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Insurers turn to Facebook timelines to beat fraud

By TIMOTHY SMIT, director and MEGAN BADENHORST, senior associate in the dispute resolution practice at Cliffe Dekker Hofmeyr

Facebook, according to Statista, had 2.32 billion monthly active users by the fourth quarter of 2018. Thanks to Facebook, you can post videos, brag about your children, announce your new job or even moan about your former boss to people all over the world, instantly. Many people even have public profiles – meaning that they do not change their security settings to limit who can see what they post on Facebook – but do they know who’s watching?

The Guardian newspaper recently reported that William Owen wasn’t worried about who was looking at his profile. Mr Owen had come 7th out of 2,000 in a 10km race. Before that, he had signed up for a half marathon and posted a photograph of himself on top of Mount Snowdon. Who wouldn’t plaster that all over Facebook? His insurer certainly “liked” his photos because the 29-year-old had, a few months earlier, claimed to have suffered neck and back pain caused by whiplash after a car reversed into his vehicle at a garage. His insurer understandably didn’t think that they should have to pay his claim.

Insurance companies may use information found on a public Facebook profile. Yes, there is a right to privacy in s14 of the Constitution and it includes the right not to have your communication infringed but that right is not absolute. It is framed by subjective and objective expectations of privacy. When you click “I accept” on the standard terms and conditions on any social media platform you erode your own subjective expectation of privacy.

Facebook, for example, expressly state in their Terms of Service that they “Provide a personalised experience for you”. How? By analysing “the connections you make, the choices and settings you select, and what you share and do on and off our Products”. Your objective expectation of privacy requires the rest of society to recognise your expectation of privacy as being reasonable. So, if you are instagramming your dinners, tweeting your workout routine or vlogging about your online dating – society will assume that you aren’t a very private person.

Facebook aside, to what other apps do you give personal information? Did you check their Terms of Service? The Wall Street Journal (WSJ) reported that several popular health apps share personal and health data with Facebook. Extreme Tech recounted a finding by WSJ that 11 of the 70 iOS apps it tested shared personal or health data with Facebook’s servers via Facebooks Analytics. These included apps that record heart rate data or even when a user was having her period.

Going back to insurance companies – are they allowed to use unlawfully obtained information? For example, information obtained by hacking? Surprisingly, the position is not completely clear.

In Harvey v Niland and Others, Harvey relied on Niland’s private Facebook posts to prove that Niland was secretly competing and violating his fiduciary duties to their joint business. Was the Facebook evidence admissible? Niland said it infringed his right to privacy and was obtained through the commission of an offence under s86(1) of the Electronic Communications and Transactions Act, No 25 of 2002 (Act). Judge Plasket held that the Act didn’t prohibit evidence obtained in contravention of s86(1) but reasoned that the admission of the evidence would depend

·       on the nature and extent of the violation of Niland’s right to privacy; and

·       whether Harvey could have obtained the evidence in another, lawful way.

Judge Plasket found that hacking Niland’s Facebook communications would have produced both information that was relevant to the issue before him and information that was irrelevant and entirely private. The relevant portion accessed established that Niland had been conducting himself in a duplicitous manner, contrary to the fiduciary duties he owed to the business – not to mention the fact that he had denied the allegations and undertaken not to do as he had done. Plasket said “his claim to privacy rings rather hollow.” Finally, the Judge found that the evidence was essential to Harvey’s case and could not in practice have been procured in another, lawful way. “All he had was a suspicion but, without [the hacked posts], he had no evidence of Niland’s wrongdoing.” The application to strike out the hacked posts was dismissed with costs.

Arguably, an insurer can also rely on unlawfully obtained evidence to defeat a fraudulent claim. A fraudulent claimant is obviously acting dishonestly and what if that is the only way the insurer can prove it? Bhekisisa reports that fraud, waste and abuse is costing the private healthcare system more than R22 billion. In 2018, it was reported by IOL that by rooting out fraudulent claims, Discovery Health saved R568 million for its client schemes in 2017, up from R405 million in 2016. Should the rest of us have to pay higher premiums because Jane Soap faked a knee injury and then used her pay-out to go skiing? Surely not.

It is an intriguing debate, but in the meantime, you might want to re-evaluate your online and in app activity and decide what sort of privacy you expect to enjoy.

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Now IBM’s Watson joins IoT revolution in agriculture

Global expansion of the Watson Decision Platform taps into AI, weather and IoT data to boost production

IBM has announced the global expansion of Watson Decision Platform for Agriculture, with AI technology tailored for new crops and specific regions to help feed a growing population. For the first time, IBM is providing a global agriculture solution that combines predictive technology with data from The Weather Company, an IBM Business, and IoT data to help give farmers around the world greater insights about planning, ploughing, planting, spraying and harvesting.

By 2050, the world will need to feed two billion more people without an increase in arable land [1]. IBM is combining power weather data – including historical, current and forecast data and weather prediction models from The Weather Company – with crop models to help improve yield forecast accuracy, generate value, and increase both farm production and profitability.

Roric Paulman, owner/operator of Paulman Farms in Southwest Nebraska, said: “As a farmer, the wild card is always weather. IBM overlays weather details with my own data and historical information to help me apply, verify, and make decisions. For example, our farm is in a highly restricted water basin, so the ability to better anticipate rain not only saves me money but also helps me save precious natural resources.”

New crop models include corn, wheat, soy, cotton, sorghum, barley, sugar cane and potato, with more coming soon. These models will now be available in the Africa, U.S. Canada, Mexico, and Brazil, as well as new markets across Europe and Australia.

Kristen Lauria, general manager of Watson Media and Weather Solutions at IBM, said: “These days farmers don’t just farm food, they also cultivate data – from drones flying over fields to smart irrigation systems, and IoT sensors affixed to combines, seeders, sprayers and other equipment. Most of the time, this data is left on the vine — never analysed or used to derive insights. Watson Decision Platform for Agriculture aims to change that by offering tools and solutions to help growers make more informed decisions about their crops.” 

The average farm generates an estimated 500,000 data points per day, which will grow to 4 million data points by 2036 [2]. Applying AI and analysis to aggregated field, machine and environmental data can help improve shared insights between growers and enterprises across the agriculture ecosystem. With a better view of the fields, growers can see what’s working on certain farms and share best practices with other farmers. The platform assesses data in an electronic field record to identify and communicate crop management patterns and insights. Enterprise businesses such as food companies, grain processors, or produce distributors can then work with farmers to leverage those insights. It helps track crop yield as well as the environmental, weather and plant biologic conditions that go into a good or bad yield, such as irrigation management, pest and disease risk analysis and cohort analysis for comparing similar subsets of fields.

The result isn’t just more productive farmers. Watson Decision Platform for Agriculture could help a livestock company eliminate a certain mold or fungus from feed supply grains or help identify the best crop irrigation practices for farmers to use in drought-stricken areas like California. It could help deliver the perfect French fry for a fast food chain that needs longer – not fatter – potatoes from its network of growers. Or it could help a beer distributor produce a more affordable premium beer by growing higher quality barley that meets the standard required to become malting barley.

Watson Decision Platform for Agriculture is built on IBM PAIRS Geoscope from IBM Research, which quickly processes massive, complex geospatial and time-based datasets collected by satellites, drones, aerial flights, millions of IoT sensors and weather models. It crunches large, complex data and creates insights quickly and easily so farmers and food companies can focus on growing crops for global communities.

IBM and The Weather Company help the agriculture industry find value in weather insights. IBM Research collaborates with start up Hello Tractor to integrate The Weather Company data, remote sensing data (e.g., satellite), and IoT data from tractors. IBM also works with crop nutrition leader Yara to include hyperlocal weather forecasts in its digital platform for real-time recommendations, tailored to specific fields or crops. IBM acquired The Weather Company in 2016 and has since been helping clients better understand and mitigate the cost of weather on their businesses. The global expansion of Watson Decision Platform for Agriculture is the latest innovation in IBM’s efforts to make weather a more predictable business consideration. Also just announced, Weather Signals is a new AI-based tool that merges The Weather Company data with a company’s own operations data to reveal how minor fluctuations in weather affects business.

The combination of rich weather forecast data from The Weather Company and IBM’s AI and Cloud technologies is designed to provide a unique capability, which is being leveraged by agriculture, energy and utility companies, airlines, retailers and many others to make informed business decisions.

[1] The UN Department of Economic and Social Affairs, “World Population Prospects: The 2017 Revision”

[2] Business Insider Intelligence, 2016 report: https://www.businessinsider.com/internet-of-things-smart-agriculture-2016-10


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What if Amazon used AI to take on factories?

By ANTONY BOURNE, IFS Global Industry Director for Manufacturing

Amazon recently announced record profits of $3.03bn, breaking its own record for the third consecutive time. However, Amazon appears to be at a crossroads as to where it heads next. Beyond pouring additional energy into Amazon Prime, many have wondered whether the company may decide to enter an entirely new sector such as manufacturing to drive future growth, after all, it seems a logical step for the company with its finger in so many pies.

At this point, it is unclear whether Amazon would truly ‘get its hands dirty’ by manufacturing its own products on a grand scale. But what if it did? It’s worth exploring this reality. What if Amazon did decide to move into manufacturing, a sector dominated by traditional firms and one that is yet to see an explosive tech rival enter? After all, many similarly positioned tech giants have stuck to providing data analytics services or consulting to these firms rather than genuinely engaging with and analysing manufacturing techniques directly.

If Amazon did factories

If Amazon decided to take a step into manufacturing, it is likely that they could use the Echo range as a template of what AI can achieve. In recent years,Amazon gained expertise on the way to designing its Echo home speaker range that features Alexa, an artificial intelligence and IoT-based digital assistant.Amazon could replicate a similar form with the deployment of AI and Industrial IoT (IIoT) to create an autonomously-run smart manufacturing plant. Such a plant could feature IIoT sensors to enable the machinery to be run remotely and self-aware; managing external inputs and outputs such as supply deliveries and the shipping of finished goods. Just-in-time logistics would remove the need for warehousing while other machines could be placed in charge of maintenance using AI and remote access. Through this, Amazon could radically reduce the need for human labour and interaction in manufacturing as the use of AI, IIoT and data analytics will leave only the human role for monitoring and strategic evaluation. Amazon has been using autonomous robots in their logistics and distribution centres since 2017. As demonstrated with the Echo range, this technology is available now, with the full capabilities of Blockchain and 5G soon to be realised and allowing an exponentially-increased amount of data to be received, processed and communicated.

Manufacturing with knowledge

Theorising what Amazon’s manufacturing debut would look like provides a stark learning opportunity for traditional manufacturers. After all, wheneverAmazon has entered the fray in other traditional industries such as retail and logistics, the sector has never remained the same again. The key takeaway for manufacturers is that now is the time to start leveraging the sort of technologies and approaches to data management that Amazon is already doing in its current operations. When thinking about how to implement AI and new technologies in existing environments, specific end-business goals and targets must be considered, or else the end result will fail to live up to the most optimistic of expectations. As with any target and goal, the more targeted your objectives, the more competitive and transformative your results. Once specific targets and deliverables have been considered, the resources and methods of implementation must also be considered. As Amazon did with early automation of their distribution and logistics centres, manufacturers need to implement change gradually and be focused on achieving small and incremental results that will generate wider momentum and the appetite to lead more expansive changes.

In implementing newer technologies, manufacturers need to bear in mind two fundamental aspects of implementation: software and hardware solutions. Enterprise Resource Planning (ERP) software, which is increasingly bolstered by AI, will enable manufacturers to leverage the data from connected IoT devices, sensors, and automated systems from the factory floor and the wider business. ERP software will be the key to making strategic decisions and executing routine operational tasks more efficiently. This will allow manufacturers to keep on top of trends and deliver real-time forecasting and spot any potential problems before they impact the wider business.

As for the hardware, stock management drones and sensor-embedded hardware will be the eyes through which manufacturers view the impact emerging technologies bring to their operations. Unlike manual stock audits and counting, drones with AI capabilities can monitor stock intelligently around production so that operations are not disrupted or halted. Manufacturers will be able to see what is working, what is going wrong, and where there is potential for further improvement and change.

Knowledge for manufacturing

For many traditional manufacturers, they may see Amazon as a looming threat, and smart-factory technologies such as AI and Robotic Process Automation (RPA) as a far off utopia. However, 2019 presents a perfect opportunity for manufacturers themselves to really determine how the tech giants and emerging technologies will affect the industry. Technologies such as AI and IoT are available today; and the full benefits of these technologies will only deepen as they are implemented alongside the maturing of other emerging technologies such as 5G and Blockchain in the next 3-5 years. Manufacturers need to analyse the needs which these technologies can address and produce a proper plan on how to gradually implement these technologies to address specific targets and deliverables. AI-based software and hardware solutions will fundamentally revolutionise manufacturing, yet for 2019, manufacturers just have to be willing to make the first steps in modernisation.

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