Links – Week of 24 December

Insurance Industry News

Trov offers a combination policy (Medical + Trip Interruption + Lost Items) to passengers in Waymo self-driving cars:

LindkedIn Announcement

FT Analysis

AXA teamed up with UberEATs to provide another combination policy (A&H and Third Party Liability) to its couriers in Europe:

Reuters

I blogged about sub-prime auto and increasing frequency here:

http://ronaldrichman.co.za/2017/12/21/usa-personal-auto-frequency-and-the-link-to-sub-prime-loans-speculative/

Data Vis

Design secrets behind the FT’s best charts of the year

Stats/R:

Simply Stats with a nice summary of interesting stats/R items from 2017:

https://simplystatistics.org/2017/12/20/a-non-comprehensive-list-of-awesome-things-other-people-did-in-2017/

Deep Learning

This came out a few weeks ago, but it’s worth noting this new approach to  neural networks – Capsule Networks – from Hinton:

ARXIV Paper

Explainer

Research

IBNR

COHERENT INCURRED PAID (CIP) MODELS FOR CLAIMS RESERVING

Yield Curves – Might be useful for some African countries

Approximating risk-free curves in sparse data environments

Equity Valuation

Art, Science or Craft?

Climate Change and Property Values (via the amazing Marginal Revolutions blog):

From the abstract – Homes exposed to sea level rise (SLR) sell at a 7% discount relative to observably equivalent unexposed properties equidistant from the beach. This discount has grown over time and is driven by sophisticated buyers and communities worried about global warming.

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3073842

 

USA Personal Auto Frequency and the link to Sub-prime Loans (Speculative)

A topic receiving some attention in the USA is the deteriorating profitability in both Personal and Commercial Auto. Quite a bit has been written in industry publications, and a good summary of the situation appears in the Swiss Re Global Insurance Review for 2017.

The reasons for the increasing loss ratios (driven by increased frequency and severity) are a matter of some debate and speculation. The Swiss Re article notes increased employment, lower prices of petrol, and more drunk and distracted driving as causes of the increased frequency (I’ve also seen legalization of marijuana given as a reason) and the increased costs of repairs of newer vehicles as driving severity. However, it doesn’t seem to me that anyone has a conclusive take on the subject so here is my contribution to the topic.

It is well known that credit scores are predictive of future claims and actuaries generally include credit scores in Auto pricing models (I am making no comment on whether this is a morally unacceptable thing to do). A very good paper on the subject is Golden et al. (2016) in the NAAJ who provide some figures relating to the impact of credit score on loss ratios, frequency and severity based on a sample of data from US based P&C insurers writing Personal Lines Auto in 1998. They also provide some explanations of why credit scores are predictive. They find that credit score is a significant predictor of both frequency and severity.

It is worth wondering what has happened to the average credit score of drivers on the roads in the US. An article linked in Matt Levine’s excellent daily newsletter Money Stuff of 21 December 2017 discusses the huge increase in sub-prime auto loans made after the crisis and notes that default rates are now at the same level as in 2010. If loan underwriting was made significantly weaker over the past couple of years and many more people with lower credit scores are now on the roads compared to earlier years, then it would be fair to expect that the average credit score is worse than in previous years. Also, if credit score is still predictive of frequency and the relationship holds at the lower credit scores, then it would be fair to expect increased frequency compared to prior years.

All things being equal, this wouldn’t apply to loss ratios, since the premiums quoted for this segment would be higher than for those with better credit scores. Of course, this may not have been fully accounted for in some pricing models. However, frequency is generally calculated without reference to premiums.

References

Golden, L. L., Brockett, P. L., Ai, J., & Kellison, B. (2016). Empirical Evidence on the Use of Credit Scoring for Predicting Insurance Losses with Psycho-social and Biochemical Explanations. North American Actuarial Journal, 20(3), 233-251.

 

Links – Week of 17 December

Insurance Industry:

Horrific year for insurers

Series C funding for Lemonade = $120m

Statistical Learning:

Intro to Gaussian Processes

New research:

Mortality

Free book – Visualizing Mortality Dynamics in the Lexis Diagram

IBNR

Individual claims reserving: a survey

Non parametric individual claim reserving in insurance