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AI aims at banking

Despite the bad reputation some older movies have given AI, a recent survey by Accenture shows that it could well be a big deal in banking, says KELE BOAKGOMO, Managing Director for Financial Services practice at Accenture, South Africa.

Artificial Intelligence gets a bad rap in pop culture. Movies like Terminator (with its rebellious Skynet) and 2001 (with its murderous HAL 9000) portray a future where the robots get smart, and conclude that it is in their interests to try and destroy mankind.

But the truth about AI is a lot more mundane. Most of us use AI every day when we talk and interact with Siri or Google on our phones and AI is why Netflix knows what movies you’ll like and what other products you’ll want to buy on Amazon.

And AI is poised to become a big deal in banking. An Accenture poll of more than 600 bankers reveals that 79 percent believe AI will revolutionize how banks learn from and interact with customers; 76 percent believe that AI interfaces will be the primary point of contact between banks and customers within three years; and 71 percent think AI can be the face of their brand.

AI encompasses three different technologies: Language processing that allows computers to “talk” with humans; machine learning where computers compare new information with existing data to find patterns, similarities and differences; and expert software systems that provide personalised advice. At its best, machines learn from experiences and can interact with humans and behave in ways that mimic the human brain.

Robots and artificial intelligence are already being embraced by banks around the world, both in branches and in back offices. At City Union Bank in the Indian city of Chennai, a robot called Lakshmi tells customers about their account balances and the current interest rates on mortgages. At the Bank of Tokyo Mitsubishi UFJ, a robot called Nao analyses facial expressions and behavior as it interacts with customers in Japanese, English and Chinese. Lakshmi and Nao are early, visible signs of how banks can use AI to personalize the banking experience.

In South Africa, AI is not new, but the move of AI beyond process to interaction with customers is new. AI is coming of age, tackling problems both big and small by making interactions simple and smart. It is becoming the new user interface in the banking space and underpinning the way we transact and interact with systems. Nearly two-thirds (63 percent) of South African bank respondents in the recent Technology Vision for banking research agree that AI will revolutionise the way they gain information and interact with customers.

Now, banks in the U.S are also starting to catch on. Capital One customers can check their accounts and pay credit card bills by talking to Amazon’s Alexa and HSBC customers can quiz the bank’s virtual online assistant Olivia who can answer questions about security and other issues and learns from the effectiveness of her answers. Santander has voice banking, powered by Nuance’s virtual assistant Nina, which allows customers to make transfers and payments based on voice recognition authentication. And, RBS has developed Luvo – a customer service pop-up window that asks customers online if they need help with simple tasks, freeing staff to work on resolving more complex problems. At Accenture, we’ve built Collette – a virtual mortgage adviser that asks customers questions in a natural conversational style and generates personally-tailored advice.

But these cool services are only the first step. Banks need to start using AI to streamline the process of applying for loans or to reimagine ATM interactions to reflect the customer’s typical needs, giving customer’s a blank screen to start with, for example, rather than a standard menu. In the end, AI will help banks truly customise the banking experience by making personalised recommendations and advice. Your bank’s AI might notice from your deposits that your salary has increased and will suggest ways to save more for retirement, or that you just started purchasing diapers for the first time and maybe it’s time to start a college savings account.

Crunching a trove of customer data – everything from banking to automotive records and credit bureau reports – will give banks a clearer picture than ever before of what their customers might want from a financial institution. That’s important because more than two-thirds (67 percent) of bankers say they currently struggle to understand their customers’ needs.

But as banks move forward, they have to make sure they don’t lose the human touch where it’s needed. AI can delight customers and make their transactions quicker and easier. But it can’t completely replace people. In many situations, from personal interactions to nuanced understanding of someone’s financial status, customers need to work with human beings.

A Weber Shandwick survey reveals that, while more than half of consumers say they would trust AI to provide financial guidance, 52 percent of people are concerned about the possibility of stolen data or invasion of privacy — concerns that banks can address by applying extra levels of security around complex transactions such as transferring money between accounts.

Incorporating AI will make banks more efficient, save them money and will make staff more productive by freeing them up to help customers in a more targeted way. And, as we have noticed from other disruptive technologies, once other banks have embraced these advances they will become a mandatory component of any banking offering to retain customers and gain new ones.

Companies should take three steps to ensure that they get it right with AI: 1) Create a clear strategy for using customer data and define how AI tools can best leverage that information; 2) Consider developing an AI Center of Excellence to spearhead the effort; 3) Create a test-and-learn environment to accelerate innovation and to explore how machines can add the cognitive processes of perception, learning and reasoning.

It’s inevitable that customers will have fewer visits at bank branches, but these few interactions with human staff will become more important to customer satisfaction. That means that the bank of the future will need to blend a mix of AI and human interactions if they want to be successful. What we see around us is just the beginning.

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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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