Tag: Robo Advice

Singapore banks will benefit from regulatory push to strengthen artificial intelligence capabilities

From Moody’s

On Monday, the Monetary Authority of Singapore (MAS) announced that it is collaborating with the Economic Development Board (EDB), Infocomm Media Development Authority (IMDA) and Institute of Banking and Finance (IBF) to accelerate the adoption of artificial intelligence (AI) in the financial sector. The four agencies will jointly facilitate research and development of new AI technologies and adoption of AI-enabled products, services and processes. The effort will encompass three key initiatives: developing AI products, matching users and solution providers and strengthening AI capabilities.

The increased use of AI and data analytics by financial institutions, including Singapore’s three-largest banks, DBS Bank Ltd., Oversea-Chinese Banking Corp. Ltd. and United Overseas Bank Limited (UOB, will help them achieve greater operational cost efficiencies and tap new revenue opportunities, and is positive for their profitability. In addition, the banks will also benefit from a greater number of financial technology (fintech) companies with AI capabilities with which they can work to strengthen AI capabilities in their digital transformation.

In the collaborative effort, the EDB will augment MAS’ Artificial Intelligence and Data Analytics programme by providing support for AI solution providers locally and globally to conduct both upstream research and product development activities and create new AI products and services for Singapore’s financial sector. The MAS will work with EDB and IMDA to facilitate link-ups between companies in the financial and technology sectors, and pair local companies seeking AI solutions with credible AI solutions providers. The MAS will work closely with IBF and IMDA to equip financial industry professionals with the necessary skill set to transition into new jobs arising from the use of AI in financial services.

As part of their digital transformation, the three Singapore banks already have adopted the use of AI and analytics across various parts of their organizations and businesses. According to the banks’ managements, leveraging AI technology, for instance for machine learning and data analytics, has allowed them to automate repetitive and time-consuming manual tasks and processes, strengthen their risk management capabilities in handling complex surveillance activities and improve the productivity of their sales force and marketing efforts.

In November 2017, UOB reported that it adopted robotic process automation to handle repetitive data entry and computation tasks for its trade finance operations and retail unsecured loan processing function, which were able to substantially cut down processing time compared with the time taken to complete the tasks manually. Also in November 2017, OCBC unveiled its plans to work with fintech company ThetaRay to implement an algorithm-based solution to detect suspicious transactions in its anti-money laundering monitoring. According to the bank, the accuracy of identifying suspicious transactions increased by more than four times using the new technology.

OCBC also set up an AI-powered chatbot application in April 2017 that is able to address customer questions and compute debt-servicing requirements. The application managed to convert customer enquiries into new loan approvals totaling more than SGD100 million in 2017.
DBS reported that its sales productivity improved after relationship managers were provided with customer analytics on a mobile platform, raising the income per head by 57% over three years.

We expect Singapore’s banks to remain committed to their digital growth strategies to keep pace with customer expectations for more digital services and solutions, and remain competitive given the increasing number of fintech companies in the ecosystem. At the same time, we expect that banks will actively engage fintech companies in collaborative ventures to enhance their digital capabilities.

Member Personalisation the ‘New Paradigm’

From InvestorDaily.

Super funds must embrace digital, personalised advice if they want to retain their high-balance members, says industry veteran and SuperEd founder Jeremy Duffield.

Jeremy Duffield had a 30 year career at Vanguard between 1980 and 2010, and established the US indexing giant’s Australian presence in 1996.

Mr Duffield left Vanguard in late 2010 to co-found digital advice, education and member engagement fintech start-up SuperEd with former Westpac executive Hugh Morrow.

SuperEd received a $5 million funding boost in January 2018 from investors including former Macquarie director Mark Johnson and Shadforth founder Kevin Bailey.

The company offers digital member engagement services to super funds, including retirement income forecasts, member relationship management, education, and intra-fund advice.

“Personalisation is going to be a huge trend, because it’s what people expect in everyday life now,” Mr Duffield told InvestorDaily.

He differentiated SuperEd from other ‘robo-advice’ businesses that are mostly calculator-based and “leave it up to the consumer” to interpret the results of the calculator.

“That’s always been unrealistic – the numbers don’t speak for themselves, members need more than that and they need the story. We’re trying to work with super funds to tell the story,” Mr Duffield said.

SuperEd is the engine behind former Challenger executive Paul Rogan’s start-up Retirement Essentials, which helps retiring Australians apply for the government age pension.

SuperEd’s other clients include a corporate super advice group, group insurer AIA and “one of the large Victorian super funds”, said Mr Duffield.

Commenting on the industry’s transition to digital advice, Mr Duffield it is “disappointingly slow” – but it is happening.

“They’re hiring digital chiefs, they’re building up their web capabilities, they’re investing in CRM – there are signs.

“But it does feel like starting Vanguard Australia all over again – I’m out there in front trying to get people to change the way they do things,” he said.

Having helped drive down the cost of investment during his time at Vanguard, Mr Duffield is looking to do the same for the advice process.

“I think there’s more value to be added through advice than there is product,” he said.

Mr Duffield said he is “fully confident” that the trend towards digital advice will develop over time.

“We might be early, but we’re definitely in the right place. These changes that we’re betting on just have to happen,” he said.

NAB launches super virtual assistant

From Financial Standards.

NAB has launched a digital assistant that helps MLC members engage with their superannuation.

Available on Google Home devices, Talk to MLC answers 15 common questions members ask: such as how to open an MLC account, find lost super and change investment options.

MLC customer experience specialist Peter Forster said the super fund expects most members to access superannuation in a way that’s convenient and personalised without the need for passwords.

He said millennials and older Australians will likely be the first to embrace Talk to MLC.

“The technology took us six weeks to develop and deploy, and we’re in the process of developing other technology at a similar speed that will help to reduce asymmetry of information and further benefit our customers,” Forster said.

He added in the near future MLC will be able to provide personalised tips to help members boost their super; project where their super balance will be at retirement time; and advise how best to invest their money in super.

NAB executive general manager of digital and innovation Jonathan Davey said the proliferation of voice-activated, hands-free devices such as Siri and Google Home and Amazon’s Alexa in the Australian market is reshaping consumer behaviour and expectations.

“We live in a world that wants instant gratification. We want quick answers and problems that are solved immediately – we don’t want to be left waiting. Our lives are busier than ever before,” Davey said.

Early this year, CBA launched Ceba, a chatbot that recognises about 60,000 consumer banking questions.

Ceba’s point of difference, according to CBA executive general manager digital Pete Steel, is that it can actually carry out tasks for customers, rather than providing instructions on how they can be done.

ANZ is also deploying chatbots with the help of Progress’ NativeChat, to enable customers to converse and transact with chatbots naturally. NASDAQ-listed Progress helps develop industry-specific and self-learning chatbots for organisations.

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.

 

The Oldest Trick In The Book

The focus on power prices and the behaviour of the power companies where households end up on higher rates, draws attention to the oldest trick in the book – offer attractive rates for new business, but rely on consumer apathy/confusion or create hurdles to continue to get the best prices. Companies seeking to maximise their returns from hapless consumers.

The same is true in financial services, where both on the mortgage and deposit side of the ledger, it is easy for households to drift to rates which are not the best available.  The banks rely on this to protect their margins and profits, just like the power companies.

Now of course the power companies, under duress, are going to write to some consumers to help them find better deals. So why not the banks too?

In the past year lenders repriced their mortgage books aggressively, to increase margins, and imposed out of cycle rate hikes on all borrowers.  The trajectory of those varied between investor and owner occupied loans.

This chart, using data from the RBA tracks the rates on offer and we overlay the cash rate on the second axis.   They have built quite a war chest!

But in fact, the best new attractor rates are more than 100 basis points lower for some low risk customers, but only for new ones. It is harder for existing customers to get better rates, (but it is still worth being proactive and asking). There are costs involved in switching, and work to be done to find the deals out there.

Philip Lowe commented recently on how competitive dynamics drove lenders to take more risk:

One might ask why lenders themselves did not do more to constrain their activities in these areas, given the earlier trends were adding to risk in the overall system. When everything is going well, it appears that any single institution has difficulty pulling back. Each worries about their competitive position and about the market reaction. Individual institutions are also more likely to focus on their own risks, rather than the risks to the system as a whole.

We suspect the same argument would be mounted internally – replace risk, with profit – against the idea of helping customers to optimise the returns from their deposits and reduce their mortgage rates. But we think this is precisely where differentiation can and should emerge.

If a bank was really interested in customer outcomes, they would be proactive in suggesting products with better rates, rather than relying on inertia. Actually this could become a significant point of differentiation, and would be a touchstone for brand improvement and cultural change.

Imagine using the information systems inside the bank to analyse existing product footprints and proactively suggest better alternatives. No better way to build customer trust a loyalty, something which is sadly missing in the current profit at all cost drive approach. The truth is that long term shareholder value is actually aligned to building true customer loyalty. Yet this is largely ignored at the moment.

What about a proactive financial health check, where the bank and customer could jointly discuss options and alternatives?  This is even something a robo-banker might do.

Time for someone to step out from the pack.

NAB Launches Virtual Banker For Business Customers

NAB says it is the first bank in Australia to launch a digital virtual banker specifically for business customers, enabling them to receive instant answers and assistance with common banking questions and tasks.

NAB’s virtual banker is in pilot and available 24/7 on nab.com.au, providing help with more than 200 common questions related to the servicing of business banking accounts.

NAB Chief Operating Officer Antony Cahill said the development of the virtual banker continued NAB’s commitment to providing leading solutions that make life easier for customers.

“Our research shows that two thirds of Australian SMEs cite dealing with administrative tasks as taking a lot of effort, and our customers desperately want to spend more time on their business and less time on dealing with admin tasks.

“’We’re working hard to make banking an easy and supportive experience for our customers and technology like this helps save business customers critical time. When they have a question about their banking, our virtual banker is there to help solve it 24 hours a day, seven days a week; it’s a simple and seamless on-the-go experience.

“We will continue to develop the virtual banker over coming months, enabling an even broader and more diverse range of instant answers and guidance for business customers.”

The virtual assistant’s artificial intelligence is derived from thousands of real-life customer enquiries. There are more than 13,000 variants of the 200 questions the virtual banker can answer; if the question can’t be answered, the customer will then be directed to a human banker.

Customers were involved in the testing and development phase, with more than 75 per cent saying a virtual banking was a highly desirable offering that would help them with their banking needs.

Part of NAB’s delivery of new customer self-assistance also includes walk-through tutorial videos for NAB Connect users. The short step-by-step videos help customers understand how they can use and take advantage of the platform’s wide capabilities, with tutorials that help with common tasks like ‘adding users’ or ‘setting up reoccurring payments’.

The initiatives are just two examples of the many that have been developed by NAB’s Customer Journey teams, who are reimagining specific customer experiences.

“We currently have a number of different streams of work underway with almost 1000 employees across various areas of the bank – from bankers, to product specialists, marketing experts and technologists – working together on these projects and delivering at pace,” Mr Cahill said.

Hear from NAB’s EGM Business Transformation Anne Bennett talking NAB’s new Virtual Banker

Computer says no: robo-advice is growing but we still don’t trust it

From The Conversation.

People are open to receiving financial advice from robots, our studies show, but there might be a way to go to in convincing people to trust them over a human.

We surveyed 138 people about their attitudes to, and preferences for, superannuation advice from a human or a computer. Unsurprisingly, most stated they would prefer to deal with a human across a broad range of financial decisions.

Some did prefer the computer – these tended to be younger people, and those on higher incomes. In a follow-up study we tested whether this would change after people actually used the technology.

We did this by exposing 101 people to an online calculator, in which they learned how increasing their superannuation contributions would change their income in retirement. We compared these to another 101 people in a control group who simply read some general information about retirement income.

A little over half of our sample indicated that they would trust robo-advice. Those who got advice from the online calculator showed a small, but statistically significant, increase in trust towards robo-advice. However, most still stated they preferred human advice, and were slightly less willing to pay for automated advice after trying out the calculator.

The openness of younger people is encouraging, as they tend to be the most disengaged from their superannuation, but also have the most to gain from getting it right (as the benefits will build up for longer).

How the robots could help

Automated financial advice systems (so-called “robo-advisors”) have great potential to extend the reach of professional financial advice.

Digital technology has quietly revolutionised the world of banking and financial services. Automatic Teller Machines (ATMs) were an early example of a computer replacing a human worker. Interestingly banks responded to this increasing productivity by employing more people.

Financial advice is the latest frontier for automation, with a number of “robo-advisors” beginning to interact with customers. Even though we face a growing number of financial decisions, most Australians currently don’t get formal financial advice. Cost is a significant barrier to this.

Like many digital products, robo-advisors are costly to design and build, but once up and running they can serve large numbers of people. These bots could extend low-cost and dependable advice to those who currently miss out.

Robo-advice is currently a small part of the financial services market, but it is forecast to grow rapidly. In the US, firms such as Betterment and Wealthfront have begun to disrupt the wealth management industry. In Australia robo-advice products are being developed both by startups and industry incumbents.

But financial advice is about more than numbers. While technology can easily handle the maths, people may also need the human touch. They may want emotional support and motivation, rather than just the cold hard facts, to get them to confidently engage with these difficult and important decisions. Trust requires good design.

Digital technology might also prove useful in getting people more engaged with their financial decisions. Most Australians pay remarkably little attention to their superannuation, even though it makes up a significant portion of our overall wealth (second only to the family home).

Robo-advice could help people learn, and try out different scenarios, without the worry of appearing ignorant to a person. Unfortunately, our experiment found little evidence for this – overall levels of motivation, and perceptions of autonomy and competence were unchanged.

Distinguishing between people who were initially more engaged or less engaged with their superannuation, our study showed that the online calculator had a greater impact on those who were initially less engaged. This suggests robo-advice might prove most useful to those who need it the most, making them feel more competent and in control.

So for those of us who don’t pay enough attention to our financial decision-making, the robots are here to help.

 

Authors: Andrew Reeson, Behavioural Economist, CSIRO; Andreas Duenser, Research Scientist

 

BT Panorama inks deal with robo-adviser

From Investor Daily.

BT has made a clear statement of intent on digital advice by signing a platform connectivity deal with Ignition Wealth.

In a deal that will be announced this morning, BT has agreed to connect advisers and accountants using the Ignition Wealth platform to BT Panorama.

A spokesperson for BT confirmed to InvestorDaily that Ignition Wealth is the first digital advice provider to have connectivity with Panorama.

The agreement between the two companies is squarely aimed at accountants who are no longer permitted to provide advice to SMSFs after the expiry of the ‘accountants’ exemption’ on 1 July 2016.

Speaking to InvestorDaily about the deal, Ignition Wealth chief executive Mark Fordree said that while many clients will continue to be offered portfolios of ETFs, others will now be recommended a BT Panorama product if it is in their best interests.

The experience will be “seamless”, he said, with the Ignition Wealth engine granted permission to create BT Panorama accounts for clients.

“Our hybrid solution will allow an individual to have a complete self-service into numerous investment options including BT Panorama but not limited to it,” Mr Fordree said.

“The journey starts with Ignition Wealth, and a proportion of the clients that come through our platform may end up in BT Panorama where it’s appropriate.”

The agreement with BT was a long time in the making and it was a matter of “jumping bank-grade hurdles” at every step of the process, Mr Fordree said.

While the initial agreement with BT is limited to connectivity with Panorama, the goal of Ignition Wealth is to become completely integrated into Panorama and the BT Wealth platform.

“All the major players in the wealth management business may over the next few years have a digital platform,” Mr Fordree said.

“Whether they build it themselves or partner is the key question. We’re offering a solution to those who either don’t have the appetite or capability to build it.

“If they haven’t started already, I suspect that they’re already leaving it too late.”

In a separate statement, Ignition Wealth said the deal with BT was “the largest fintech deal in Australia to date”.

“This marks the first of the ‘big four’ to choose an independent technology provider to power their digital financial advice,” it said.

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.”

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.