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FROM CASHLESS TO CARD LESS

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FROM CASHLESS TO CARD LESS

By Jason McCallum, consultant business analyst at IT consultancy BJSS

Mobile payment is going mainstream. Apple’s entry into the US payments space last year ignited a new race from technology companies to capture a slice of what will be a lucrative market.

The industry is continuing to grow in the US as Samsung, Google and PayPal announced payment services to rival Apple Pay. However as things hot up in the US, Europe will be the battlefield for the foreseeable future. The myriad of technologies that power the industry are finally reaching maturity, just as the infrastructure needed to support the systems reaches fruition. The UK is high on the list of target markets, but, overall, banks are still concerned about security and fraud issues.

Visa’s European roll out of tokenisation in April, paved the way for Apple Pay, amongst others, to provide a solution that’s more convenient than typing numbers into a keypad, but who truly has potential to succeed?

Of all the mobile payment solutions competing for a slice of the action, five favourites are emerging, but they all face obstacles yet to be overcome or that limit their solution:

Apple Pay

Apple Pay contactless payments use proven NFC (near field communication) technology built into compatible devices in conjunction with the iPhone’s existing Touch ID fingerprint sensor for security, allowing single step transactions. NFC automatically launches Pay when placed near a NFC terminal resulting in rapid transactions; supporting ‘queue busting’ customer journeys, such as transport hubs or supermarket checkouts.

Apple Pay can also be used online in a similar manner as long as merchants add support to their mobile apps. The payee credentials are held in the scheme’s enterprise systems but the transaction is tokenised, so no card information is available to the retailer.

Apple Pay maximum contactless payments will initially be limited by the wider industry agreed of £20 (rising to £30) but as early confidence builds in Apple Pay, it’s hard to see this limit remaining in the long term.

From a merchant perspective there is a 0.15% fee per transaction, which may initially limit merchant adoption. Apple Pay will be rolling out during the summer and many of the earlier disputes over Apple transaction fees seem to be resolved – so all eyes are on the level of adoption in the UK which many think will comfortably surpass the US.

Zapp

Owned by VocaLink (operator of the UK’s national payment infrastructure), Zapp has the potential to be one of the UK’s biggest payment systems. It relies on support being included in participating mobile banking apps providing real time payments between consumers and merchants without the need for a mobile wallet. It generates a digital token hiding the customer details but also works with multiple technologies such as NFC, Bluetooth, QR codes and PIN terminals. It uses the established Faster Payments network and so doesn’t attract credit card fees.

However, as the Zapp solution is not as well-known as its more popular rivals, it looks likely to be severely constrained by the speed at which both banks and merchants integrate and operate the service.

Samsung Pay

Jason McCallum

Jason McCallum

Samsung’s recent acquisition of LoopPay allows it to leverage Magnetic Secure Transmission (MST) technology. Samsung Pay mimics payments made by traditional magnetic strip cards by using existing credit/debit card magnetic strip readers and turning them into secure, contactless receivers of magnetic signals from mobile devices. This gives almost any retailer instant compatibility with Samsung Pay – the user simply holds their device up to a point of sale terminal to complete their purchase. Samsung has partnered with major payment networks such as MasterCard and Visa to integrate their technology into Samsung Pay and are also rumoured to be waiving any transaction fees.

Despite the attractions of instant compatibility, adoption seems likely to be limited, as the payment technology world is rapidly moving away from MST.

Google Wallet and Android Pay

Google Wallet has been around for a long time now but has yet to become a mainstream consumer product.

Google is launching Android Pay to take over where Google Wallet failed. The new service will allow users to make contactless payments for goods and services using their Android smartphone, initially using NFC technology. Each transaction will generate a single-use ‘token’, which is sent to the receiving device to secure against fraud. Credit card data will be stored locally so that payments can be conducted without a data connection on the smartphone. Unlike Apple Pay, Android Pay will not exist as a standalone app. Instead, it will be used by third-party apps to create payment products such as store or payment provider-specific apps, acting as a payment source as well as a credit card replacement.

PayPal Wallet

The PayPal solution is designed to replace your wallet by allowing you to check-in with stores and make purchases as if you were placing an order through a website. Rather than taping your phone against a card swiping machine and using NFC to transmit credit card information, the transaction is performed entirely through the wallet app. You look up the store, check-in at the location and then enter your order. Once your order is complete, you need only tell the cashier and they’ll take it from there.

One limitation is that it’s only available at participating stores that accept PayPal as a payment method.

Looking ahead

Factors inhibiting progress vary but all providers have one obstacle in common – success depends on consumers and retailers adopting a new payment system when they already have a tried-and-tested, trusted and easy-to-use payments system – why should they adopt another?

The frenetic race to market with the current range of technology and solutions is just the first step on the road to universally adopted mobile payments. NFC is a transitional technology, originally developed to deal with the primitive telephone connections of the past. What everyone’s working towards is a ‘no-step’ transaction where secure payments occurs seamlessly and with no user involvement or risk of fraud.

With the imminent launch of Apple Pay, the industry will see fundamental changes in banking behaviour, both for customers and for the banks themselves.

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81% of Business Managers in the Manufacturing Industry Agree that a Modern IT infrastructure Accelerates Innovation, Creativity, and Productivity

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81% of Business Managers in the Manufacturing Industry Agree that a Modern IT infrastructure Accelerates Innovation, Creativity, and Productivity 1
  • 83% of business decision makers are convinced that slow running networks and applications are inhibiting these three success factors
  • 78% of IT decision makers believe that innovation, creativity, and productivity of the employees is being limited by the technology in their companies, and 94% within this say it is costing their company money

  • 86% of business decision makers in the manufacturing industry believe that digital performance is critical for business growth

LONDON, 22nd September, 2020 – Riverbed® today launched its expanded ‘Rethink Possible: Visibility and Network Performance – The Pillars of Business Success’ Study, focused on the manufacturing industry, a critical sector for Germany. The study revealed that 81% of business decision makers in manufacturing companies are convinced that IT infrastructure plays a crucial role in enabling their organisations to be innovative, creative, and productive. And, when limited, IT infrastructure inhibits these three success factors enormously (83%). 

The Study – which lays out the indisputable link that business and IT decision makers see between strong IT infrastructure and the manufacturing industry – further revealed that 40% of business decision makers in the manufacturing sector consider IT investment to be the most important business objective at present. And that a further 35% of business decision makers in the manufacturing industry prioritise digital transformation. This means that IT expansion in manufacturing is currently more important than traditional corporate operations such as financial rationalisation. Almost two-fifths of the business decision makers (39%) stated that they had pushed digitalisation in their companies to the greatest possible extent, whilst 52% are still in the process of implementing it. 

The vast majority IT decision makers in the manufacturing industry (93%) say that a well-functioning infrastructure plays an even greater role in creativity, innovation and productivity than it does for business decision makers. At the same time, 94% of IT managers say that limitations of these three factors costs organisations a lot of money.

Other key findings from the Rethink Possible: Visibility and Network Performance Study include:

  • IT and business leaders agree that digital performance is crucial for business growth (86%) and staff retention (80%)

  • 88% of IT decision makers reveal that employee satisfaction falls considerably when systems are slow

  • More than two-thirds of IT decision makers (68%) consider network transparency in their companies to be sufficient

  • The majority of IT managers (82%) would, however, like to see more investment in network transparency

  • And finally, at least six in every ten IT decision makers (63%) acknowledge that there’s a lot of catching up to do in preparing management for the challenges of digitisation.

“The Study shows that the majority of the manufacturing industry recognises the importance of having an efficient IT infrastructure. In the coming months and years, as manufacturing returns to a ‘new normal’ way of working, companies will have to invest even more in technology to fully implement digital transformation,” comments Colette Kitterhing, Senior Director UK&I at Riverbed Technology. With remote working expected to continue and an overall shift towards a more distributed workforce as a result of the pandemic, the performance of corporate networks is also becoming increasingly important for those sectors where the focus has so far been on the automation of equipment rather than their IT infrastructure. However, the convergence of manufacturing plants and IT is creating new challenges, and the opportunities offered by digitalisation are being fully exploited. To ensure that networks and applications deliver the necessary performance and work efficiently, the IT team needs complete transparency. This is the only way for employees to be truly innovative, creative and productive in times of digitalisation.”

Rethink Possible: Evolving the Digital Experience

With 86% of IT and business decision makers in the manufacturing industry believe that digital performance is critical for business growth, technology is the enabler in this process. Riverbed’s portfolio of next-generation solutions is giving customers across the globe the visibility, acceleration, optimization and connectivity that maximizes performance and visibility for networks and applications.

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Sectigo Selected by Baidu to Provide SSL Services for All-New Baidu Trust SSL Certificates  

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Sectigo Selected by Baidu to Provide SSL Services for All-New Baidu Trust SSL Certificates   2

Sectigo, a leading provider of automated digital identity management and web security solutions, announced that Baidu (NASDAQ: BIDU), a leading Chinese search engine with more than one billion daily page views, has chosen Sectigo to provide the back-end services for the company’s all-new Baidu Trust SSL Certificates. Baidu will offer Sectigo Domain Validated (DV), Organization Validated (OV), and Extended Validation (EV) certificates white-labeled under the Baidu Trust product line, greatly expanding Sectigo’s footprint in Asia.

“Baidu is committed to making the complicated world simpler through technology. This mission includes providing our customers and users with best-practice security technologies and a seamless experience,” said Roy Zhang, Leader of Enterprise Application Ecology Products, Baidu division. “We have chosen Sectigo to supply the infrastructure for the new Baidu Trust SSL Certificates because the global CA is highly trusted and offers a certificate type for every use case, from authenticating small personal websites to large enterprise domains. Our customers can choose from many trust products, ranging from basic DV SSL certificates to more premium EV SSL certificates, based on their individual needs.”

Sectigo has issued more than 100 million certificates worldwide and offers the widest selection of white-label-ready SSL product options available from a leading certificate authority. Baidu’s Trust offering, available today to website owners in China, leverages this legacy of trust to enhance the security assurance for millions of internet users across Eastern Asia.

“Trust is a key component of Sectigo’s brand. We are not only providing trust through our certificates, but also as a proven technology partner with decades of experience,” explained Michael Fowler, President of Partners and Channels at Sectigo. “Asia and China, specifically, are experiencing an unprecedented shift to conducting business and sharing information online. Baidu and Sectigo have proven successful in their industries and collaborated to bring the best SSL security to greater China.”

“Baidu’s choice to use Sectigo for their Baidu Trust SSL certificates underscores the confidence that leading internet brands have in our products and team—and we look forward to a successful partnership serving companies across Asia and China though a shared vision of excellence,” added Fowler.

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How AI and ML are changing insurance for good

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How AI and ML are changing insurance for good 3

By Alan O’Loughlin, Director of Analytics and Statistical Modelling, International and John Beal, Senior Vice President of Analytics at LexisNexis® Risk Solutions

The Insurance industry has been dealing with vast volumes of data for years, but analytics, Artificial Intelligence (AI) and Machine Learning (ML) techniques are increasingly being used to help insurance providers make faster data driven decisions.  Given the exponential level of data available today with AI/ML, insurance providers can now efficiently extract new insights into their customer’s needs and create stronger long-term value.

Personalising Insurance Pricing

Starting with how the market calculates premiums, the insurance sector now has access to thousands of data points to help them calculate premiums. Machine learning algorithms expedite the identification of the most predictive attributes driving claims losses – the most recent data points being historical cancellation data and gaps in cover.

This helps insurers become more competitive, match their risks to the most appropriate pricing strategies and write the risks that meet their underwriting appetite. In turn, customers get more personalised quotes based on their unique risk characteristics across any line of business

Achieving a single customer view

Personalisation within any sector works best of course when you really know who you are dealing with. Today, an explosive amount of data is collected, but it is vastly under-utilised as many organisations do not have the expertise to bring data together from different parts of the business to create a single customer view.  Add to this, the amount of mergers and acquisitions in the insurance market over the past few years and the challenge of managing multiple customer databases.  Linking and matching technology using policy history data to find common threads helps overcome this problem to create one consolidated view of the customer. Optimised matching algorithms are also the most accurate and relevant data is reviewed, reducing consumer friction during the quoting process.

Normalisation makes sense of masses of data

In the same vein, as organisations aggregate massive volumes of data, the value of cleansing and normalisation can’t be overlooked. One example, as usage-based insurance develops, whether through aftermarket telematics devices, smartphone apps, connected vehicles, even in the future from smart home data, all that data needs to be gathered, normalised, standardised. That way, any consumer can enjoy an improved shopping experience based on their needs and preferences, no matter the device brand and insurers have consistent quality standards and outcome decisions for all consumers.

Making Vehicle Data work for insurance

Data normalisation is already helping insurance providers understand the presence of Advanced Driver Assistance Systems (ADAS) on a vehicle at the quotation stage.  An ADAS classification system has been created using machine learning to scan millions of lines of car manufacturer vehicle data to logically sequence and classify vehicle safety features and component’s intended operation or purpose.  Extraction and proper classification of this type of data is extremely difficult, time consuming and error prone without the use of AI/ML

Thinking big, starting small in motor claims

At the claims stage in motor insurance, image recognition technology is being used to capture damage or invoices, run a system audit, and if the claim meets the approved criteria, it is automatically paid without human involvement.  This kind of virtual or ‘touchless’ claims handling is speeding up claim settlement times, cutting costs and improving the customer experience.  The ability to quickly analyse years of historical policy and quote history at the consumer level will add an additional level of security prior to a carrier releasing any claim payments.

Alan O’Loughlin

Alan O’Loughlin

Building context through AI and ML

Staying with motor insurance, telematics data can be used much more broadly than originally intended through AI and ML.  From the point of impact through to claim resolution, telematics data can allow insurance providers to get on the front foot at first notification of loss (FNOL), helping to deliver a better consumer experience post-accident, whilst providing invaluable insights regarding the circumstances of the collision.

AI/ML techniques communicate the conditions before, during and after the time of the accident.  Data points like air bag deployment impact sensor activation and g-force metrics can be analysed to understand claim severity and bodily injury potential.  In addition, by combining vehicle build data, carriers can understand the repair cost and potential impact to expensive ADAS features.  Insurance providers can instantly also help their customers with emergency services, vehicle rentals and repairs through instant analysis.

Taking the pain from home insurance applications

Moving into home insurance, we know that conversion rates of people shopping for home insurance is quite low due to a number of hard to answer questions along the customer journey. Rebuild costs is a classic example.  Prefill and data validation solutions are now helping to solve that problem but they are only possible through a huge amount of modelling, linking and AI-ML techniques to pull all the data together to return accurate and up-to-date information on the person and property.

Putting customers in the picture

AI is also at work in the commercial property insurance arena.  It can provide valuable insights regarding a potential location for a new branch or business relocation – footfall, crime rate, exposure to perils or other local circumstances that increase risk. This insight when provided to the customer enables them to take preventative measures if they do go ahead in that location, decreasing risk and loss costs, whilst helping to improve customer experience and retention.

AI and ML can help in the democratisation of data

Finally, AI and ML techniques are helping consumers take advantage of their individual data points which in turn provide the most accurate and updated view data to the insurance providers they choose to interact with on their own schedule.

A good example is the way driving behaviour data from aftermarket devices, or in the future, direct from the connected car gives a clearer picture of someone’s driving risk on the road.  Drivers then benefit from being judged based on their individual behaviours, rather than paying premiums based on average driving habits.

This requires transparency. Each time a consumer applies for insurance they consent to their data being used to provide the insurer with the best information possible, so they can set an appropriate premium based on the risk. Within insurance, we are focusing more than ever on educating consumers about how their data can be used and evaluated in a way they control and understand.  AI and ML automate and process the data consumers are happy to share – supporting greater choice, improved fairness and reduced friction with more personalised insurance protection.

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