at risk of losing and act quickly to retain valuable customers. For example, 47Lining, an Amazon Web Services partner, uses a combination of site behavior, demographics, and media-sentiment measures to predict customer churn with 71 percent accuracy. Companies using machine learning to address customer churn have achieved reductions of as much as 25 percent.
  • Collections: Collection practices and debt restructuring work best when closely aligned with borrowers’ changing circumstances and propensity to pay. Machine learning can help companies build robust dynamic models that are better able to segment delinquent borrowers, and even identify self-cure customers (that is, customers that proactively take action to improve their standing). This enables them to better tailor their collection strategies and improve their on-time payment rates. TrueAccord’s HeartBeat, for instance, is a machine learning tool that helps lenders customize personal interactions in real time, based on its ability to detect why a customer’s payments are late. Companies using machine learning have been able to reduce their bad debt provision by 35 to 40 percent.
  • Treasury pricing: In commercial payments, companies can capture 10 to 15 percent more revenue through optimized treasury pricing. In the near term, advanced analytics can identify quick-win opportunities to reduce price leakage (such as discounts exceeding authorized limits) and billing errors. Over the long term, clustering techniques built on machine learning can significantly improve customer segmentation and lead to more appropriate pricing models.
  • Customer care: Over time, McKinsey expects to see a gradual increase in the automation of many customer services. This is an area in which the cognitive intelligence capabilities of machine learning are particularly well suited. Among the benefits are: lower servicing costs, enhanced agent performance, more efficient capacity management, improved digital customer experience, reduced risk, and elimination of waiting times. A variety of relevant applications are already available, including virtual assistants that use natural language processing, deep insight tools like IBM’s Watson, and cognitive engines that can do things presently handled by humans, such as IPSoft’s Amelia, which can understand and interact with people (see sidebar, “Cognitive agents”).

Machine learning in the card collection environment

Cognitive agents

Cognitive agents like IPSoft’s Amelia combine natural language and deep insight technologies to complete tasks typically handled by humans. Using a three-step process, they help companies intelligently automate a variety of tasks:

  1. Understand: Cognitive agents can rapidly absorb and comprehend a diverse range of data sets, from complex manuals to call logs and flow charts.
  2. Learn: Cognitive agents absorb data from the customer language they process, and can refer the customer to a live agent language they process, and can refer the customer to a live agent when uncertain about how to react. They also learn from cases they refer to agents, further improving effectiveness as they continue to record interactions and data.
  3. Resolve: Finally, cognitive agents can directly resolve customer inquiries received through online chat, mobile, and voice channels. Alternatively, when connected to a backend system they can support live agents in resolving customer issues.

Service automation tools can help payments providers increase customer satisfaction, enhance financial performance, and improve compliance.

In card issuing, machine learning is already having a valuable impact. This is especially true in collections, where McKinsey has seen 10 to 15 percent improvements in recovery rates and 30 to 40 percent increases in collections efficiency. To minimize delinquencies, issuers can use individual-account pattern recognition technologies, and develop contact guidelines and strategies for accounts that are already delinquent. Following an account delinquency, issuers allow a brief time window (usually 90 days) before they write off the receivables and turn collection over to third-party providers. This brief period is an ideal time for issuers to apply collection strategies that draw heavily on the capabilities of machine learning.

 

Bank launches AI ‘home loan assistant’

From The Adviser.

A new chatbot has been launched by UBank to help answer customers’ home loan questions, in what is being billed as Australia’s “first virtual assistant for home loan applications”.

RoboChat has been launched by the NAB-owned online bank as a “virtual assistant to help potential homebuyers and refinancers complete their online home loan applications” and “simplify the home loan application process”.

The new chatbot has been built by IBM Watson to act as a virtual assistant to aid customers through the home loan application form.

According to UBank, the bot — which is still “in training” — aims to “help reduce the time needed for customers to complete the form by offering on-the-spot help”.

It has been trained on data collected from customer questions submitted via UBank’s LiveChat experience, and has been tested by “dozens of users and iteratively trained”.

UBank has said that the bot’s artificial intelligence will “continue to learn as more customers engage with it, becoming smarter and more user-friendly over time”.

However, the bank has been emphatic on the fact that the bot “won’t affect the size of the local UBank customer service team or the great work they do”.

Speaking of the product, the CEO of UBank, Lee Hatton told The Adviser that RoboChat was created to help customers overcome the “fear of how long it will take to fill out the applications”.

When asked if the platform was meant to emulate what a broker does, Ms Hatton said: “RoboChat was designed specifically to guide customers through UBank’s online home loan application form.

“UBank doesn’t use mortgage brokers, so RoboChat, along with our experienced advisers, can help customers as they decide which home loan is right for them.”

Ms Hatton added that the chatbot is available 24 hours a day, 7 days a week so can provide real-time answers, but customers can still use a bank adviser to assist with “more specific support or [questions] fitting to [an] individual situation”.

Putting RoboChat to the test

Once a user selects that it wants to start a home loan application, the RoboChat option opens up alongside the online application process. It is here that users can ask the bot questions. The website notes that once an application is completed (with questions answered), a home loan specialist will be in touch to move the process forward.

When first opened, the chatbot introduces itself as Ubank’s “beta virtual assistant”, adding that it is “still in training” and will “do [its] best to answer your home loan questions”. It says you can speak to a real person at any time.

The Adviser tested out the platform, and it successfully answered simple questions such as “What is your variable rate?’ (with that answer being 3.74 per cent) and “Do you have an offset?” (which it doesn’t), as well as “What are your charges?” (it doesn’t have any unless the loan is fixed rate) but struggled when the next question was a new subject (“Can I have an interest-only loan?”), responding that it was “wasn’t expecting that kind of response”.

However, once establishing that the user was still interested in interest rates, and ascertaining that the user was an owner-occupier, the bot asked if it was for principal and interest, or interest-only. Once interest-only was chosen, the bot produced the interest rates with comparison rates (4.13 per cent with the Real Reward Offer or 4.46 (4.39 per cent comparison) without).

When asked “Should I use a broker?” the RoboChat asked for the question to be rephrased. When asked “Do you have a broker?”, it said: “We’re an online bank so we don’t have branches, mobile lenders, or use brokers. The upside is we have fewer costs than traditional banks and we pass the savings onto you. Do you want to know more?”

It also understood “Can you tell me your LVR?”, but struggled with “What is your LVR?”, and didn’t understand “Can I refinance?” but knew the answer to “How do I refinance?”.

When asked “How much can I borrow?” RoboChat said: “I can’t perform calculations just yet” and offered links to tools and calculators.

The platform handled other questions, such as “I’m self-employed. Do you have a loan for me?”, responding “To ensure we’re a good match, your primary income needs to be from an Australian PAYG source of a company not owned by you. And, all applicants must be either an Australian citizen OR Australian Permanent Resident OR a NZ citizen AND reside in Australia. As a bank, we’ll always need to perform a credit assessment based on the details of each applicant.”

While the bank has said that it has in-built humour and offers “tongue-in-cheek responses”, the bot was not able to respond to the joke as outlined in the press release (“How much does a hipster weigh?” Answer: “An Instagram”), instead saying “Hmmmm, I’m a bit confused. Can you rephrase your question?” When asked for the meaning of life, it did say, however, “Simpler, better, smarter is my life motto!”

Ms Hatten commented: “Our goal is to deliver simpler, better, smarter banking to our customers and RoboChat will help deliver on this by streamlining the application form.

“RoboChat will be a very welcome addition to our team of customer service experts” continued Hatton.

“UBank will still have experienced staff on hand to chat on the phone, via email and our live online chat offering, RoboChat will provide an added option for those needing quick online responses or those that are close to finalising the form.”

Brock Douglas, vice president of Watson for IBM Asia Pacific added: “From deepening the customer experience, to increased productivity for employees, virtual assistants are being adopted across industries and becoming more advanced in natural conversation and emotional intelligence, with the help of cognitive technology.

“UBank’s work with IBM Watson is a powerful example of how organisations are leveraging cognitive virtual assistants that have the ability to engage in a conversation, ask questions, learn and respond in context – as opposed to providing stock responses.”

Even God now accepts digital payments

From Bloomberg.

In the most cashless society on the planet, even God now accepts digital payments.

A growing number of Swedish parishes have started taking donations via mobile apps. Uppsala’s 13th-century cathedral also accepts credit cards.

The churches’ drive to keep up with the times is the latest sign of Sweden’s rapid shift to a world without notes and coins. Most of the country’s bank branches have stopped handling cash; some shops and museums now only accept plastic; and even Stockholm’s homeless have started accepting cards as payment for their magazine. Go to a flea market, and the seller is more likely to ask to be paid via Sweden’s popular Swish app than with cash.

“Fifteen years ago I would withdraw my entire salary and put it in my wallet, so I knew how much I had left, but these days I never really carry cash,” said Lasse Svard, the acting vicar at the parish of Jarna-Vardinge, about 50 kilometers (31 miles) south of Stockholm.

Vanishing Act

Swedes’ aversion to cash is increasingly showing up in money supply data. According to Statistics Sweden, notes and coins in public circulation dropped to an average of 56.8 billion kronor ($6.4 billion) in the first quarter of this year. That was the lowest level since 1990 and more than 40 percent below its 2007 peak with the pace of the decline accelerating to its fastest ever in 2016.

According to the central bank, which is also studying whether to launch its own digital currency, the main reason for the disappearing act is technical innovation.

Riksbank Deputy Governor Cecilia Skingsley notes that Swedes were early adopters of both personal computers and mobile phones (remember those Ericsson phones?), and that the country’s banks were quick to create sector-wide structures such as debit cards, credit cards and Swish, which has 5.5 million users and is owned by the country’s largest banks. Swedes also seem to trust those systems, she said in a recent interview in Stockholm.

Innovation Drive

“A drive for innovation has been created in Sweden to come up with cost-effective and user-friendly alternatives to cash,” Skingsley said. Cash is likely to “more or less disappear” as a means of payment in the private sector, she said.

 

But a cashless society is not without its challenges or critics.

Many pensioners are struggling to make payments in an online world, while privacy campaigners lament the fact that the state is acquiring greater control over what its citizens do. There are also concerns about the vulnerability of a cashless society in the event of an attack or major blackouts.

It seems that for now, the benefits — including lower business costs, more control over tax revenue and greater safety from criminals — outweigh the drawbacks.

Policy Impact

In fact, Swedes have become so averse to cash that they’ve even been shunning it during the current regime of negative interest rates.

“The fact that people chose to hold so little cash in their wallets despite getting zero interest on their bank accounts emphasizes the strength of this trend even more,” Robert Bergqvist, chief economist at SEB AB, said by phone.

So far, the development has had little impact on monetary policy. But if cash were to disappear entirely, it would give extra powers to the central bank.

“Negative interest rates would be more powerful as there wouldn’t be any way to escape their impact,” Bergqvist said. “In a sense it would give the Riksbank more room for maneuver.”

 

Fintech sector receives federal budget ‘boost’

From Investor Daily.

The government has announced initiatives to increase competition in Australia’s fintech sector, increasing access to capital for small businesses and expanding the AFSL exemption for start-ups.

As part of the federal budget handed down this evening, the government and Australian Prudential Regulation Authority (APRA) announced there will be a reduction in barriers for new banks and a focus on increasing competition to drive lower prices and a better service for consumers.

The government will look to relax the legislative 15 per cent ownership cap for innovative new entrants, and will also lift the prohibition on the use of the term ‘bank’ by Authorised Deposit-taking Institutions (ADIs) with less than $50 million in capital.

This will allow smaller ADIs to benefit from the reputational advantages of being called a ‘bank’. Over time, these changes are expected to improve competition by encouraging new entrants, the government said.

As well as committing to increasing competition in the fintech sector, the government has released draft legislation that will make it easier for start-ups and innovative small businesses to raise capital.

The draft legislation looks to extend crowd-sourced equity funding (CSEF) to proprietary companies. This will open up crowd-sourced equity funding for a wider range of businesses and provide additional sources of capital, the government said.

Proprietary companies using CSEF will be able to have an unlimited number of CSEF shareholders.

The government will also be introducing a “world-leading” legislative financial services regulatory sandbox, according to the budget.

This will enable more businesses to test a wider range of new financial products and services without a licence which will reduce regulatory hurdles that have traditionally suffocated new businesses trying to develop new financial solutions, and has caused Australian talent go offshore, the government said.

Robust consumer protections and disclosure requirements will be in place to protect customers, however.

Further, the government is removing the double taxation of digital currency to make it easier for new innovative digital currency businesses to operate in Australia.

From 1 July 2017, purchases of digital currency will no longer be subject to the GST.

This will allow digital currencies to be treated just like money for GST purposes. Currently, consumers who use digital currencies can effectively bear GST twice: once on the purchase of the digital currency and once again on its use in exchange for other goods and services subject to the GST.

The government has also commissioned Innovation and Science Australia to develop a 2030 Strategic Plan for Australia’s Innovation, Science and Research (ISR) System. The plan will outline what the nation’s ISR system should look like into the future and ensure that Australia is positioned as a world leader in innovation, the government said.

Fintech Australia chief executive Danielle Szetho warmly welcomed the measures, describing the budget as a “boost” for the emerging sector.

“We welcome these initiatives – they’re a huge step forward when it comes to growing a globally competitive Australian fintech industry, that will also deliver greater choice and improved financial outcomes for consumers,” Ms Szetho said.

“We’re also proud that many of these initiatives have come about through the strong and detailed advocacy work undertaken by FinTech Australia and its members.”

Productivity, Technology, and Demographics

From The IMF Blog

Hal Varian, chief economist at Google, says that if technology cannot boost productivity, then we are in real trouble.

In a podcast interview, Varian says thirty years from now, the global labor force will look very different, as working age populations in many countries, especially in advanced economies, start to shrink. While some workers today worry they will lose their jobs because of technology, economists are wondering if it will boost productivity enough to compensate for the shifting demographics—the so-called productivity paradox.

“I would say there are at least three forces at work,” says Varian. “One of these is the investment hangover from the recession—companies have been slow to reestablish their previous levels of investment. The second has been the diffusion of technology—the increasing gap between some of the more advanced companies and less advanced companies. And third, existing metrics are facing some strains in terms of adapting to the new economy.”

Varian believes demographics is important, particularly now that baby boomers, who made up most of the labor force from the 1970s through 1990s, are now retiring but will continue to be consumers.

“Today, the working labor force is growing at less than half of the rate of population growth, which is a concern in terms of how to make the amount produced equal to the amount that people want to consume,” he said.

Varian’s answer to the concern of older workers who are afraid robots will take over their jobs:

“There’s a saying in Silicon Valley that we overestimate what can happen in two years, and we underestimate what can happen in ten years—this has proven true time and again. What the 40- and 50-year-olds should be doing is continuing to learn. Lifetime learning is the norm now.”

 

The agile working style started in tech but it could work for banks

From The Conversation.

The purpose of the “agile” working style is to help businesses adapt to turbulent markets by adopting a fast and flexible approach to work. In one sense, it should come as no surprise that ANZ’s chief executive Shayne Elliot recently announced that ANZ will be shifting parts of its workforce to this style.

With the bank’s recent withdrawal from Asia and subsequent lower than expected revenues, this is part of ANZ refocus on its core business. In fact, each of Australia’s big four banks might be looking to become more efficient and responsive in the face of a tightly regulated market and slowly building retail banking competition from newer financial technology companies.

In another sense though, it’s surprising that one of Australia’s largest banks should signal such a profound change in work style. Finance is certainly not where agile got its start.

The origins of agile

The forerunners of agile stretch back as far as the Plan-Do-Study-Act method developed by Walter Shewart at Bell Labs in the 1930s and the Toyota Production System, based, in part, on the quality and systems thinking of Shewart’s student, William Edwards Deming.

However, agile as we understand it today is seen as emerging from software programming communities. It crystallised when 17 software developers gathered at the Snowbird ski resort in Utah in 2001 to share and refine their approaches to software development.

One of the participants had been reading a book on major companies coping with turbulent markets, called Agile Competitors and Virtual Organizations: Strategies for Enriching the Customer. Drawn to the agile’s connotations of speed and responsiveness, the group eventually adopted it as the moniker for their movement.

They published their views in The Manifesto for Agile Software Development, intending to help accelerate developers’ efforts to reliably produce software of the highest quality. Agile has since spread beyond the confines of IT to the other types of work and other organisations.

How to work in an agile style

As academics Rigby, Sutherland, and Takeuchi explain, agile now covers a broad range of methods, each varying according to their guiding principles and work rules. The three most well-known methods are scrum, lean, and kanban.

Scrum focuses on structuring teams to work across functions in a business, using creative and adaptive teamwork, daily stand-up meetings, and project reviews to quickly invent solutions and improve team performance.

Lean focuses on eliminating waste in systems and does not prescribe work rules to achieve this in the same way as scrum.

Kanban aims to shorten the time between the initiation and completion of work by visualising workflows, restricting the work being done at each stage in development, and measuring work cycle times to detect improvement.

ANZ seems to be most interested in the scrum method. ANZ’s Head of Product Katherine Bray stated:

There are vestiges of roles that we recognise, but with the underpinnings of hierarchy totally blown apart…[A scrum coach] is not your boss, that’s a coach, who is a peer. That product owner is not your boss, they’re a product owner who defines the how, and you galvanise around that.

Going back to the origins of agile and system thinking, it seems clear the agile approach is most likely to succeed where the organisation adopting it possesses structural modularity. Modularity proposes that organisations structure themselves in a way that allows teams to produce work that is layered, discrete, and testable.

This is what Bray is talking about – a radically new approach to roles and work styles at ANZ.

We might dismiss this whole reorganisation as marketing theatre, but intensifying competition and rapid change are all too real. This means many Australian businesses will have to come to terms with agile approaches if they are to remain responsive and competitive.

By taking up agile’s shift from top-down management to teams that organise themselves, and from a focus on compliance to a focus on innovation, ANZ is making its intent clear. It wants to achieve different results by doing things differently – surely a sane approach to change.

Yet this idea challenges the conventional structure and ethos of banks and similarly run businesses. These organisations are built to be secure and centralised in service of efficiency; modularity pushes them to be integrated and decentralised in service of innovation.

Modularity and agility are not easy to achieve. But they are fast becoming necessary if large companies, like ANZ, are to move with the times and adapt well to market turbulence.

Authors: Massimo Garbuio, Senior Lecturer, University of Sydney; Dreu Harrison, Research Assistant, University of Sydney

Robo-advice is not disruptive: Reinventure

From Investor Daily.

Automated advice is not a disruptive innovation and will only sustain the existing business models of the major banks, according to venture capital firm Reinventure.

Reinventure general partner Kara Frederick says the Westpac-backed venture capital fund will not be investing in robo-advice any time soon.

Ms Frederick, who is a Silicon Valley ‘native’, joined Reinventure founders Danny Gilligan and Simon Cant in March to help them broaden the fund’s reach into the US.

Speaking to InvestorDaily, she distinguished “sustaining” innovation products – such as robo-advice – from truly disruptive fintech technology. Reinventure is backing the latter, rather than the former, she said.

The venture capital fund has $100 million in total to invest, with approximately $45 million already committed to a portfolio of 15 companies.

When it comes to developing innovative technologies, banks typically have three approaches available to them, which are “build, buy or partner”, Ms Frederick said.

“If a bank’s going to go out of its way to do an incremental build or an entire build, that’s probably not where Reinventure’s going to play at all,” she said.

“Because we see that as sustaining innovation and that’s where we see most of the robo-advice products.”

Reinventure is more interested in the technologies that banks will either buy or partner with.

“The reason [banks want to buy or partner] is because it’s either too far in the future – it’s a potential real disruption but it’s not one or two years’ away,” Ms Frederick said.

“When you think of fintech globally, [Reinventure] sits in more of the ‘buy and partner’ side whereas the ‘build’ is more intrinsic to the banks themselves.”

Ms Frederick helped Reinventure invest in SME debt management start-up InDebted on Monday, and she has taken up a position on the start-up’s board.

Banks and Fintech – Where Do They Fit?

US Fed Governor Lael Brainard spoke on “Where Do Banks Fit in the Fintech Stack?” at the Northwestern Kellogg Public-Private Interface Conference on “New Developments in Consumer Finance: Research & Practice”

In particular she explored different approaches to how banks are exposing their data in a fintech context and the regulatory implications. Smaller banks may be at a disadvantage.

Different Approaches to the Fintech Stack

Because of the high stakes, fintech firms, banks, data aggregators, consumer groups, and regulators are all still figuring out how best to do the connecting. There are a few alternative approaches in operation today, with various advantages and drawbacks.

A number of large banks have developed or are in the process of developing interfaces to allow outside developers access to their platforms under controlled conditions. Similar to Apple opening the APIs of its phones and operating systems, these financial companies are working to provide APIs to outside developers, who can then build new products on the banks’ platforms. It is worth highlighting that platform APIs generally vary in their degree of openness, even in the smartphone world. If a developer wants to use a Google Maps API to embed a map in her application, she first must create a developer account with Google, agreeing to Google’s terms and conditions. This means she will have entered a contract with the owner of the API, and the terms and conditions may differ depending on how sensitive the particular API is. Google may require only a minimum amount of information for a developer that wants to use an API to display a map. Google may, however, require more information about a developer that wants to use a different API to monitor the history of a consumer’s physical locations over the previous week. And in some cases, the competitive interests of Google and a third-party app developer may diverge over time, such that the original terms of access are no longer acceptable.

The fact that it is possible and indeed relatively common for the API provider–the platform–to require specific controls and protections over the use of that API raises complicated issues when imported to the banking world. As banks have considered how to facilitate connectivity, the considerations include not only technical issues and the associated investment, but also the important legal questions associated with operating in a highly regulated sector. The banks’ terms of access may be determined in third-party service provider agreements that may offer different degrees of access. These may affect not only what types of protections and vetting are appropriate for different types of access over consumers’ funds and data held at a bank in order to enable the bank to fulfill its obligations for data security and other consumer protections, but also the competitive position of the bank relative to third-party developers.

There is a second broad type of approach in which many banks have entered into agreements with specialized companies that essentially act as middlemen, frequently described as “data aggregators.” These banks may lack the budgets and expertise to create their own open APIs or may not see that as a key element in their business strategies. Data aggregators collect consumer financial account data from banks, on the one hand, and then provide access to that data to fintech developers, on the other hand. Data aggregators organize the data they collect from banks and other data sources and then offer their own suite of open APIs to outside developers. By partnering with data aggregators, banks can open their systems to thousands of developers, without having to invest in creating and maintaining their own open APIs. This also allows fintech developers to build their products around the APIs of two or three data aggregators, rather than 15,000 different banks and other data sources. And, if agreements between data aggregators and banks are structured as data aggregators performing outsourced services to banks, the bank should be able to conduct the appropriate due diligence of its vendors, whose services to those banks may be subject to examination by safety and soundness regulators.

Some banks have opted for a more “closed” approach to fintech developers by entering into individual agreements with specific technology providers or data aggregators. These agreements often impose specific requirements rather than simply facilitating structured data feeds. These banks negotiate for greater control over their systems by limiting who is accessing their data–often to a specific third party’s suite of products. Likewise, many banks use these agreements to limit what types of data will be shared. For instance, banks may share information about the balances in consumers’ accounts but decline to share information about fees or other pricing. While recognizing the legitimate need for vetting of third parties for purposes of the banks fulfilling their responsibilities, including for data privacy and security, some consumer groups have suggested that the standards for vetting should be commonly agreed to and transparent to ensure that banks do not restrict access for competitive reasons and that consumers should be able to decide what data to make available to third-party fintech applications.

A third set of banks may be unable or unwilling to provide permissioned access, for reasons ranging from fears about increased competition to concerns about the cost and complexity of ensuring compliance with underlying laws and regulations. At the very least, banks may have reasonable concerns about being able to see, if not control, which third-party developers will have access to the banking data that is provided by the data aggregators. Accordingly, even banks that have previously provided structured data feeds to data aggregators may decide to limit or block access. In such cases, however, data aggregators can still move forward to collect consumer data for use by fintech developers without the permission or even potentially without the knowledge of the bank. Instead, data aggregators and fintech developers directly ask consumers to give them their online banking logins and passwords. Then, in a process commonly called “screen scraping,” data aggregators log onto banks’ online consumer websites, as if they were the actual consumers, and extract information. Some banks report that as much as 20 to 40 percent of online banking logins is attributable to data aggregators. They even assert that they have trouble distinguishing whether a computer system that is logging in multiple times a day is a consumer, a data aggregator, or a cyber attack.

For community banks with limited resources, the necessary investments in API technology and in negotiating and overseeing data-sharing agreements with data aggregators and third-party providers may be beyond their reach, especially as they usually rely on service providers for their core technology. Some fintech firms argue that screen scraping–which has drawn the most complaints about data security–may be the most effective tool for the customers of small community banks to access the financial apps they prefer–and thereby necessary to remain competitive until more effective broader industry solutions are developed.

Clearly, getting these connectivity questions right, including the need to manage the consumer protection risks, is critically important. It could make the difference between a world in which the fintech wave helps community banks become the platforms of the future, on the one hand, or, on the other hand, a world in which fintech instead further widens the gulf between community banks and the largest banks.