If money is the quintessential social symbol, and the mobile phone is the quintessential social platform, what innovations will the mobile age bring to financial services? We look beyond the mobile-wallet zeitgeist to make predictions for the short-, mid-, and long term.
One-click everything
Short-term prediction: mobile payment will be increasingly prevalent, not as a stand-alone service but as an integral part of mobile applications with a broader purpose.
Our expectations about services in the real world are increasingly formed by our online experiences. These have set the benchmark for being able to effortlessly and fluidly search for, compare, purchase, and consume products and services. Mobile phones promise to bring this same fluidity to the world formerly known as “offline,” in the physical realm.
A good example is the new generation of taxi apps such as “GetTaxi,” “Hailo,” and “Uber,” which are revolutionizing the cab-riding experience with new models of sharing rides or information about taxi availability.
Such new services have far-reaching consequences for the underlying, preceding business models. These applications are designed around the customer journey, providing an end-to-end solution for ordering a cab, tracking its arrival, and paying for the ride. GetTaxi, for example, cuts traditional dispatch services out of the loop, placing all taxi drivers on a level playing field in their ability to win rides – and freeing up a niche in the value chain for GetTaxi to extract revenue. Taxi drivers who had previously refused credit card payment have virally adopted the GetTaxi service, which uses a mobile payment solution based on credit cards and connects directly into the metering system. From a mobile payment perspective, the key lesson here is that new payment systems will often be rejected on their own merits, but accepted if they are an integral part in a larger value proposition.
In a similar vein, from a payment perspective the much-publicized Square card-reader and mobile wallet solutions, act purely as aggregators to underlying, pre-existing credit card and interchange infrastructure. If a mere vehicle for payment were all they offered, they wouldn’t be attractive to customers or vendors. The real value of Square is that it radically simplifies the user experience of payment, while enriching the user experience either side of payment. It does so by bundling it into a rich overall customer journey that includes vendor discovery and loyalty programs. This has driven Square’s market growth and allowed them to justify extracting much more revenue than if they were purely processing payments.
The rapid growth of Square has spurred PayPal, previously a pure online player, to launch PayPal Here, a nearly identical service. These services, which bring together location data, mobile applications, credit cards, and loyalty programs, can be considered as bridges between online and offline purchasing behavior. They may ultimately dissolve this distinction.
Parallel currencies
Mid-term prediction: Mobile e-currencies will thrive in the “post-cash economy,” allowing telcos and retailers to compete with banks for many of their core services – and often without submitting to the same regulatory controls.
Since the launch of the Internet many e-currency solutions have been explored in theory and practice, but most have remained limited in scope. But recent examples of mobile money from East Africa seems to show that mobile phone based e-currencies offer advantages that could be applied globally.
The M-Pesa branchless banking solution in Kenya and Tanzania is the most striking and well-known example. The service enables use of the mobile phone to deposit and withdraw money, transfer money to other users and non-users, pay bills, purchase airtime, and transfer money to and from a conventional bank account – all without being categorized as a bank. M-Pesa has given almost 30 million users in Kenya and Tanzania access to banking services for the first time, with a level of convenience and simplicity that leapfrogs the standards of the “developed” world. Among the several remarkable aspects to this service is the fact that it was born out of a user-generated innovation – the use of airtime credits as a cash-alternative for money transfers – improved upon through the creation of the M-Pesa service. The M-Pesa is also, to all intents and purposes, a parallel, privately controlled e-currency, backed by the Kenyan shilling but separate from it. In just 5 years of use, this has grown to be the currency in which 25% of Kenyan financial transactions occur. In other words, M-Pesa has expanded the national money supply by 25%, outside the control of the central and commercial banking system. In November 2012, an additional blurring of boundaries occurred with the launch of M-Shwari, a mobile-only banking service linked to M-Pesa that provides an interest-earning savings account and a micro-financing service.
In the light of this runaway success, we’re anticipating that the launch of a parallel currency by Amazon, announced for May 2013, may be much more than a mere micro-payment solution for in-app purchases on the Kindle Fire. (That would scarcely be justifiable, given that Kindle users already have convenience of one-click payment). It may rather be the first step towards a parallel Amazon economy, with a supranational and hyper-liquid e-currency. That would have far-reaching consequences for the international financial system – as well as providing the perfect vehicle for Amazon to enter the field of mobile payments.
The crisis of risk
Long-term prediction: real-time Big Data will challenge the insurance industry as we know it, radically changing our way of understanding risk.
The basic idea of insurance is to mutualize risk: a very large group of people put their money in the pot in case one of them needs it later. It’s very simple when we believe that our likelihood of needing a pay-out (essentially, our risk profile) is approximately equal: anyone can contribute to the pot on the same terms. However, risk profiles are not equal, and the trend of the insurance industry has been to create more and more sophisticated models to define risk categories. This tends towards the asymptote of the “risk category of one,” where everything known about me is combined to calculate a unique risk profile for me as an individual. This profile in turn defines how much my insurance costs. And the more accurate the profiling, the greater the cost variance, eroding the basic principle of risk-sharing to the point where those most likely to require insurance may no longer be able to afford it.
Already today, the limitations on the accuracy of this risk-profiling are not in the data science, but in our social, legal and commercial conventions. In the US, for example, car insurance premiums are calculated including such factors as age, gender, and marital status, but factors such as race, religion, and income are excluded by law. But big data will enable insurers to work around these exclusions and uncover additional statistically relevant factors, from my personal and family history, the neighborhood I live in, my social circle, school, workplace, profession, and even genetic code, which may be used to calculate an increasingly accurate, unique risk profile. And before long, the differential between risk profiles might no longer be measured in percentage points, but in orders of magnitude.
Now imagine taking this static data and intersecting it with the real-time data provided by mobile devices. What happens to my car insurance risk profile if my GPS shows I drive erratically, or park my car in a bad neighborhood (maybe not the neighborhood where I have declared my residence)?
How would my health insurance risk profile change if my mobile purchase history shows me buying cigarettes, or alcohol, or even just high-cholesterol food? And if my call history shows erratic sleep patterns and
a reduction of my social circle – common signs of depression – will my life insurance risk profile change?
Today we deliberately turn our eyes away from the predictive value that such real-time Big Data could provide – because of our justifiable fear for its social and individual consequences. But this ostentation of innocence cannot be sustained for long. Whether we like it or not, our fundamental ideas about choice, destiny, discrimination, individual and social responsibility will be put to the test by the emerging data science – and we predict that insurance as we know it today will be one of the first casualties.
It’s unlikely that all three predictions will play out exactly as we’ve called them. But they are representative of recurring patterns we see in the expansion of disruptive platform technologies, from the pc to the Internet to the mobile phone. Firstly, the new platforms allow existing services to be offered through new channels, and by new players. Secondly, they allow new “native” services to emerge, that solve old problems in new ways.
And finally, they shift the balance of power and value in whole industries in ways that lead to the collapse of existing business models, and the rise of their replacements from unexpected quarters. The players who benefit from this are those who combine fluency with the new technology, with willingness to embrace change and disruption – which gives us reason to suspect that many will be from outside the boundaries of the financial services industry today.
(article by T. Sutton)