NAB trialling IBM blockchain technology

From Investor Daily.

National Australia Bank is one of a number of global banks that are trialling a cross-border payments solution powered by IBM Blockchain.

IBM has rolled out a new blockchain banking solution designed to reduce settlement times for cross-border payments.

NAB is the only Australian bank involved in the trial so far, along with institutions from Argentina, Indonesia, Thailand and the Philippines, among others.

According to a statement by IBM, the solution uses a blockchain distributed ledger to allow all parties to have access and insight into clearing and settlement of payments.

“It is designed to augment financial flows worldwide, for all payment types and values, and allows financial institutions to choose the settlement network of their choice for the exchange of central bank-issued digital assets,” said the statement.

The IBM solution, which has been created in collaboration with open source blockchain network and KlickEx Group, is already processing live transactions in 12 currency “corridors” across the Pacific islands and Australia, said IBM.

“For example, in the future, the new IBM network could make it possible for a farmer in Samoa to enter into a trade contract with a buyer in Indonesia.

“The blockchain would be used to record the terms of the contract, manage trade documentation, allow the farmer to put up collateral, obtain letters of credit, and finalise transaction terms with immediate payment, conducting global trade with transparency and relative ease.”

The solutions is run from IBM’s open source Blockchain Platform on Hyperledger Fabric.

Do computers make better bank managers than humans?

From The Conversation.

Algorithms are increasingly making decisions that affect ordinary people’s lives. One example of this is so-called “algorithmic lending”, with some companies claiming to have reduced the time it takes to approve a home loan to mere minutes.

But can computers become better judges of financial risk than human bank tellers? Some computer scientists and data analysts certainly think so.

How banking is changing

On the face of it, bank lending is rather simple.

People with excess money deposit it in a bank, expecting to earn interest. People who need cash borrow funds from the bank, promising to pay the amount borrowed plus interest. The bank makes money by charging a higher interest rate to the borrower than it pays the depositor.

Where it gets a bit trickier is in managing risk. If the borrower were to default on payments, not only does the bank not earn the interest income, it also loses the amount loaned (provided there wasn’t collateral attached, such as a house or car).

A borrower who is deemed less creditworthy is charged a higher interest rate, thereby compensating the bank for additional risk.

Consequently, the banks have a delicate balancing act – they always want more borrowers to increase their income, but they need to screen out those who aren’t creditworthy.

Traditionally this role was fulfilled by an experienced credit manager — a judge of human character — who could distinguish between responsible borrowers and those who would be unlikely to meet their repayment schedules.

Are humans any good at judging risk?

When you look at the research, it doesn’t seem that humans are that great at judging financial risk.

Two psychologists conducted an experimental study to assess the kind of information that loan officers rely upon. They found that in addition to “hard” financial data, loan officers rely on “soft” gut instincts. The latter was even regarded as a more valid indicator of creditworthiness than financial data.

Additional studies of loan officers in controlled experiments showed that the longer the bank’s association with the customer, the larger the requested loan, and the more exciting its associated industry, the more likely are loan officers to underrate loan risks.

Other researchers have found that the more applications that loan officers have to process, the greater the likelihood that bank officers will use non-compensatory (irrational) decision strategies. For example, just because a customer has a high income that doesn’t mean they don’t have a bad credit history.

Loan officers have also been found to reach decisions early in the lending process, tending to ignore information that is inconsistent with their early impressions. Lastly, loan officers often fail to properly weigh the credibility of financial information when evaluating commercial loans.

Enter algorithmic lending

Compared with human bank managers, a computer algorithm is like a devoted apprentice who painstakingly observes each person’s credit history over many years.

Banks already have troves of data on historical loan applications paired with outcomes – whether the loan was repaid or defaulted. Armed with this information, an algorithm can screen each new credit application to determine its creditworthiness.

There are various methods, based on the specific data in each applicant’s profile, from which the algorithm identifies the most relevant and unique attributes.

For example, if the application is filled in by hand and scanned into the computer, the algorithm may consider whether the application was written in block capitals or in cursive handwriting.

The algorithm may have detected a pattern that applicants who write in all-caps without punctuation are usually less educated with a lower earning potential, and thereby inherently more risky. Who knew that how you write your name and address could result in denial of a credit application?

On the other hand, a degree from Harvard University could be viewed favorably by algorithms.

On balance, computers come out ahead

A large part of human decision making is based on the first few seconds and how much they like the applicant. A well-dressed, well-groomed young individual has more chance than an unshaven, dishevelled bloke of obtaining a loan from a human credit checker. But an algorithm is unlikely to make the same kind of judgement.

Some critics contend that algorithmic lending will shut disadvantaged people out of the financial system, because of the use of pattern-matching and financial histories. They argue that machines are by definition neutral and thus usual banking rules will not apply. This is a misconception.

The computer program is constrained by the same regulations as the human underwriter. For example, the computer program cannot deny applications from a particular postal code, as those are usually segregated by income levels and racial ethnicity.

Moreover, such overt or covert discrimination can be prevented by requiring lending agencies (and algorithms) to provide reasons why a particular application was denied, as Australia has done.

In conclusion, computers make lending decisions based on objective data and avoid the biases exhibited by people, while complying with regulations that govern fair lending practices.

Author: Saurav Dutta, Head of School at the School of Accounting, Curtin University

Fintech Disruption Index Moves Higher

The latest edition of the Disruption Index has just been released, and it is 41.57, up from 38.29 last quarter. This is good news for Fintechs in that the SME community is adopting digital faster than ever.

The Financial Services Disruption Index, which has been jointly developed by Moula, the lender to the small business sector; and research and consulting firm Digital Finance Analytics (DFA).

Combing data from both organisations, we are able to track the waves of disruption, initially in the small business lending sector, and more widely across financial services later.

The index tracks a number of dimensions. From the DFA Small business surveys (52,000 each year), we measure SME service expectations for unsecured lending, their awareness of non-traditional funding options, their use of smart devices, their willingness to share electronic data in return for credit, and overall business confidence of those who are borrowing relative to those who are not.

Moula data includes SME conversion data, the type of data SME’s share, the average loan amount approved, application credit enquiries, and speed of application processing.

Here are some of the highlights:

Business Confidence

SME Business Confidence of those borrowing is on the up, reflecting stronger demand for credit, with the indicator jumping a healthy 15.8%, however, the amount of “red tape” which firms have to navigate is a considerable barrier to growth.

Knowledge of Non-bank Financial Providers

Awareness of new funding options continues to rise if slowly, creating a significant marketing opportunity for the new players, and a potentially larger slice of the pie.

Business Data

Greater willingness to share data and use of cloud-based services continue to rise. One-third of businesses have data held within the cloud, including accounting, customer management, invoicing, human resource, and tax management. We see variations across the segments in their use of these services. Of the businesses applying for funding, almost 90% now provide some form of electronic data via online loan application and are clearly comfortable in doing so (suggesting security concerns are less of a deterrent than the incentive of the speed of application and execution).

Average Loan Size

Average loan size continues to move upwards to register above $40k for the first time, indicating that better businesses are embracing alternative finance arrangements. More than likely, these businesses have traditional banking relationships, but either choose (or are forced to) look elsewhere for liquidity.

Mortgage fintech looks to blockchain for home loans

From The Adviser.

The CEO and founder of an online mortgage platform has revealed that the fintech is looking into how it can utilise blockchain to make the home loan contract process more efficient.

Speaking at the Informa Credit Law Conference, Mandeep Sodhi, the CEO of HashChing, revealed that the platform was looking into the distributed ledger technology for mortgages.

When asked by The Adviser what HashChing was using blockchain for, Mr Sodhi said: “We have been exploring blockchain in the home loan contract, smart contract space and securitisation as well.

“It’s more in the future road map but mostly around how quickly can we exchange a contract, through smart contracts. But also, if you decide to come on with a loan product at a later stage (of course, we’ll distribute it through brokers only), but then, how quickly can you settle that loan as well and securitisation? That’s where blockchain plays a really important role, if you need a securitised [loans] done quickly.”

He concluded: “Start-ups are tapping into AI technology, through Amazon Alexa, Google. It’s where banks are lagging, but start-ups are moving fast. That’s what banks need to think about.”

Bank couldn’t beat broker rate

Looking back at the journey of HashChing, Mr Sodhi stated that the idea first came about in 2014, after he found that a major bank, at which he worked, could not match or beat a broker-secured home loan rate.

Speaking at the Informa Credit Law Conference, Mr Sodhi said that he had gotten his mortgage through the bank at a discounted employee rate, but later found that one of his friends had gotten a lower rate for his mortgage at the same bank.

He said: “I was a loyal banker looking for my first home loan and I reached out and said: ‘Hey, can I get my staff discount?’ And my bank said that they could give me the special staff discount rate.

“I told my friend, Atul Narang (the co-founder of HashChing), about securing this great rate on my home loan and asked him: ‘Why don’t you become a banking man?’ And he said: ‘Well, actually, I’ve secured a better rate than you, also at your bank.’ And that left a bad taste in my mouth. So, I took his letter to the bank and asked how he got a better rate and asked them to match it, or at least beat it, because it’s really embarrassing. And they said: ‘We can’t do that.’ When I asked why, they said it was because he had used a mortgage broker.

“Now, I didn’t think that mattered… I worked for the bank. But they said: ‘We can’t match mortgage brokers’ rates.’”

It was after this “frustrating experience” that Mr Sodhi said he tried to find the same rate on comparison sites and then through a broker, but still couldn’t (he reportedly didn’t use Mr Atul’s broker due to geographical barriers).

Mr Sodhi continued: “There are thousands of people searching for good home loan rates every day on home loan comparison sites, who are clueless, just like I was. And that’s when we decided to start HashChing — where the journey starts with a negotiated rate that the broker secures from the lender.”

He went on to tell delegates that the majority of fintech start-ups come to market because of “frustrations with the banking system”.

“They’ve seen this opportunity, tried to change it in banking, but have been shut down — and that happened to me as well, so we decided to take it on ourselves.”

Mr Sodhi said that the HashChing platform, which launched in 2015, now has 679 brokers on the platform helping 23,959 borrowers apply for more than $12 billion of loans through more than 60 lenders.

How marketers use algorithms to (try to) read your mind

From The Conversation.

Have you ever you looked for a product online and then been recommended the exact thing you need to complement it? Or have you been thinking about a particular purchase, only to receive an email with that product on sale?

All of this may give you a slightly spooky feeling, but what you’re really experiencing is the result of complex algorithms used to predict, and in some cases, even influence your behaviour.

Companies now have access to an unprecedented amount of data on your present and past shopping and browsing preferences. This ranges from transactional data, to website traffic and even social media posts. Predictive algorithms use this data to make inferences about what is likely to happen in the future.

For example, after a few times visiting a coffee shop, the barista might notice that you always order a latte with one sugar. They could then use this “data” to predict that tomorrow you will order the same thing, and have it ready for you before you get there.

Predictive algorithms work the same way, just on a much bigger scale.

How are big data and predictive algorithms used?

My colleagues and I recently conducted a study using online browsing data to show there are five reasons consumers use retail websites, ranging from simply “touching base” to planning a specific purchase.

Using historical data, we were able to see that customers who browse a wide variety of different product categories are less likely to make a purchase than those that are focused on specific products. Meanwhile consumers were more likely to purchase if they reached the website through a search engine, compared to a link in an email.

With information like this websites can be personalised based on the most likely motivation of each visitor. The next time a consumer clicks through from a search engine they can be led straight to checkout, while those wanting to browse can be given time and inspiration.

Somewhat similar to this are the predictive algorithms used to make recommendations on websites like Amazon and Netflix. Analysts estimate that 35% of what people buy on Amazon, and 75% of what they watch on Netflix, is driven by these algorithms.

These algorithms also work by analysing both your past behaviour (e.g. what you have bought or watched), as well as the behaviour of others (e.g. what people who bought or watched the same thing also bought or watched). The key to the success of these algorithms is the scope of data available. By analysing the past behaviour of similar consumers, these algorithms are able to make recommendations that are more likely to be accurate, rather than relying on guess work.

For the curious, part of Amazon’s famous recommendation algorithm was recently released as an open source project for others to build upon.

But of course, there are innumerable other data points for algorithms to analyse than just behaviour. US retailer Walmart famously stocked up on strawberry pop-tarts in the lead up to a major storm. This was the result of simple analysis of past weather data and how that influenced demand.

It is also possible to predict how purchase behaviour is likely to evolve in the future. Algorithms can predict whether a consumer is likely to change purchase channel (e.g. from in-store to online), or even if certain customers are likely to stop shopping.

Prior studies that have applied these algorithms have found companies can influence a consumer’s choice of purchase channel and even purchase value by changing the way they communicate with them, and can use promotional campaigns to decrease customer churn.

Should I be concerned?

While these predictive algorithms undoubtedly provide benefits, there are also serious issues about privacy. In the past there have been claims that companies have predicted consumers are pregnant before they know themselves.

These privacy concerns are critical and require careful consideration from both businesses and government.

However, it is important to remember that companies are not truly interested in any one consumer. While many of these algorithms are designed to mimic “personal” recommendations, in fact they are based on behaviour across the whole customer base. Additionally, the recommendations or promotions that are given to each individual are automated from the database, so the chances of any staff actually knowing about an individual customer is extremely low.

Consumers can also benefit from companies using these predictive algorithms. For example, if you search for a product online, chances are you will be targeted with ads for that product over the next few days. Depending on the company, these ads may include discount codes to encourage you to purchase. By waiting a few days after browsing, you may be able to get a discount for a product you were intending to buy anyway.

Alternatively, look for companies who adjust their price based on forecasted demand. By learning when the low-demand periods are, you can pick yourself up a bargain at lower prices. So while companies are turning to predictive analytics to try to read consumers’ minds, some smart shopping behaviours can make it a two-way street.

Author: Jason Pallant, Lecturer of Marketing, Swinburne University of Technology

CBA To Launch New Low Rate Credit Card

Commonwealth Bank today has announced three new initiatives including a new credit card with an interest rate below 10 per cent.

The three initiatives are:

  1. A new credit card with a 9.90 per cent purchase interest rate
  2. All customers with a credit card can receive real-time alerts for credit card repayments and high cost transactions, and all transaction account customers can receive overdrawn account alerts
  3. All credit card customers will have access to an instalment feature designed to help them pay down existing balances or large purchases, in easy fixed instalments

Clive van Horen, Executive General Manager at Commonwealth Bank, said: “We’ve heard feedback from customers and consumer groups and understand there’s a need to offer a greater range of affordable and easy to manage products.”

Designed to give customers more visibility and control over their personal finances, the new credit card, real-time alerts, and instalment feature will launch in phases.

“We know there’s strong demand for a simple credit card option and we also recognise we need to help our customers avoid credit card late payment and overdrawn account fees. The real-time alerts in our CommBank App give customers even more tools to help manage their spending and avoid fees and charges,” said Mr van Horen.

New credit card

Available from early 2018, the new CommBank credit card will offer a highly competitive interest rate of 9.90 per cent, and a low account keeping fee of just $5 per month. The new credit card is suited to customers who want a low, competitive interest rate, low account keeping fee with a low maximum limit, and no access to cash advances.

Real-time alerts for credit card repayments, overdrawn accounts and high cost transactions

From November, customers will be able to take advantage of three new alerts:

  • Customers with the CommBank App will receive real-time alerts, reminding them their credit card payment is due. If their payment becomes overdue, customers will receive an additional alert advising them if they make their payment by midnight the following day they will not incur a late payment fee.
  • Customers whose transaction accounts have been overdrawn due to a scheduled payment or direct debit will receive a real-time alert and they too will not incur an overdrawn access fee if settled by midnight.
  • Customers that make a high cost credit card transaction (such as an ATM cash advance or online gambling) will be alerted in real time that these transactions incur cash advance fees and interest.

Instalment feature

From mid-2018, credit card customers can choose to pay down large purchases or a portion of their balance through fixed monthly instalments at a discounted rate, over a fixed period, allowing them greater control of their credit card repayments.

Empowering customers to manage their spending and avoid fees and charges

These latest product initiatives join the suite of online tools and features launched over the last three years to give customers more visibility over their credit card spending, including:

  • Transaction Notifications: Eligible customers automatically receive an instant notification every time they pay with their credit card.
  • Lock, Block, Limit: Gives customers real-time control over what types of transactions their card could be used for – such as ATM withdrawals and overseas spending. More than 1 million cards have enrolled for this feature since 2014.
  • Spending cap and credit limit decreases: Customers can set a spending cap to manage their spending or reduce their credit limit online. Approximately 13,000 credit limit decreases are performed each month since launch.
  • Spend Tracker: Each credit card transaction is categorised automatically in the CommBank App so customers can see where they are spending and compare expenditure across months.
  • Earlier this year CommBank also launched Click to Close: a feature which allows customers to close their credit cards online through NetBank and the CommBank App.

“We continue to innovate for our customers’ benefit and we hope these latest steps will be welcomed,” added Mr van Horen.

SocietyOne sets new record with triple milestones in 2017

From Australian Fintech.

SocietyOne, Australia’s consumer marketplace lender, has set new records for lending with growth in 2017 already surpassing the level of volumes for the whole of 2016.

Total lending since the company started operating five years ago has now topped $350 million as the current loan book also reached $200 million for the first time in SocietyOne’s history.

These were new records for a consumer finance marketplace lender in Australia with SocietyOne having originated more than twice the loans than that of the company’s nearest competitor. SocietyOne has now enjoyed seven successive quarters of strong growth as it scales up.

New lending to borrowers in 2017 has so far totalled $141 million, topping the $139 million which was advanced over the course of 2016.

Jason Yetton, CEO and Managing Director of SocietyOne, said: “We have continued to build on the strong momentum achieved in 2016 and have seen sustained year-on-year growth with comparable lending volumes now above the levels achieved for the whole of last year.

“Our growth in 2017 underlines the demand from consumers for a real alternative to the major banks. Consumers are looking for a better deal on their finances and our risk-based pricing is attractive for customers that have demonstrated that they have a good credit history.

“Customers have responded positively to a number of improvements we have delivered over the past year. These have included a strong focus on better service outcomes and speed of loan approvals, increasing the maximum loan amount on personal loans to $50,000 and continued success with our advertising and marketing activities, including the “Make It Happen” campaign that launched in June.

“Our customers are also loving the differentiated service experience that we offer. We have had more than 600 customer reviews on with an average rating of 4.7 out of 5. We also track Net Promoter Scores on our lending and this now stands at +63.”

Of the $350 million of lending to date, $270 million has been advanced to consumer borrowers as personal loans and $80 million to farmers via their agents through SocietyOne’s unique secured livestock lending product.

Launched as a pilot in March 2014, SocietyOne AgriLending is now scaling up and during the third quarter was recognised for its support of Australian cattle and sheep farmers at the 2017 Australian Business Banking Awards by being named as a finalist in the industry specialisation category. The last quarter saw improvements to both the product and the technology platform.

The first three-quarters of 2017 have also seen a record amount of funding made available by investor funders with new mandates secured from existing and new institutions and high net worth individuals. The total number of funders since inception has risen to 320 and committed available funding as at 30 September 2017 stood at $61 million.

SocietyOne enjoys strong support from the customer-owned banking sector with the number of mutual banks and credit unions as funders now numbering 20. Mutual institutions have to date provided $100 million of funding out of the $350 million advanced to borrowers.

As for the outlook, Mr Yetton said the company was looking forward to another strong quarter ahead and welcomed moves by the Federal Government to encourage more competition in the banking sector by legislating for comprehensive credit reporting and open banking.

“With a more dynamic approach to both comprehensive credit reporting and open banking on the horizon, Australians are becoming increasingly aware of the better choices now available,” he said.

“As the undisputed leader in marketplace lending for personal loans, our customers are clearly telling us there are more attractive ways to sort out their finances, whether it is consolidating credit card debt, renovating their homes, buying a car, going on holiday or paying for a wedding.

“I’m also pleased at the way we are getting behind Australian livestock farmers as the growth in SocietyOne AgriLending has shown. The team is standing ready to help them even more so as rural and regional Australia waits for the rains that will kick start the Spring growth and rearing season.”

Myer launches Android and Apple Pay

From Fintech Business.

Myer customers will be able to use Android Pay and Apple Pay to make purchases in store via the launch of a new credit card, the Myer Credit Card, issued by Macquarie Bank.

Using Visa payment technology, Myer shoppers will be able to pay for purchases from their digital wallet on their smartphone through the Myer Credit Card App.

Commenting on the launch, Myer chief executive and managing director Richard Umbers said he was excited to be bringing Android and Apple Pay via the Myer Credit Card.

“The card will provide our customers with an easier way to pay and reward them for their loyalty,” he said.

“We are delighted with our partnership with Macquarie and Visa, which will further accelerate the growth of Myer’s digital capability.”

Macquarie head of banking and financial services group Greg Ward said the company is delighted to have been chosen as the card issuer.

“For many years we have offered credit card products directly and through white label arrangements, and this is the latest step in supporting innovative digital banking solutions for Australians,” he said.

Customers will also be able to accumulate Myer one shopping credits on the credit card, a spokesperson for Myer said.

Visa group country manager for Australia, New Zealand and the South Pacific Stephen Karpin said it was an “exciting time” for Australian retail.

“With digital technology driving new and imaginative commerce experiences … how people pay is at the heart of these experiences,” he said.

“It’s for this reason we’re delighted to be working with Myer and Macquarie Bank to bring the future of commerce to Australians today.”

Banks need a ‘better cost structure’: Narev

From Investor Daily.

Australia’s major banks must use data analytics, artificial intelligence and robotics to increase productivity and reduce costs, says outgoing CBA chief executive Ian Narev.

Speaking at a Morningstar conference in Sydney on Friday, outgoing CBA chief executive Ian Narev said the major banks must “adapt or die” when it comes to new technology.

“Over five to 10 years in [the banking] industry, if you do not successfully adapt, you will not succeed,” Mr Narev said.

“I say that without any sense of hyperbole at all. And [CBA] does not feel at all complacent about where we are, because you have got to keep going, but we feel pretty good about our relative position today.”

First, banks need to realise that their customers want to do business online – and will compare their banking experience with Facebook, Apple and Amazon, Mr Narev said.

“Number two is that the opportunities to apply artificial intelligence, data analytics, robotics to fundamental productivity is critical because we need a better cost structure,” Mr Narev said.

“While we are evolving to a better cost structure, we also need to be the responsible employer of 50,000 people and help our own workforce make the transition, which we are very committed to doing.

“So for us, this has been a topic of real focus for the last few years. It will remain a topic into the future. We are committed to adapt.”

Mr Narev also took the opportunity to reiterate his apologies to CBA’s shareholders and customers for “not reaching the standards we should have” regarding AUSTRAC’s accusations of CBA’s failings relating to anti-money laundering compliance.

“We let down our stakeholders and, regardless of the ins and outs of the legal claim, I am sorry for that as the chief executive. I take accountability for it and can assure you that we are taking it extremely seriously,” Mr Narev said.

Mr Narev, who is due to leave CBA by 1 July 2018, also joked about the identity of his successor.

“We have got uncertainty with leadership succession, although I can give you a guarantee that the next chief executive of the Commonwealth Bank will be better than the current one,” he said.


NAB’s now using Google Assistant to answer customer questions

From Business Insider.

The next time you have a question for the NAB, the chances are Google might giving you the answer as part of a voice-based automation program.

The “Talk to NAB” pilot is an local first for banking, enlisting Google Assistant on smartphones and the recently launched Google Home to answer general banking questions, ranging from replacing lost cars or resetting passwords.

NAB’s executive general manager of digital and innovation, Jonathan Davey, said the vast majority of customer contacts are now through digital platforms and the bank is experimenting with virtual assistants on a range of fronts, including a virtual banker chatbot for business customers, and a Facebook chatbot pilot.

“We know they want more self-service capability and they want to be able to solve basic questions in a channel that suits them and when it’s convenient for them,” Davey said.

“This is very much a first step for us in the voice-based smart assistance space; we will continue to develop our capability with the Google Assistant over time so it can answer more questions and perform more tasks for NAB customers”.

The Talk to NAB program is now live and available to NAB customers who have Google home or a smartphone with assistant.