Facebook and Google set to expand respective financial services

Earlier this month Google and Facebook announced that they will expand the scope of their financial services by developing a Google checking account and a digital wallet that will be integrated across all Instagram services. Although these services are set to make our lives simpler, adding our financial data to their existing database allows Big Tech companies to understand our risk tolerance, buying habits, track our shopping behaviour as well as our loans and credit card exposures. Click here to read more


Insurtech start-ups in the KiwiBank accelerator

While there are no life and health insurance start-ups in the accelerator there is plenty to be interested in. Two appear to have a general insurance flavour, the best of which is Stash, a outfit I have come across before and deserves to break out into broader market success. I also like the concepts for the blockchain identity development and the KiwiSaver advice service. Check out the list here.

 


Australia: life insurance adequacy

Rice Warner has this article on life insurance adequacy for households with two adults and children, a highlight from their life insurance adequacy study which is available for purchase. In the forthcoming quarterly life report I will contrast this with estimates of insurance adequacy for New Zealand, indemnity models, and the affordability of the recommended cover amounts. Any questions on indemnity-based models and how these contrast / inform advised cover levels - please drop me a line.


Gen Re: machine learning and underwriting

Gen Re has an excellent article on the role of classification model performance in underwriting. This includes some useful comments on machine learning, plus the gloriously-named 'confusion matrix' with associated metrics for assessing the accuracy of models. There are some broad implications for the future of underwriting which can be drawn, you should check out the whole article, of course, but the thoughts below are my own, based on their article and other sources:

Larger data sets, and especially digital test data sets of sufficient size, are particularly valuable for assessing a model. Companies would do well to both maintain such data sets (over time, with claims performance data attached) and the expertise necessary to assess the value of different classification models. A presumption of the efficiency of current underwriting methodologies is not necessarily well-founded. It is also somewhat self-selecting and may lead to ignoring data not currently collected, which could be of value in developing new methods for making underwriting assessments. Non-traditional data sources - such as credit records, social media profiles, wearable health tech, and so on, could provide new opportunities for assessment models. You won't know until you look, so some investment in collecting better data is necessary R&D expenditure.

Data collected is not always equal to data assessed, also, data required to apply for a product may be reduced, while data-collection may continue after application (and it may be useful and economically rewarding to continue to collect the data) so that better analysis of factors for future modelling may be identified. A practical version of that point might be: we can still have a non-underwritten product (to make application easy) and collect lots of data to feed our machine learning tools to enable better underwriting decisions to be made on the basis of third-party data, for future sales.

 


USA: Legal and General Brings in SelfieQuote Tool

This article on Insurance Business NZ website discusses how 'selfies' are to be used in the insurance industry with the introduction of SelfieQuote which uses a photo of the consumers face to estimate their age, gender and BMI using facial analytics technology.

This is part of a growing trend towards trying to completely crush the time it takes to quote and apply for insurance cover. Lemonade in the United States has made your address the main piece of information required to quote. Increasingly insurers and reinsurers are looking at non-medical data to categorise their clients into risk pools - eliminating entirely the '30 year memory-test' approach to underwriting or the dangerous alternative of non-underwritten cover.


Digitization and the effect on jobs and wages

There is a fascinating chart (you can find here) which shows the effect of digitization on occupational categories, by the number of jobs and the wages changed. It is clear that digitization is having an effect - but now you can see how much, and in what way, the effects are felt. For now, at least, personal financial advisers - like most medical staff - appear to be thriving as digitization gathers pace. Others have not fared so well. The image below is just a picture, but if you click the link above you get an interactive chart, which is much more interesting. 


David Chaplin: on Rob Everett's criticism of financial services companies

David Chaplin, of Investment News NZ,  has this excellent piece on the comments Rob Everett made about financial services companies. There are many good quotes, so many that you should just read it, but I want to highlight these: 

Everett said the days of financial services companies building business models based on stable rules backed by long-established legislation were “over”.

Good point, and a reminder that the regulator is primarily there for the consumer, not the industry. Speaking at a fintech conference, this was bound to be well received. Everett went on to criticise some incumbents for not acting responsibly, "... or even competently".  In my first draft of this post I wrote that Everett pulled no punches, but deleted it because it was all delivered in so matter-of-fact way that there was no pugilism involved. For Everett there is no argument about this. Therefore, he welcomed newcomers and challengers of all types with: 

“Any challenger or disrupter businesses that look like they are addressing, really, the customers’ needs and wants will find financial services rich pickings,”