I want to respond to your question around the future of insurance, given how it will be shaped by technology in the years to come. I think one major shift – in addition to faster/more efficient issuing of new policies – will be a reduction of fraud. The large players like Mass Mutual have decades of data on which types of policyholders or claims are most likely to be problematic. In addition to preventing the issuance of policies to ‘likely’ fraud perpetrators, insurance companies can use machine learning to spot fraudulent claims as they come in. This will save countless hours of adjustor time and drive down wasted resources spent on false claims.
It’s fascinating to speculate how the face of healthcare will change in the next 5-10 years as consumers drive more of the decision-making and demand more personalized care.
At the same time, I agree with your concern that projects like 23andMe’s genetic data mining could trigger patient privacy scandals in the future. I believe the answer to avoid such an issue is by (1) using transparency and upfront disclosure with customers about how their data will be used; and (2) sharing that value / those insights with users of the product (e.g., new medication that may be particularly effective for their genotype).
I generally think of mining as a very traditional, ‘old-school’ industry so it’s interesting to consider how technology is reaching this sector!
On your first question, I think it’s clear that AV will significantly impact operations at any of Suncor’s mines. As a rule, change is hard – usually even harder than was expected at the outset! I expect the biggest challenge Suncor will face is negative publicity from any layoffs that come as a consequence of this initiative. I wonder if reinvesting some of the cost savings of these vehicles could be applied towards job re-training for the drivers?
I was really excited to read about what Alibaba is trying with Zhima Credit, but was similarly concerned by the questions of fairness and regulatory compliance that the author raised. It’s a similar challenge that the US credit rating bureaus faced in the wake of the Equifax data leak. I think they can proactively address this in three ways: (1) be transparent with customers about how data is gathered and how scores are formed; (2) work in partnership with government other credit-rating financial institutions to lay out industry-wide ground rules; and (3) create feedback loops for users. If credit scores are both more widely accessible to underbanked individuals AND seem like less of a black box, everyone (including Alibaba) will win.
I was really intrigued by this piece – fashion is one place where I didn’t expect to see relevance for machine learning, but clearly TNF is finding ways to drive sales and upsell customers with precisely these tools!
To address your question, I think TNF should only consider bringing machine learning to its brick-and-mortar stores in a very conservative way. I’d expect customers who come to a retail outlet in person are more traditional shoppers that may be off-put by non-human recommendation engines telling them what to try on or buy. However, if employees in the store were given tablets that can provide this information seamlessly (e.g., a man+machine team), that could be a high quality customer experience and a way for employees to focus on selling rather than staying up-to-date on every single line in the TNF product assortment.
I don’t believe all organizations should building machine learning capabilities in-house; rather, I think deploying machine learning should be achieved via acquisition, outsourcing, or partnership. Morgan Stanley has a clear competitive advantage and core capability as a retail banking institution – i.e., in servicing loans, pricing risk, etc. They do not have existing talent to build out their own algorithms to handle customer service functions, for example; they would be better suited partnering with an expert in cloud-based AI like AWS/Amazon’s Lex product suite.