The digital transformation of existing business models is a theme of our age. Robotic process automation (RPA) is one of the many acronyms to have found its way into the terminology of businesses today. I highlighted the potential for telecoms to digitalise their business models in this post. Klaus Schwab of the World Economic Forum in his book “Fourth Industrial Revolution” refers to the current era as one whereby “new technologies that are fusing the physical, digital and biological worlds, impacting all disciplines, economies and industries, and even challenging ideas about what it means to be human”.
The financial services business is one that is regularly touted as been rife for transformation with fintech being the much-hyped buzz word. I last posted here and here on fintech and insurtech, the use of technology innovations designed to squeeze out savings and efficiency from existing insurance business models.
Artificial intelligence (AI) is used as an umbrella term for everything from process automation, to robotics and to machine learning. As referred to in this post on equity markets, the Financial Stability Board (FSB) released a report called “Artificial Intelligence and Machine Learning in Financial Services” in November 2017. In relation to insurance, the FSB report highlights that “some insurance companies are actively using machine learning to improve the pricing or marketing of insurance products by incorporating real-time, highly granular data, such as online shopping behaviour or telemetrics (sensors in connected devices, such as car odometers)”. Other areas highlighted include machine learning techniques in claims processing and the preventative benefits of remote sensors connected through the internet of things. Consultants are falling over themselves to get on the bandwagon as reports from the likes of Deloitte, EY, PwC, Capgemini, and Accenture illustrate.
One of the better recent reports on the topic is this one from the reinsurer SCOR. CEO Denis Kessler states that “information is becoming a commodity, and AI will enable us to process all of it” and that “AI and data will take us into a world of ex-ante predictability and ex-post monitoring, which will change the way risks are observed, carried, realized and settled”. Kessler believes that AI will impact the insurance sector in 3 ways:
- Reducing information asymmetry and bringing comprehensive and dynamic observability in the insurance transaction,
- Improving efficiencies and insurance product innovation, and
- Creating new “intrinsic“ AI risks.
I found one article in the SCOR report by Nicolas Miailhe of the Future Society at the Harvard Kennedy School particularly interesting. Whilst talking about the overall AI market, Miailhe states that “the general consensus remains that the market is on the brink of a revolution, which will be characterized by an asymmetric global oligopoly” and the “market is qualified as oligopolistic because of the association between the scale effects and network effects which drive concentration”. When referring to an oligopoly, Miailhe highlights two global blocks – GAFA (Google/Apple/Facebook/Amazon) and BATX (Baidu/Alibaba/Tencent/Xiaomi). In the insurance context, Miailhe states that “more often than not, this will mean that the insured must relinquish control, and at times, the ownership of data” and that “the delivery of these new services will intrude heavily on privacy”.
At a more mundane level, Miailhe highlights the difficulty for stakeholders such as auditors and regulators to understand the business models of the future which “delegate the risk-profiling process to computer systems that run software based on “black box” algorithms”. Miailhe also cautions that bias can infiltrate algorithms as “algorithms are written by people, and machine-learning algorithms adjust what they do according to people’s behaviour”.
In a statement that seems particularly relevant today in terms of the current issue around Facebook and data privacy, Miailhe warns that “the issues of auditability, certification and tension between transparency and competitive dynamics are becoming apparent and will play a key role in facilitating or hindering the dissemination of AI systems”.
Now, that’s not something you’ll hear from the usual cheer leaders.
Posted in Insurance Market
Tagged Accenture, AI, AI systems, algorithm bias, Alibaba, Amazon, Apple, Artificial Insurance, artificial intelligence, asymmetric global oligopoly, auditability, Baidu, Capgemini, claims processing, competitive dynamics, Deloitte, Denis Kessler, digital transformation, ex-ante predictability, ex-post monitoring, EY, Facebook, Financial Stability Board, fintech, Fourth Industrial Revolution, Future Society, Google, granular data, Harvard Kennedy School, improving efficiencies, information asymmetry, information commodity, insurance Innovation, insurance sector, InsurTech, internet of things, Klaus Schwab, machine learning, machine learning algorithms, machine learning techniques, new AI risks, Nicolas Miailhe, product innovation, PwC, robotic process automation, robotics, SCOR, telemetrics, Tencent, transparency, Xiaomi
One lesson from the internet bubble is that big is beautiful in tech. But longevity is another lesson, think Yahoo! So one must be fickle in ones tech affections and one must never ever pay too much. After much patience, I was lucky enough to eventually get into Apple in early 2013 when sentiment was particularly sore. I didn’t manage to heed my own advice on getting into Google at a reasonable price in December 2014 when it was trading around 60% of its current value, as per this post on internet relative valuations (more on that post later). Since 2013, I have watched sentiment gyrate on AAPL as the standard graph I use below illustrates (most recent AAPL posts are here and here). I used the current $135 price high as the most recent data point for the Q12017 valuation.
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Investors and analysts seem giddy these days about the impact of Trump tax changes and the iPhone 10 year anniversary on AAPL and have been pointing to Berkshire’s position increase in AAPL as confirmation bias of more upside. I, on the other hand, have been taking some of AAPL off the table recently on valuation concerns and will likely again be a buyer when the inevitable worries return along the “one trick iPhone pony” lines. God bless gyrating sentiment! Even Lex in the FT was saying today that the current TTM PE ex net cash of 13 is reasonable (eh, a TTM PE ex net cash of 7 a year ago was more reasonable)! AAPL still has be a core holding in anybody’s portfolio but prudent risk management requires trimming at this price in my opinion.
In my search for new ideas whilst I await some divine sense to emerge from the Trump & Brexit fog, I thought it would be interesting to revisit the post referred above on internet valuations. First off, I took the graph showing forward PEs to projected EPS growth using analyst estimates from December 2014 and inserted the actual change in share price from then to now. Two notable exceptions, at the extremities, from the graph below are Amazon and Twitter with share price changes of 173% and -56% respectively.
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Although every company is different and has its own dynamics, my simplistic take from the graph below is that high PE stocks (e.g. > 40) with high EPS projections (e.g. > 35%) can easily run aground if the initial high growth phase hits harsh reality. The sweet spot is decent PEs with EPS growth in the 15% to 35% range (again assuming one can get comfortable that the EPS growth projections are real) indicative of the larger established firms still on the growth track (but who have successfully navigated the initial growth phase) .
A similar screen based upon today’s values and analyst estimates out to 2018 is presented below. This screen is not directly comparable with the December 2014 one as it goes out two years rather than one.
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Based upon this graph, Google and Netease again look worthy of investigation with similar profiles to two years ago. Netease has the attraction of a strong growth track record with the obvious Chinese political risk to get over. Expedia looks intriguing given the strong growth projected off a depressed 2016 EPS figure. Ebay and Priceline may also be worth a look purely on valuation although I have a general aversion to retail type stocks so I doubt I’ll bother look too deeply. All of the data used for these graphs is based upon analyst estimates which also need to be validated.
Valuations currently are juicy, generally too juicy for me, so this exercise is simply one to determine who to investigate further for inclusion on a watch-list. Time permitting!
Posted in Equity Market
Tagged AAPL, AAPL guidance, AAPL tax bill, AAPL valuation, Alibaba, Alphabet, Apple below $100, Baidu, bubble valuations, China iPhone sales, Chinese internet stocks, Ctrip, diluted GAAP EPS, Earnings guidance, EPS multiples, expedia, Facebook, forward PE, forward PE Google, forward PE ratio, future demand iPhone, geographic revenue split, Google valuation, internet bubble, internet valuations, iPhone average price, iPhone gross margin, iPhones sold, linkedin, NetEase, overvalued AAPL, PE ratio, sentiment on Appl, SINA, SOHU, tech target price, technology stocks, Tencent, undervalued AAPL, undervalued Apple