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
According to this article in the FT by Bhanu Baweja of UBS, the rise in the spread between the dollar 3-month LIBOR, now over 2.25% compared to 1.7% at the start of the year, and the overnight indexed swap (OIS) rate, as per the graph below, is a “red herring” and that “supply is at play here, not rising credit risk”. This view reflects the current market consensus, up until recently at least.
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Baweja argues that the spread widening is due to the increased T-bill-OIS spread because of increased yields due to widening fiscal deficits in the US and to the increased commercial paper (CP) to T-bill spread due to US company repatriations as a result of the Trump tax cuts. Although Baweja lists off the current bull arguments to be cheerful, he does acknowledge that an increasing LIBOR will impact US floating borrowers of $2.2 trillion of debt, half of whom are BB- and below, particularly if 3-month US LIBOR breaks past 3%. Baweja points to rises in term premiums as the real red flags to be looking out for.
Analysts such as Matt Smith of Citi and Jonathan Garner of Morgan Stanley are not as nonchalant as the market consensus as articulated by Baweja. The potential for unintended consequences and/or imbalances in this tightening phase, out of the greatest monetary experiment every undertaken, is on many people’s minds, including mine. I cannot but help think of a pressure cooker with every US rate rise ratcheting the heat higher.
Citi worry that LIBOR may be a 3-month leading indicator for dollar strengthening which may send shock-waves across global risk markets, particularly if FX movements are disorderly. Garner believes that “we’re already looking at a significant tightening of monetary policy in the US and in addition China is tightening monetary policy at the same time and this joint tightening is a key reason why we are so cautious on markets”. Given Chairman Powell’s debut yesterday and the more hawkish tone in relation to 2019 and 2020 tightening, I’ll leave this subject on that note.
The intricacies of credit market movements are not my area of expertise, so I’ll take council on this topic from people who know better.
Eh, help Eddie….what do you think?
Posted in Economics, Equity Market
Tagged Bhanu Baweja, Chairman Powell, China monetary policy, Citi, commercial paper, CP-T-bill spread, credit risk, dollar strength, fiscal deficits, hawkish tone, Jonathan Garner, LIBOR, LIBOR-OIS spread, Matt Smith, monetary experiments, monetary tightening, Morgan Stanley, OIS, overnight indexed swap, spread widening, T-bill-OIS, term premiums, UBS, unintended consequences, US floating debt
It’s been a while since I posted on the specialty insurance sector and I hope to post some more detailed thoughts and analysis when I get the time in the coming months. M&A activity has picked up recently with the XL/AXA and AIG/Validus deals being the latest examples of big insurers bulking up through M&A. Deloitte has an interesting report out on some of the factors behind the increased activity. The graph below shows the trend of the average price to book M&A multiples for P&C insurers.
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As regular readers will know, my preferred metric is price to tangible book value and the exhibit below shows that the multiples on recent deals are increasing and well above the standard multiple around 1.5X. That said, the prices are not as high as the silly prices of above 2X paid by Japanese insurers in 2015. Not yet anyway!
click to enlarge
Unless there are major synergies, either on the operating side or on the capital side (which seems to be AXA’s justification for the near 2X multiple on the XL deal), I just can’t see how a 2X multiple is justified in a mature sector. Assuming these firms can earn a 10% return on tangible assets over multiple cycles, a 2X multiple equates to 20X earnings!
Time will tell who the next M&A target will be….
Posted in Insurance Market
Tagged AIG, AXA, bermudian insurers, European reinsurers, insurance M&A action, insurance valuation, London based specialty insurers, london insurance market, M&A premium, price tangible book values, price to book value, price to tangible book value, reinsurance price to tangible book value, reinsurance pricing, reinsurance rates, risk based capital, specialty insurance sector, specialty insurer valuation, tangible book value, total return, Validus, XL Group