I like that even the claims processing side can be automated using ML. Traditionally, insurance companies have had to employ hundreds of claims adjusters in order to evaluate claims and reduce the incidence of fraud. However, because Lemonade is removing the cost of hiring claims adjusters, it can probably afford to have a slightly higher incidence of fraud. As Lemonade’s claims algorithm improves and the incidence of fraud is reduced, Lemonade should be able to materially outperform traditional insurance companies.
I wonder, however, about a few things:
1. To what extent is Lemonade’s algorithm unable to underwrite and process more complex and costly claims? Such tail-risks or black swan events (e.g. large hurricanes) could potentially bankrupt the company, especially because doesn’t seem to have investment gains on its float to provide additional income. Therefore, Lemonade’s solvency becomes even more dependent on the accuracy of its underwriting model (i.e. will 80% of premiums always be sufficient to cover claims and reinsurance)
2. As Lemonade gains scale, will it be able to reduce the 20% fee? I could imagine that traditional insurance companies would be able to charge a much lower fee once they move into automated/digital insurance because the would have better economies of scale/lower operating costs to cover.
I like that Nokia is using open innovation to populate the front end of its innovation funnel, but I worry about Nokia’s ability to take these ideas and turn them into viable businesses or products. Nokia is effectively running a corporate venture capital arm, and therefore, Nokia’s ability/willingness to commercialize ideas will be limited to products that are adjacent to its existing/aspirational business lines. I doubt that Nokia is well positioned to take moonshot ideas and commercialize them if they are not somehow synergistic with Nokia’s other business lines. Nokia is not GoogleX. Perhaps Nokia can incubate ideas and sell them to other businesses, but I worry that this would be too far a departure from Nokia’s core competencies.
I’d love to better understand the economics of Adidas’s 3D printed shoes. Adidas could either focus on improving performance, or on lowering costs of its shoes. It seems likely that in the beginning, Adidas would have to focus more on performance (and selling to extreme users / early adopters), and then reduce costs over time to serve a broader market for the product. I expect that the key to encouraging broader adoption of 3D printed shoes will be to convince KOLs like athletes and celebrities of the value of the shoes from a functional/performance perspective. However, to the extent that the market for high-end shoes is a “luxury” market that values scarcity and exclusivity, it might be hard to convince customers that a shoe that is printed can be exclusive.
Open innovation is definitely a great way to identify new ideas for SIA, but I wonder whether SIA has given the public poor guidance on the problems worth solving. Open innovation is only as effective as the problems it is asked to solve. In this case, if SIA has been losing market share to other airlines, I think it should be focusing on the most important problems (e.g. overbooking, delays, route availability, pricing), rather than spending time tweaking the mobile app and the inflight shopping experience. As such, open innovation at times seems like a panacea, and may not actually lead to any real solutions.
I wonder whether banks like JPM have been able to quantify the new risks that arise from the adoption of ML. If ML algorithms are making trading decisions, do the humans who designed the algorithms have sufficient control to stop the system from making large and risky trades? I worry that broader adoption of algorithmic trading at banks and hedge funds creates new systemic risks and undermines our ability to understand how the market behaves. What happens when all algorithms across multiple firms start to behave in the same way due to a convergence of specific signals?
I agree that ML will radically transform the financial services industry, but I’m not equally excited about all potential applications. Using ML to reduce compliance costs and legal paperwork is probably a good starting point. ML-based trading and lending probably requires a more cautious approach.
I agree with the previous comment regarding the risks associated with a large bet on AM. Given the nascent nature of AM, it would likely be extremely difficult to ascertain fundamental value for any acquisition targets. Also, given how fast AM technology is evolving, there is significant risk of technology and equipment obsolescence, and TDG risks overpaying for an target that could become worthless. It also seems likely that acquisitions of AM companies (esp. those that have IP, but limited cash flow) would be dilutive to near-term earnings/cash flow at TDG, which might lead to an adverse share price reaction (at least until the market is willing to give TDG credit for the longer term value of the combination).
Instead, TDG should consider forming a JV with another company who has AM capabilities, rather than spending $2Bn on an acquisition. JV’s can help mitigate the valuation problem because both partners will be invested in the future of the JV; unlike an outright sale, there is less incentive for the seller to “fleece” the buyer. In addition, if TDG can form a JV with a company that has AM R&D capabilities, TDG might gain ongoing access to evolving AM technologies, thereby also reducing obsolescence risk.