Category: AI & MI

Why API Integration Is A Must For Digital Banking Growth In 2019

May 30, 2019

The banking industry is currently overwhelmed by technological disruptions and heightened customer expectations, with non-traditional players such as Facebook, PayPal, Google, and others quickly usurping roles previously played by banks. Non-traditional players have access to cutting-edge technology, which results in excellent user experience (UX) and innovative financial solutions. Customer expectations cannot be met by traditional banks which restrict themselves to digital solutions such as mobile apps or 24/7 customer service. However, banks can choose to be savvy and make the right choice of opening up their APIs to these third-party products and applications. According to one survey, 55% of financial institutions believe that API integration is critical to business strategy. Banks need to collaborate with newer and non-traditional players and open up their APIs in order to remain competitive and witness growth. API integration is an urgent need Behavioral changes and customer preferences have vastly changed over the years, with millennials and Generation Z expecting more from their banks than older users. Providing excellent customer service and a great mobile application are simply not enough anymore, because of the innovative disruptions initiated by non-traditional players. According to a report published by Intelligent Finance, Baby Boomers (or those born between 1946 and 1964) considered poor face-to-face customer service as a major determinant to exit a bank, while millennials revealed they would exit a bank not only if they disliked its smartphone app but also if it suffered from security breaches. Younger customers are also likely to quit a bank if they are unable to use their bank accounts on third-party applications and products. This is a gap that non-traditional players have capitalized on, and is an existential threat to traditional banks. People aged between 18 and 34 are two times likelier than older customers to use mobile payments and P2P lending products. In addition, the same demographic group prefers to receive constant updates via preferred channels such as text message, app notifications, etc. As Millennials grow older and more affluent, and as Generation Z takes the place of the millennials, the importance of digital banks providing a holistic financial ecosystem consisting of third party products and services used by customers become more apparent. Here are some successful examples of API integration: People with financial difficulties in the USA have started to use P2P lending tools such as Earnin and PayActive. It is now possible to consolidate debt too, thanks to debt aggregators. Marcus from Goldman Sachs and SoFi are often cited as examples for non-traditional lenders. Often, these tools are integrated with e-commerce sites or food delivery apps so that people can purchase what they need on credit, bypassing banking lending rules. Credit unions are a non-traditional alternative to bank loans. Walmart MoneyCenters are extremely popular today because they offer a borrowing alternative to people with poor credit histories. If banks integrate their data with these products, customers can continue to make payments for P2P loans without canceling their accounts. One of the best examples of API integration is when PayPal decided to integrate its API with Siri. iPhone users can send and receive money via PayPal by speaking to Siri. Wave is an invoicing and accounting software used by businesses and individuals. Wave uses banking APIs to help users control all their business finances in a single place. It collects as much data as possible from various sources and even markets loans provided by OnDeck on its platform to eligible users. Larger banks have started to offer data aggregation services to their customers. For instance, HSBC recently launched its Connected Money app, on which customers can view their account details in 21 other banks without ever leaving HSBC’s application. Facebook Messenger payments allow Facebook users to transfer money to their friends without ever having to leave the network. Facebook currently has integrated the APIs of PayPal, Stripe, Visa, MasterCard, American Express and others. If traditional banks do not understand the metamorphosis that has already taken place, they stand to lose more of their existing and future customers to non-traditional players. Specific reasons for API integration In order to survive technological disruption, banks need to engage in business model reinvention, which includes open banking and partnering with the newer third-party apps and products. While the producer market consists of banks and other financial enterprises that create products and services, customers can access these products and services on third-party applications, websites and or use voice control. Capitalizing on these distribution channels by opening up APIs is very important for banks survival. E-commerce and on-demand services such as Uber and Airbnb have spurred a customer-centric demand for always-on banking Internet of Things (IoT) enabled devices have led to a growing need for smart solutions and banking services available on intelligent devices Omni-channel banking experience requires data exchange between apps, and customers take this facility for granted now What kind of apps need integration? New services and applications that need API integrations with banking applications include: Payments, clearing and settlement services Mobile and web-based payment applications Digital currencies (DCs), Blockchain and Distributed Ledgers Deposits, lending, and capital raising services Crowdfunding Market provisioning services such as smart contracts and e-aggregators Emerging technologies such as Big Data, cloud computing, Artificial Intelligence (AI) and robotics (Robo-advice) Electronic trading and insurance API Integration can prove to be challenging If you thought your in-house developers can release an API along with the application, you will be disappointed to learn that in 2019, it is a very complex situation. Developing an integration workflow consumes the most amount of time during API Integration, and requires special skills. Event Driven Integration is far more complex than simple API integration, as it needs to provide real-time status updates to customers. Real-time status updates are crucial in today’s financial market. APIs are not always uniform and there are no industry standards at the moment. 70% of the developers work with REST APIs, which makes it a wise choice for API Integration. However, REST APIs will not work for all kinds of applications and

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How To Enrich Customer Experience Using AI?

January 4, 2019

Artificial Intelligence has become a buzzword over the last two years and there has been good development around it as well. However, customer experience still hasn’t seen any drastic improvement. The reason often stated is that automation lacks creating an atmosphere of emotional engagement which is a must for positive customer experience. This is where artificial intelligence is going to come to our rescue. AI-enabled technologies can help us understand customers at a deeper level, and predict what they really want, helping us craft campaigns and product experiences that lead to better customer experiences. Let us take a look at how you can start using artificial intelligence quickly and easily, in order to enrich the customer experience. Implement AI-enhanced customer service Artificial intelligence-enabled tools process gigantic amounts of data to quickly understand the situation and history of a customer, helping customer service agents to respond accurately. In addition, customer service enhances customer experience only when it is available 24/7, on all channels. Only artificial intelligence can make that possible for you in a cost-effective manner via chatbots and virtual assistants. Here are some specific uses of implementing AI-enhanced customer service: Chatbots can respond to queries on a number of channels 24/7 Virtual assistants can provide human-like customer service via text and voice AI-enabled tools can assist customer service agents to ingest a customer’s entire history while answering queries Identify customer emotions and provide context-appropriate customer service Use case: CIMB Bank now offers 24/7 customer service by integrating EVA, a Chabot-enabled mobile app. Bank customers can perform a number of activities such as transferring money. paying bills, checking balance, etc. The bank could reduce its dependence on human customer service agents, and also provide 24/7 customer service. Point to note: AI can be used independently if you cannot afford an in-house customer service team. Consider AI-assisted after sales support From Internet of Things to predictive analysis, after-sales support is rapidly changing in recent years. Thanks to artificial intelligence and the many technologies that fall under this bracket, implementing after-sales support is easier than ever. Internet of Things-enabled devices can help provide predictive product support, thereby enhancing customer experiences. Here are a few situations when you can use IoT-enabled after-sales support: Identify when parts are malfunctioning and send technicians immediately Observe downtime and uptime of products and make product enhancements Gather data related to product usage Make improvements to your product and enhance customer experience Use case: Syncron Uptime is an IoT-enabled product by Syncron, which helps manufacturers to provide after-sales support. The technology helps identify when a product will require replacement or repair, by tracking equipment in real time. It uses sensor data to identify malfunctioning and other anomalies, and help manufacturers respond before the customer has knowledge of something going wrong. Point to note: If you provide services rather than products, you can use textual analytics to process natural language on social media or email to identify what your target audience wants. Invest in intelligent data analytics While older data analytics tools only processed data and generated reports, AI-enabled tools can unify various data sources and bring real-time insights to you. Most importantly, AI helps you put insight into its business context. Text analytics solutions are increasingly being used to identify patterns and predict outcomes and take action when required. By using an AI-enabled data analytics tool, you can: Build a dynamic customer profile that recognizes individual interests, previous communication, loyalty, etc. Offer predictive personalization to your customers for enhanced satisfaction Unify data related to customer journeys and generate insight that is more predictive in nature Identify when customer churn might occur based on text communications Use case: Nordea is a Swedish bank that uses an AI-based text analytics solution. The tool analyses hundreds of inbound customer communications every second and processes them intelligently. Each communication is forwarded to the right business unit, eliminating customer frustration. The tool can also be used to recognize customer churn and eliminate it. Point to note: Customer behaviours are chaotic and their interaction datasets are messy. Data insights can bring discipline into an otherwise undefined and unchartered territory. Understand your customers at a deeper level Affective computing, a branch of artificial intelligence that recognizes people’s cognitive and emotional states, is expected to grow to a $41 billion industry by 2022. Apple, Facebook, Google, and other companies are currently working with affective computing specialists such as Beyondverbal, Affective and Sensay to bring facial analysis, emotion recognition, voice pattern analysis, and other humanizing technologies to software programs that run products and services. Here is how you can implement effective computing in the coming months: Identify customer moods on social media by using textual analysis Provide customer support by recognizing negative emotional states such as frustration or anger Use conversational IVRs to recognise frustration during telephone calls Chatbots can recognize emotions and respond appropriately by using neuro-linguistic programming Use Case: Ford is working with Affectiva to bring AutoEmotive, an Automative AI, to its cars in order to prevent accidents and incidents of road rage. This emotion-recognition software uses AI to identify human psychological conditions such as lack of attention, rage, anxiety, etc. in order to take control over the vehicle or just stop it from moving. Point to note: Implement a solid privacy and consent policy to keep yourself safe from litigations, and protect your customers as well. Focus on the humanization of customer interactions A monogamous relationship with AI may spell doom to your customer experience strategy. Nearly 50% of those interviewed in a survey expressed that they would prefer if AI-enabled interactions were more human-like. While AI has obvious benefits of cost reduction, efficiency in customer service, and access to valuable insight, it may reduce customer experience unless you humanize it as well. Here are a few tips to humanize an AI-enabled customer support strategy Implement natural language processing and human voice to bring a “real human” experience Use AI-enabled data density to provide personalized and individual attention to customers Implement real-time support with

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How To Enrich Customer Experience Using AI In 2019?

January 4, 2019

Artificial Intelligence has become a buzzword over the last two years and there has been good development around it as well. However, customer experience still hasn’t seen any drastic improvement. The reason often stated is that automation lacks creating an atmosphere of emotional engagement which is a must for positive customer experience. This is where artificial intelligence is going to come to our rescue. AI-enabled technologies can help us understand customers at a deeper level, and predict what they really want, helping us craft campaigns and product experiences that lead to better customer experiences. Let us take a look at how you can start using artificial intelligence quickly and easily, in order to enrich the customer experience. Implement AI-enhanced customer service Artificial intelligence-enabled tools process gigantic amounts of data to quickly understand the situation and history of a customer, helping customer service agents to respond accurately. In addition, customer service enhances customer experience only when it is available 24/7, on all channels. Only artificial intelligence can make that possible for you in a cost-effective manner via chatbots and virtual assistants. Here are some specific uses of implementing AI-enhanced customer service: Chatbots can respond to queries on a number of channels 24/7 Virtual assistants can provide human-like customer service via text and voice AI-enabled tools can assist customer service agents to ingest a customer’s entire history while answering queries Identify customer emotions and provide context-appropriate customer service Use case: CIMB Bank now offers 24/7 customer service by integrating EVA, a Chabot-enabled mobile app. Bank customers can perform a number of activities such as transferring money. paying bills, checking balance, etc. The bank could reduce its dependence on human customer service agents, and also provide 24/7 customer service. Point to note: AI can be used independently if you cannot afford an in-house customer service team. Consider AI-assisted after sales support From Internet of Things to predictive analysis, after-sales support is rapidly changing in recent years. Thanks to artificial intelligence and the many technologies that fall under this bracket, implementing after-sales support is easier than ever. Internet of Things-enabled devices can help provide predictive product support, thereby enhancing customer experiences. Here are a few situations when you can use IoT-enabled after-sales support: Identify when parts are malfunctioning and send technicians immediately Observe downtime and uptime of products and make product enhancements Gather data related to product usage Make improvements to your product and enhance customer experience Use case: Syncron Uptime is an IoT-enabled product by Syncron, which helps manufacturers to provide after-sales support. The technology helps identify when a product will require replacement or repair, by tracking equipment in real time. It uses sensor data to identify malfunctioning and other anomalies, and help manufacturers respond before the customer has knowledge of something going wrong. Point to note: If you provide services rather than products, you can use textual analytics to process natural language on social media or email to identify what your target audience wants. Invest in intelligent data analytics While older data analytics tools only processed data and generated reports, AI-enabled tools can unify various data sources and bring real-time insights to you. Most importantly, AI helps you put insight into its business context. Text analytics solutions are increasingly being used to identify patterns and predict outcomes and take action when required. By using an AI-enabled data analytics tool, you can: Build a dynamic customer profile that recognizes individual interests, previous communication, loyalty, etc. Offer predictive personalization to your customers for enhanced satisfaction Unify data related to customer journeys and generate insight that is more predictive in nature Identify when customer churn might occur based on text communications Use case: Nordea is a Swedish bank that uses an AI-based text analytics solution. The tool analyses hundreds of inbound customer communications every second and processes them intelligently. Each communication is forwarded to the right business unit, eliminating customer frustration. The tool can also be used to recognize customer churn and eliminate it. Point to note: Customer behaviors are chaotic and their interaction datasets are messy. Data insights can bring discipline into an otherwise undefined and unchartered territory. Understand your customers at a deeper level Affective computing, a branch of artificial intelligence that recognizes people’s cognitive and emotional states, is expected to grow to a $41 billion industry by 2022. Apple, Facebook, Google, and other companies are currently working with affective computing specialists such as Beyondverbal, Affective and Sensay to bring facial analysis, emotion recognition, voice pattern analysis, and other humanizing technologies to software programs that run products and services. Here is how you can implement effective computing in the coming months: Identify customer moods on social media by using textual analysis Provide customer support by recognizing negative emotional states such as frustration or anger Use conversational IVRs to recognise frustration during telephone calls Chatbots can recognize emotions and respond appropriately by using neuro-linguistic programming Use Case: Ford is working with Affectiva to bring AutoEmotive, an Automative AI, to its cars in order to prevent accidents and incidents of road rage. This emotion-recognition software uses AI to identify human psychological conditions such as lack of attention, rage, anxiety, etc. in order to take control over the vehicle or just stop it from moving. Point to note: Implement a solid privacy and consent policy to keep yourself safe from litigations, and protect your customers as well. Focus on the humanization of customer interactions A monogamous relationship with AI may spell doom to your customer experience strategy. Nearly 50% of those interviewed in a survey expressed that they would prefer if AI-enabled interactions were more human-like. While AI has obvious benefits of cost reduction, efficiency in customer service, and access to valuable insight, it may reduce customer experience unless you humanize it as well. Here are a few tips to humanize an AI-enabled customer support strategy Implement natural language processing and human voice to bring a “real human” experience Use AI-enabled data density to provide personalized and individual attention to customers Implement real-time support with

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Implementing Machine Learning Strategies for Business Success

October 10, 2018

With each passing day, machine learning’s business implications are becoming clearer. Machine learning is a branch of artificial learning in which systems identify patterns from data, learn from insights, and make autonomous decisions with very little human intervention. As the number of smart devices connected to internet increase, so will the data generated by them. This deluge of data is also known as Big Data, and machine learning applies complex algorithms to understand patterns in Big Data to make decisions. Machine learning can provide real-time insights based on data, giving businesses a competitive edge over their peers. In this article, let us take a look at how machine learning is going to influence businesses across the spectrum. Where is machine learning used? Currently, machine learning is being used across industry verticals for business success. Here are some examples: In the media, machine learning is used to personalize content and to make recommendations, predict paywall price, and to optimize layouts. In marketing, data insights can be used to make upsell forecasts and churn predictions, while it can also help in lead scoring. Machine learning also assists in making KPI predictions such as CLV (customer lifetime value). The eCommerce industry has begun to use machine learning to promote products in a targeted manner. The retail industry, on the other hand, uses machine learning to make predictions related to inventory and store layout. Financial services use machine learning to predict churn rate and to reduce it. It is also used to predict loan outcomes and identify risky customer behavior patterns. Three scenarios in which you can implement machine learning immediately Make better sales forecasts, improve marketing campaigns, and enhance customer satisfaction You can start using machine learning to consume and analyze data from unlimited sources. You can also rapidly process analyses and make predictions related to sales and marketing campaigns. In addition, you can use machine learning tools to evaluate past behaviors of customers. According to Forbes, 84% of marketing organizations currently use some form of machine learning or AI to enhance their services. Use cases Example 1: Azure Machine Learning can be used to analyze customer churn and minimize it as well. This is more cost-effective than other traditional and time-consuming methods to minimize customer churn. Interactive Pricing Analytics Pre-Configured Solution (PCS) is a Microsoft Azure machine learning solution that helps to determine the pricing elasticity of every product that you may sell. In other words, this tool can be used to offer contextually relevant pricing. Example 2: Salesforce Einstein is a great example of what machine learning and AI can do to enhance existing CRM solutions. Salesforce Einstein can be used to implement predictive lead scoring, and the tool looks at various demographic and behavioral data sets. It can also help recommend products to your customers based on their interests, and to cross-sell and up-sell products more effectively. Offer predictive maintenance and avoid downtime Most businesses rely on corrective maintenance to fix machines and applications. Corrective maintenance requires one to wait until an issue arises, but the costs in downtime, unscheduled maintenance requirements, and labor can increase the overall expenditure exponentially. Some businesses have begun to use preventive maintenance, which urges customers (and their own staff) to replace spare parts regularly or to ensure certain security and upgrading protocols for software tools. Even scheduled downtime and under-utilization of spares before their full lifetime can result in unnecessary losses. Machine learning helps businesses to undertake predictive maintenance at the right time, whether onsite or for customers. It is the smartest way to ensure that equipment and systems are used to their full lifetime and that problems are identified before they cause issues. You can implement predictive maintenance to reduce over-corrective maintenance, scheduled downtime, and labor costs by analyzing user data and identifying when interventions need to be taken. Specific benefits include: Detecting anomalies in system performance or in equipment Predict when an asset may fail Estimate how long an asset may remain useful Recognize the reasons for an asset’s failure Recognize what steps need to be taken to offer maintenance support to Azure Machine Learning and Microsoft Azure AI platform can help in the predictive maintenance of both onsite infrastructures and provide support for customers. Detect fraud and enhance security An important function of machine learning in businesses is to detect fraud and enhance security. Machine learning technology can be used to manage portfolios, engage in algorithmic trading, underwrite loans, and detect financial fraud. Here are a few ways you can implement machine learning to enhance security: eCommerce websites can make use of machine learning to prevent credit card fraud. Create real-time behavioral profiles that interpret the actions of customers, merchants, individuals, and other entities. Supervised machine learning that uses algorithms to detect fraud after having “learned” from innumerable examples of fraudulent and legitimate transactions. Supervised machine learning can only detect fraudulent activity that has taken place previously, and thus, unsupervised machine learning is the next step. This self-learning algorithm predicts fraud and by detecting outlier behavior and transactions. Adaptive analytics helps machine learning models to continuously learn from feedback. These models can be used to detect spam and thwart IT security threats as well. For example, PayPal uses an open-source based homegrown AI and ML engine to detect fraud. After implementing this model, PayPal reduced fraud by 50%. Implementing machine learning in your business Before you implement a machine learning model, follow these steps for a customized solution: Recognize the problems which machine learning will solve Identify the data sets that will help the machine learning model to solve a problem Determine which machine learning platform you will use to build your custom model Consult a data engineer or determine yourself how you will stream data into the machine learning platform Build or choose the right machine learning model to address your issues Continuously test and adjust the model Machine learning does something for every business With proper planning, you can implement machine learning to enhance sales and marketing campaigns, make

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What We Learnt From The AI Summit, London

June 15, 2018

The world’s largest AI event for business ‘The AI Summit’ took place in London from 12-14 June. Artificial Intelligence or AI is a big thing at the moment and this event showed what kind of impact AI is bringing forth in the business world. The event was supported by industry stalwarts like AWS, IBM Watson, Microsoft, Cisco, Oracle, Google, HCL, PwC, Intel, Genpact among others. Here are some updates from the event for your perusal: ‘This is a technology that changes everything, and we are living through it’. Great to hear Secretary of State for business and industry, Greg Clarke, set out the significance of AI and its role in transforming businesses and skills. Exciting times for@IBMWatson #AISummit #ai pic.twitter.com/0QWw8zgIHD — Paul Ryan (@pauldtryan) June 13, 2018 BBC team used machine learning to train a model to recognise credits across thousands of TV Programmes so they could recommend the next show to watch. – Ali Shah #AISummit @Business_AI #AI @BBC @BBCiPlayer #MachineLearning pic.twitter.com/fZCQYs6gCu — Darryll Colthrust (@dcolthrust) June 13, 2018 #ArtificialIntelligence isn't something that's on the horizon! People are, right now, using #AI solutions which are opening doors to a new wave of possibilities. #Accessibility #LTW #London #Tech #AISummit #LondonTechWeek @MSFTBusinessUK @MSNewEngland pic.twitter.com/iW9HQIY9jz — Evan Kirstel #B2B #TechFluencer (@EvanKirstel) June 13, 2018 "It's very very important that the data we are using takes place based on an incredibly transparent basis and is largely anonymised. For us it is a commercial, ethical, and moral consideration." Richard Potter of @MicrosoftUK speaking on the finance #AI panel #AISummit #fintech pic.twitter.com/jkta2MUJqx — AI Business (@Business_AI) June 13, 2018 #AI offers truly transformational opportunities for business & society. But can you explain how some of the most complex algorithms are making their decisions? We’re discussing #ExplainableAI live on stage at the #AIsummit now, paper available here https://t.co/B3lkswMhOg #LTW — Felicity Main (@felicitymain) June 13, 2018 Christina San Jose, group chief data strategist @bancosantander says 80% of her teams’ time & resources are spent on data normalisation because data was originally intended for simple reporting or a byproduct or a back office app, not for #datascience purposes. #AISummit #AI — Nick Patience (@nickpatience) June 14, 2018 Fascinating insight on the progress of #Santander’s #machinelearning activities. Here are the bank’s key customer-facing use cases for #AI & what the model deployments look like internally. They also regularly test their algorithms via sponsored competitions on @kaggle #AISummit pic.twitter.com/UOgA4X5TAw — Nicholas McQuire (@nickmcquire) June 14, 2018 Keep watching this space! We regularly publish similar blogs with current happenings from around the globe in the world of Tech and Digital.

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Disruptive Innovation in Payments

June 1, 2018

We could have written paeans about FinTech a couple of years ago, but to do so now would be to sing praises about what is already the norm. FinTech, the inevitable result of finance services making use of technology to enhance solutions and services, is the single largest disruptor in the world of finance. In addition to being a disruptive development, FinTech has evolved into becoming almost conventional, replacing legacy methods and making them seem archaic. Yet, a closer observation reveals that there is a lot of disruption taking place even within contemporary FinTech. Technology has driven FinTech to continuously evolve itself, and newer players continue to give a run for their competitors’ money almost every day. In this article, we briefly recap the growth of FinTech and contextually place this growth in a situation that continues to bring disruptive innovation in payments. We shall also take a look at certain trends driving this disruption, and what we can expect from this exciting development in the near future. The growth of FinTech While it may sound like a fancy term, FinTech isn’t actually very new. Using technology to drive financial procedures has been around since the 1900s in various forms. From being able to wire money to someone in a different part of the world to modern peer-to-peer payments, FinTech has come a long way. Barclays opened the first ATM in the world in 1967, and Wells Fargo kick-started the world’s first online checking account in 1995. PayPal came to being in 1998, and online transactions and payments have grown exponentially ever since. Apple Pay, which was announced in 2016, was another watershed moment, as it heralded a new smartphone-based payments solution. Finally, blockchain-based payment technology gave rise not only to cryptocurrency, but also to smarter payments, seamless insurance claims processing, loan disbursals, and contactless payments. We had recently written an article about how Blockchain is bringing winds of change to the insurance sector. Drivers of the change As we can see, FinTech has evolved dramatically in the last few years due to rapidly evolving technology. However, market behavior and changes within finance sector have also been major drivers of change. In this section, let us take a look at three aspects of this disruptive innovation in payments. Technological development It goes without saying that technology is a big reason for disruptive changes in FinTech. In particular, artificial intelligence and blockchain have caused tremendous changes in the finance sector, propelling drastic changes that have taken even FinTech players by surprise. For example, PayPal and other peer-to-peer payments solutions were taken by surprise when cryptocurrency came to being. Blockchain in fintech is the single-most disruptive situation at the moment. Cryptocurrency players like Bitcoin etc. were taken by surprise when Ethereum-based legal peer-to-peer payments application started to be launched. For instance, blockchain-based claims processing, stock purchase, and Ethereum-based P2P payments solutions are quickly taking over traditional smartphone-based payments applications. Smart contracts have enabled seamless and secure transactions, making financial procedures more reliable than they ever were. In fact, it wouldn’t be an exaggeration to call blockchain a cultural phenomenon. For an industry that focuses much of its energy on building and maintaining trust, blockchain and smart contracts-enabled applications are almost a godsend. It wouldn’t be an exaggeration to state that technology itself is driving change and causing more disruptive innovation in the field of FinTech. Those who aren’t part of this exciting evolution will quickly be left behind. In-store mobile payments are touted to exceed $503 billion by 2020. Just in the US, a whopping 150 million people are expected to use in-store mobile payments. The spending ability of mobile payments users is very high. They spend twice as much as non-users do, and earn at least $70,000 a year. Security-related doubts have been a hurdle for mobile payments adoption. 47% of cybersecurity professionals felt mobile payments weren’t secure enough at the moment, as opposed to just 23% feeling confident. Public Wi-Fi is the greatest vulnerability with respect to mobile payments, with a threat figure of 26%. This is closely followed by stolen devices, a situation whose threat figure is 21%. Market trends Increasingly, users have come to expect a lot more than what technology can offer at the moment. We can describe this as a market that’s so spoiled by choices that even the most disruptive technology no longer feels like disruption. Consumer behavior has shifted from being awestruck by disruption to expecting innovation by default. In other words, services that do not seem innovative enough for consumers simply get ignored. This has forced most industry players to closely study consumer behavior and surpass their expectations. This isn’t usually possible because users have come to expect a lot more than what technology realistically allows us to do. Yet, FinTech companies and solutions providers have to work harder to keep pace with market expectations ad and focus on driving change. Adopting innovation and complex technologies such as artificial intelligence, data analytics, and blockchain will help FinTech companies to surpass market expectations and bring value to the services they launch. Data analytics, in particular, can help FinTech entities to study consumer behavior closely and launch FinTech products that match market expectations. Industry changes There are a number of changes within the industry that are propelling disruptive changes within FinTech sector. Banks are desperate to retain their roles in the finance space, and payments intermediaries may simply vanish, because of smart contracts and distributed ledger technology. The same distributed ledger technology is helping FinTech organizations to make cross-border payments instantaneous, giving rise to new corporate and consumer solutions that will enable instant international payments a reality. Fintech companies have also begun to make use of open APIs, machine learning, and robotic process automation to enhance the experience. Most importantly, a lot of FinTech activity currently is focused on thwarting cyber-attacks, ensuring data privacy and safety, secure financial transactions, and eliminating payment frauds. Blockchain, smart contracts, artificial intelligence and machine learning are currently top

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Digital Disruption In Insurance: The Rise Of InsurTech

May 21, 2018

In the last few years, #InsurTech has increasingly proved to be a disruptive influence in the insurance sector, an industry which can be considered as one of the most complex in the world. #InsurTech with the help of technology and innovation has managed to immensely improve the efficiency of the existing operations, offering digital first customer-led services and enhanced customer experience. I had a very deep discussion with Dennis Grönger, InsurTech Professional, Author and Speaker at NextTo InsurTech, on the rise of #InsurTech and Digital-Led Product Innovation in Insurance. The detailed version of the conversation is given below. Every week we publish insights with a Q&A with CIOs, CTOs CMOs, and CXOs. See the link to the previous LinkedIn Q&As by Indus Net Technologies at the bottom of this post. Q1. What are the key trends in product innovation in the Insurance industry? Dennis: I am an InsurTech expert and this sector is full of surprising new ideas and concepts. Every conference I visit is full of exciting start-ups. And it’s the same on the incumbent’s side. Product innovation is a key component of Digital Transformation in the insurance industry. Despite regional characteristics, two general trends can be observed worldwide. First, there has been a shift from one-size-fits-all products to fine-grained components that can be combined individually for the customer. Second, more and more insurance services and products are being developed that add special value and benefits for using customer data. Q2. What are the key enablers and drivers of innovation in the insurance industry? Why NOW? Dennis: Two big changes have been essential for today’s innovative insurance industry to develop: a technological change and a cultural change. I wouldn’t dare to tell you guys at Indus Net Technologies about technological change as you’re much better than me in this area! As for the cultural change, I’m not aware of any insurance company that hasn’t radically changed how it uses the creativity and great ideas of its employees. When I started my career in insurances business, the whole industry was full of patriarchs at the top of companies and employees were just considered numbers on payrolls. Since then, things have fortunately changed, and many successful innovations would be inconceivable without committed employees. Q3. What are some of the interesting digital-led product innovations in the Insurance industry? Dennis: The time of new digital insurance products has just begun, and I am convinced that we are going to see a lot of exciting new and innovative ideas. For example, there is a new class of insurance products that wouldn’t work without full digital capabilities and niche products with low premiums. The combination of ‘niche‘ and ‘low premiums’ was out of the question for incumbents until now; Digital Transformation has changed that. Cyber Insurance is another good example of a different digital-led product trend: products as a combination of services that extend beyond coverage. In case of a cyber-attack, the most important thing is to find the best specialist to stop the attack as quickly as possible. How would this be solved without a digital platform that connects your customers with specialized service providers? It would be impossible! Q4. Why is user experience leading the way in Insurance innovation? What problem are we solving here? Dennis: Relevance and simplification are key terms in the case of user experience in the insurance industry. Customers want more personalized services and products that are individually tailored to their personal needs. Product relevance is also a question of when and how the customer wants to handle this product, before and after buying it. Insurers need to find answers to these customer demands. However, without simplified products that your customers can easily understand, the only user experience that you are going to get is bad user experience. Q5. How much of IoT and Big Data Analytics is being used to create new products? Has the IoT generated data attained statistical significance to be used for underwriting? Dennis: Well, I am a strategy expert and not an underwriter but there is no doubt that IoT and Big Data Analytics are going to disrupt the ways that underwriters analyze and model risks. Connected cars have already reinvented car insurance and, with Smart Home technologies in a bundle with home insurances, for example, insurers have the chance to offer real protection in addition to coverage. Q6. How can blockchain be used for disruption in the Insurance industry? Dennis: That’s the billion-dollar question right now, isn’t it? I think it’s still too early to make any reliable predictions about blockchain. However, for me, the biggest opportunities for insurance companies are in reducing costs, reducing errors, and reducing time by using blockchain-based technologies. Q7. Do you see a future for people-to-people (p2p) Insurance? How far (or near) is this from reality? Dennis: Great examples of P2P insurance, like Lemonade, have proven how far you can get with complete customer focus. But is a P2P business model profitable or even scalable? I don’t think so. The numbers from Lemonade that I’ve seen so far are reporting huge losses and Germany’s P2P pioneer Friendsurance was, due to its numbers, forced to switch their business model and have become more of an online broker with a few P2P-benefits for their customers. Q8. What are the constraints around innovation in the Insurance industry? Dennis: I want to answer this by quoting a friend of mine, Dr. Robin Kierra. “Insurers needs to do everything at once: do their homework, go out and play, and prepare for the exams in 10 years.” Would you like to try that with the 25+ year old legacy systems that most insurers are still using? Better not, but it is a fact that Digital Transformation and innovation are still at their beginnings in the insurance industry. Q9. How can Insurance and InsurTech collaborate to breed innovation at scale? Dennis: As long as insurers have the customers and InsurTechs have the technology, there is no other option than to cooperate with each other.

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#HealthTech – Has It Emerged Finally?

May 15, 2018

The emergence of the digital revolution comes at a time when healthcare is in the utmost need to respond to the ever-growing demand for resources. #HealthTech is required not only to come up with solutions but to deliver them as well. Let us see what is happening in the #HealthTech sector around the world. Top 3 technologies that can change the #healthcare industry#AI #healthtech #ehealth #tech #medicaltech #sensors #technology #IoT #Industry40 @rajat_shrimal cc @evankirstel @MikeQuindazzi @debashis_dutta @kuriharan @AnjaHoffmann @chboursin @jblefevre60pic.twitter.com/1Mq62vpXQk — Rajat Shrimal (@rajat_shrimal) May 13, 2018 10 promising #AI applications in #healthcare >> @HarvardBiz HT @psb_dc via @MikeQuindazzi >> #HealthTech #BigData #DataScience #AI #MachineLearning #PredictiveAnalytics #DeepLearning >> https://t.co/JCLs4Pv9iz pic.twitter.com/NsoRtiTLaa — Mike Quindazzi (@MikeQuindazzi) May 14, 2018 https://twitter.com/ai_amelia_/status/996215570931638277 Managing Medical Data on The #Blockchain [Infographic]https://t.co/BN0lJxxbIy [by @gemhq]#BigData #HealthTechCc @pierrepinna @SpirosMargaris @andi_staub @JohnNosta @JimMarous @evankirstel @ahier @IrmaRaste @MarshaCollier pic.twitter.com/r2xhdKs8pO — ipfconline (@ipfconline1) May 14, 2018 #ArtificialIntelligence #Cancer #Healthtech This #pen may let surgeons detect cancer in seconds @CNBC@TamaraMcCleary @SpirosMargaris @psb_dc @FrRonconi @fabiomoioli @DanielDiemers @HealthcareWen @KirkDBorne @JohnSnowai @AntonioSelas @ndwr @NeiraOscipic.twitter.com/uTfIe5ABLW — Andy Müller (@andy_lucerne) May 14, 2018 This 3D printed prosthetic arm is sleek in design and feels intuitive. #3dprinting #prosthetics #engineering #healthtech pic.twitter.com/vlI5cVRgdE — Evan Kirstel #B2B #TechFluencer (@EvanKirstel) May 10, 2018 How #Blockchain Will Transform Healthcare Information Managementhttps://t.co/JDKe6DEBA4 [by @TD_Madison v/ @Datafloq]#HealthTech #IoT #AR #VR #Wearables #SupplyChain Cc @ahier @evankirstel @DeepLearn007 @MarshaCollier @jblefevre60 @Bill_IoT @andi_staub @psb_dc @IrmaRaste pic.twitter.com/bXuNK7aOXk — ipfconline (@ipfconline1) May 12, 2018 Doctors are using #AI to see how diseases change our cells https://t.co/NksYtfJBFx @wef #ML #3D #health #HealthTech@evankirstel @ipfconline1 @SpirosMargaris @JacBurns_Comext @Fisher85M @helene_wpli @ImMBM @chboursin @mallys_ @jerome_joffre @psb_dc @ahier @JohnNosta @HITpol pic.twitter.com/UZD9hAia8i — Jean-Baptiste Lefevre (@jblefevre60) May 11, 2018 This Portable #3DPrinter Could Print Skin Over Wounds https://t.co/2yYckJfVX9 #healthtech pic.twitter.com/Ypr9fFhXJ2 — Evan Kirstel #B2B #TechFluencer (@EvanKirstel) May 10, 2018 This is a young an growing sector. However, the ever-evolving #HealthTech will surely contribute a lot to the welfare of the society.  

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Google IO – Here Are All The Updates You Need

May 11, 2018

Do we need to tell you what Google IO is? A developer festival this big and probably the biggest in the world needs no introduction. This year’s Google IO was held 8-10 May at Mountain View, California. Thousands of developers and tech geeks from around the globe got together to learn about latest developments and products from Google. We came across some path-breaking updates which made everyone burst with excitement. If you weren’t able to follow the event, no worries, here are the updates for you. That's a wrap from #GoogleIO! Here's everything Google unveiled at its biggest conference of the year https://t.co/9XXZqM0dwU — Business Insider (@BusinessInsider) May 8, 2018 Google is taking its predictive text features to the next level, this is Smart Compose, which autocomplete was your sentences as you type in Gmail #GoogleIO pic.twitter.com/6YjPsIPew6 — Karissa Bell (@karissabe) May 8, 2018 Google Photos getting new AI actions. Will offer one tap retouch options, convert documents into PDFs and turn old black and white photos into color. #GoogleIO18 pic.twitter.com/ASQwlHSaL6 — Rich DeMuro (@richontech) May 8, 2018 https://twitter.com/thekenyeung/status/993900314154565632 VERY deep dive into the new News app. Which is another way to underscore the theme "You can trust us. We promise that if we're hauled before Congress, it'll be only over antitrust stuff." #googleio18 — Andy Ihnatko (@Ihnatko) May 8, 2018 "UX isn't fonts or shades of blue, it's moments for your users"Awesome talk about making better experiences with @_davideast#io18 #googleio18 #Firebase pic.twitter.com/wq7z6Ug1B8 — Derek Pastor (@DerekJonPastor) May 10, 2018 6 new voices are added to the google assistant with the goal to get the right accents and dialects correct and they are featuring the sexy cool John Legend…oh yeah!!! #GoogleIO #googleio18 — Lynette Anthony Hundermark (@LynetteAnthony) May 8, 2018 Miss Google's #io18 keynote yesterday? We have you covered 💪 And it's all under 5 minutes ⏰ pic.twitter.com/lfdxLxxLlz — CNET (@CNET) May 9, 2018 The web is so much more powerful today, developers need to catch up! #io18 pic.twitter.com/IH49Wlyejp — Ire Aderinokun (@ireaderinokun) May 9, 2018 Watch this brilliant demonstration of @google's AI-powered personal assistant have a "human-like" conversation with a real person, via @sundarpichai #io18 pic.twitter.com/tXykdPccbH — Vala Afshar (@ValaAfshar) May 9, 2018 Updated: What’s New in Android? #io18 #sketchnote pic.twitter.com/CoxiJev2CH — Corey Leigh Latislaw 💄 (@corey_latislaw) May 9, 2018 One of the new features I'm so excited about at #io18 this year – Motion Layout a subclass of ConstraintLayout allowing for easy animations 🤩😍 pic.twitter.com/8LpQiNbIHM — Rebecca Franks (@riggaroo) May 10, 2018 What do you think about the latest developments on ‘Google Assistant’? Is it creepy that it would now be able to make calls for you or is it absolutely fantastic and a whole new level of evolution in the world of technology? Let us know your thoughts.  

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Next-level Data Analytics and AI to Unlock IoT’s True Potential

May 7, 2018

To imagine that physical objects could be connected to the Internet and that they could communicate with each other at the same time seemed ludicrous even a decade ago to most people. However, Internet of Things (IoT) is real, and it is here. This system of connected devices connected to the Internet via sensors and churn out a large amount of data. IoT-enabled devices are no longer mysterious. They are used in the health sector, smart homes, vehicles, gadgets, and just about everywhere else. It shouldn’t come to us as a surprise that all these devices result in large volumes of data, adding to what is often called the Big Data. While this data may seem insurmountable, and sometimes even redundant, artificial intelligence and data analytics can empower users, manufacturers, and businesses to venture into unchartered territories. In this article, let us take a look at how artificial intelligence and next-level data analytics are helping to unlock the potential of IoT. What’s the problem with IoT today? While most devices manufactured today have some level of IoT enabled in them, there is a lacuna of sorts, when it comes to the actual utilization of the technology. This is because, at its simplest, IoT connects devices using sensors and Internet, but there is no way to really understand the data that is produced or to enhance features that already exist. While IoT has helped millions of people to communicate with their cars, homes, machines in factories, etc right from the smartphones, a lot of data that is churned out by these IoT-enabled devices is simply ignored. Artificial intelligence and advanced data analytics can help to put IoT data into perspective. Whether it is data from medical devices or from IoT-enabled cars, analyzing this deluge of data is humanely impossible. Next-level data analytics use predictive analysis, advanced statistics, and other methods to provide insights from both structured and unstructured data. Most importantly, ignoring all this valuable data can prove to be disastrous to the society as a whole. Data is too large and complex It is not humanly possible to derive useful insights from so much of data When data isn’t analyzed or understood, valuable opportunities are missed Underutilization of data may also result in disastrous outcomes The answer lies in next-level data analytics and artificial intelligence How can data analytics and artificial intelligence take IoT to the next level? Data Analytics derives meaningful conclusions by examining data sets of various sizes. Conclusions can be used to identify patterns, trends or even predict certain outcomes. These conclusions help businesses to improve their products and services, come up with better business strategies, and make effective decisions. In addition, deriving useful conclusions from datasets will help businesses to drive revenue and profits, and gain a competitive edge. Datasets that can be used from different sensors include: Data from video and audio streams Data from geo-location sensors Data related to product usage. This may or may not be linked to sensors. Data from social media System log files There are different types of data analytics that can be used with IoT-enabled devices. Some of those are: Streaming Analytics: This uses real-time data streams to track traffic, transactions, etc. This is most helpful to understand situations where immediate action is required. Spatial Analytics: As the name might suggest, this kind of analytics uses location-based data to understand trends in usage. Time-based Analytics: Using time-based data is important too, and provides valuable information about health, weather, product usage patterns, etc. Prescriptive Analytics: This uses predictive analysis, and sometimes includes descriptive analytics. This helps companies to improve products and services and has wide-reaching commercial uses. There are a number of ways businesses can take IoT to the next level by using Data Analytics. Data insights can be used for the purpose of marketing and product usage analysis. The insights can help not only consumers but also businesses. Video and audio analytics, social analytics, etc. are opening doors to analyzing emotions and behaviors of people. This can be particularly useful to avert emergencies in crowded places or to improve products. Turbo-charging IoT with Artificial Intelligence A Gartner study predicts that more than 80% of IoT projects will involve an AI element, and that is a whopping 70% increase from today’s situation. Most organizations are looking at machine learning and deep learning to unlock the potential of IoT. Machine learning identifies trends and patterns within data sets, and also pinpoints anomalies if any. Machine learning makes identification of patterns more accurate as it does not depend only on numbers, but also on other aspects. Speech and facial recognition, emotion and behavior analysis, and predictive maintenance are all aspects of AI that will help take IoT to the next level. Imagine being able to predict the time for servicing a gadget based on an individual’s frowning patterns? This can happen. Artificial Intelligence is also being used in risk management, and avert disasters and emergencies from occurring. Machine learning enables a software “agent” to identify patterns in a dataset and use those patterns to learn how to adjust the way it further analyses data. The best example would be movie recommendations on Netflix or playlists on Spotify and Apple Music. Machine Learning can identify usage patterns in IoT and help manufacturers to improve customer experience. On the other hand, businesses can benefit from the competitive edge, improved products and services, and enhanced revenue-making potential. Need for implementing AI and Data Analytics quickly As you can see, you cannot remove artificial intelligence or data analytics from IoT. IoT without these two important technologies will simply enable devices to communicate with other and there is no room for improvement of products and services or opportunities to identifies usage patterns and trends. Both Artificial intelligence and Machine Learning take IoT to the next level and help customers and businesses to unlock experiences and opportunities they never knew existed. The future of IoT certainly vested in the implementation of Artificial Intelligence and Data Analytics. Sooner

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