Author name: Rubiscape Team

The journey of Artificial Intelligence or AI from the innovation labs and experimental R&D divisions of tech firms to the mainstream consumer market has been nothing short of phenomenal. For businesses, AI plays a transformative role in shaping the experiences they deliver for their customers as well as employees. In fact, the global AI market is expected to be worth over USD 190.61 Billion by 2025. AI is not just a driver of new experiences but has been at the forefront of adding new dimensions to technology solutions that have been around for a while and were facing their saturation levels. Today, let us explore one prime area where AI is bringing about a rapid paradigm change to the way the world perceives it. It is none other than the world of apps. From smartphones to tablet PCs and wearables, there are millions of apps available for consumers covering a wide spectrum of use cases, and delivering on-demand services right at their fingertips.  While new apps are always disrupting the market, it is to be noted that what drives the app industry forward is the ability of app makers to design and launch apps with intelligence embedded in them. In other words, the app market is growing considerably due to the increased use of AI in their core by developers. From retail to healthcare and fashion, consumers demand intelligent apps that help them navigate through their daily routines and challenges. Let us explore 5 key areas where AI adds the intelligence factor to intelligent apps. Personalization Did you know that nearly 80% of consumers are likely to do business with a brand if they offer personalized experiences? Personalization is one of the key pillars of building lasting customer relationships. Businesses of all sizes race to deliver high levels of personalized interactions to help their customers feel more engaged. The case with apps is no different. AI can drive personalization to a whole new level in services offered via apps. Just have a look at how online shopping apps like Amazon delivers personalized recommendations to shoppers, or Spotify suggests tracks to its subscribers from genres or artists they love more. Every bit of these actions in personalization is driven by AI algorithms that work behind the scenes. Security Today, consumers use apps for pretty much all their needs, and very often these needs include paying for services consumed via the apps or even banking apps that contain sensitive financial information. In addition to financial credentials, today’s apps collect a variety of data from their users for offering more personalized services. With private data and financial credentials being constantly exchanged, apps are a hot target for cybercrimes. It is said that one in every 36 mobile devices has high-risk apps installed in them. AI can bring in threat intelligence for apps and help businesses detect suspicious activity being facilitated through their consumer apps. AI can learn continuously to thwart newer threats based on behaviors it has studied in the past. Performance Most apps leverage far more memory and processing power of smartphones or devices where they are installed, while they are not in full use. AI can enable optimal utilization of resources to ensure that apps work as modular components and only necessary modules draw their required memory and computing power thereby enabling devices to last longer in their battery charge. It can learn how different components utilize device credentials and optimize clock cycles and CPU performance to provide the most suitable operating environment for the app while balancing workloads. Intelligent Search Most consumer apps offer interactive search options for users to navigate and find what they were looking for. Bringing AI into the picture takes this search experience to another level. For example, a shopping app can leverage AI to find a matching product from a photograph that the user’s smartphone clicks. If a user happens to see a print ad or mannequin spotting a dress that he or she likes, they can simply use the shopping app’s camera provision to click or scan the dress, and the AI system can quickly discover similar dresses from the shopping service’s inventory and lay down recommendations of accessories that go well with it. Enhanced IoT Capability With AI algorithms, apps can learn from data that a user’s device collects when it is connected to other smart devices or IoT sensors. Based on its learnings, the AI system can help the app improve the overall experience for the user by optimally matching environmental outcomes of the IoT sensors to the preferences the user usually makes in the app. For example, a smart home system can work autonomously with the help of an intelligent smartphone app that learns the behavior of the home user like the temperature he or she usually sets in each room, the music they love, the volume they usually set on televisions or music systems, the time they turn on the garden watering system and much more. AI opens the door for a wide array of possibilities for today’s business apps by adding intelligence to the mix. For businesses, they have the opportunity to deliver more awesome and engaging experiences for their customers and, in return, enjoy loyalty for the long run. The future belongs to intelligent apps and AI will be at the forefront to bring more intelligence into the app ecosystem.

A common trend among businesses in this day and age pertains to the ‘Speed’ of operation. They are invariably seeking new ways to automate business processes and finding unique and novel routes to implement tech solutions into their workflows. This inclination towards operational efficiencies and a greater focus on quality can be attributed to the constantly evolving market and immensely diverse audience. The emphasis is on implementing creative and innovative ideas without any hassle of having to invest in lengthy processes. And this is precisely what paves the way for buzz-phrases No-code and Low-code. Both these concepts work in favor of streamlining and altering processes with the use of Technology. In other words, Low code and No code present themselves as viable options for businesses by allowing the deployment of ideas quickly and efficiently – while eliminating the need for costly processes of coding and compiling software for internal use. That explains why start-ups, SMEs, and even classical-coding-loving Fortune 500 companies are looking to have Low-code and No-code platforms at their disposal. After all, it’s all about saving resources (cost + time) and warding off the possibility of human resources being occupied with mundane tasks. In that light, this article will attempt to draw a parallel between the needs of distinct-level organizations and shed light on the concept, prominence, value, and necessity of Low-code and No-code platforms. But first, let’s understand what they actually mean. What is Low-code development?   Low-code development is an approach that aims towards the rapid delivery of stable, reliable, and tested software applications without the need for proficient coding skills, diverse code libraries, compilers, and IDE’s. This makes it easier for non-technical users to create customized, tailor-made business solutions. Here’s how Low-code platforms work: They use a combination of drag and drop features, graphical interfaces, visual design tools, and simple business logic scripts, allowing the non-technical users to build applications with pre-built features commonly found in applications developed with traditional coding languages. A significant technical advantage is that they support several mainstream programming languages, including Java, .NET, PHP, and C#, with minimal switching costs. So, the developers or perhaps the business users can make transitions between languages per the requirements. What is No-code Development?   No-code development is an approach towards developing applications, allowing users without programming knowledge or experience access (or use) the system’s capabilities – without the need for any coding skills or tools. Prominent examples of such platforms are online website builders. Here’s how No-code platforms work: They use visual “Assistants” or wizards that guide the user through the process of making changes to an application. The process uses pre-built components and features from within the platform, as well as external solutions, allowing users to build applications without having to worry about coding. Why Start-ups and SMEs Need Low-code / No-code Data Platforms   There are constraints to working smoothly in a competitive environment. More often than not, businesses need to optimize their requirements to comply with the resources at their disposal. But what if the market demands more? Well, then that’s a catch worth addressing. You can’t leave out on growth, or can you? There is a growing consensus that enterprises need to be data-driven. But how do they inculcate the data culture within the organization unless they give the right tools in the hands of every business user? To that end, here are some considerations as to why start-ups and SMEs must invest in Low-code / No-code data platforms. Better Data Accessibility through Quick Development Let’s say as a sales leader, you want to get answers to questions like – where are your orders coming from? What is the average value of an order? Is there any seasonality? Do customers buy the product/ service after any specific marketing campaign? So far, you have relied on multiple excel sheets to get some of these answers. Sometimes, you rely on your gut feeling. But now, you want to get all this data on a centralized platform, but you don’t have the in-house capabilities to build it. Leveraging the potential of a Low-code data platform, in such a case, would provide you with literally everything at the expense of a mouse-click. Even your team members would be able to make use of wizards and assistants to develop required apps in a drag-and-drop fashion without having to learn coding, of course. This tends to simplify the overall process, swapping out the need for technical expertise for an easier-to-follow visual interface that helps in building an app in a snap. The Unparalleled Combination Cost-Efficiency and High-Grade Features No-code/ Low-code platforms also tend to be extremely cost-effective than traditional application development efforts. This is because they generally contain pre-built components within their platform, making app development or data analysis effortless. Most of the modern-day platforms are cloud-based, which means that they’re accessible from any device. The nature of cloud-based infrastructure also means that your app can be updated and made available at any time without the need for complex configuration. In today’s data-driven world, decisions need to be made based on real-time data and insights. Low-code/ no-code data platforms offer extremely cost-effective ways to make use of the data at disposal. Real-time, Data-Driven Decision-Making Suppose a company uses some internal tools and manual processes to track the performance of its marketing campaigns. Unfortunately, they’re completely outdated and inefficient. They do not offer any flexibility or real-time insights to the business decision-makers, which makes them more of a hindrance than an asset. Business leaders cannot make real-time decisions based on the campaign performance because it involves a lot of manual data processing. The crux here is that it’s hard to achieve greater productivity without technologies that simplify the work process and streamline the operations. Again, Low-code / No-code data platforms turn out to be saviors — this time owing to the principle on which they have been developed – speed and efficiency. For instance, for the above case, a marketing manager can use Low-Code/ No-Code data platform

Companies need Data Science now more than ever before as the data economy proliferates to usher in the new age – one where data is the driver of everything. The value that data brings to the table can no longer be ignored – competitive advantage, accelerated pace of innovation, successful new product development, increased efficiencies of people and processes, etc. are the obvious ones. It is but natural to assume that organizations globally will be working hard to improve their data capabilities. It is then hardly a surprise when we see the job of the data scientist being hailed as the sexiest job of the 21st century. According to a study by Figure Eight, 50% of employed data scientists are contacted once every week for new opportunities. 30% are contacted several times a week, and around 85% are contacted at least once a month. These numbers further illustrate the growing demand for data scientists. Just like the role of the data scientists is growing in prominence, it is also evolving as data science matures and industries change owing to the technology impact. So, given the general interest in mind, here’s a look at some of the key skills that an ace data scientist will possess in today’s data crazy world. Technical Skills – Statistics and Statistical Programming It’s redundant to mention that data scientists are highly educated. At least, the ace ones are. 88% have a master’s degree, and 46% have Ph.D.’s. It is natural to assume that ace data scientists have exceptional statistical knowledge and are excellent at Hypothesis Testing, Probability, Descriptive and Inferential Statistics. Having an intuitive understanding of business statistics is another feather in the data scientist’s cap. Their technical knowledge and skillset are expansive. Given that different businesses use different tools and languages in their workflow, ace data scientists have to have a strong core of technical skills that can be applied to many problems. In-depth knowledge of analytical tools is a given for the ace data scientists. The preferred one being R since R is designed to fulfill the needs of data science. While R does seem to have a steep learning curve, 43% of data scientists use it to solve the problems they encounter. Python is another programming language popular amongst great data scientists as it provides them with better insights as well as helps them correlate data from large sets of data. Having a great grasp of Python and its libraries is almost like a trademark of an ace data scientists Algorithm Knowledge Algorithms are data scientists’ playground. These people compete with things such as logistic regression, decision trees, neural networks, random forest, clustering, and the like. Having a great grasp of machine learning and advanced machine learning knowledge becomes quite imperative to participate, play, and win in this game. A deep understanding of different machine learning techniques such as supervised learning, unsupervised learning and reinforcement learning, and their subsequent algorithms is a hallmark of a good data scientist. Ace data scientists also have knowledge of neural networks as deep learning models. They know how to create deep learning models and understand how Convolutional Neural Networks, Recurrent Neural Networks, and RBM and Autoencoders work. Business understanding Did you expect a laundry list of the technical skillsets in this blog? Well, there are a few things that separate a good data scientist from one who is the rock star. This is one of the differentiators. Having good business knowledge and deep domain expertise helps them put the data to work. Data-driven problem solving includes understanding the salient features of the situation at hand, assessing how to frame the right questions to get the right answer, evaluating the approximations that make sense, and knowing which resource to approach at the right juncture of the analytics process. A data-driven approach to problem-solving comes with experience and is a venerable weapon in the data scientist’s toolkit. Having a good business understanding also hones their visualization skills and helps data scientists present their data in a visually appealing format. This helps them communicate better with their end-users as they can use the language of business as opposed to the language of IT Intellectual Curiosity Albert Einstein’s famous words, “I have no special talent. I am only passionately curious” are profoundly relevant in the narrative of a data scientist. Ace data scientists are curious beings and have an innate desire to acquire more knowledge. And ‘curiouser and curiouser’ must you get, much like Alice in Alice in Wonderland, to be an ace data scientist. Why? Because 80% of the time a data scientist’s job entails discovering and preparing data based on the asked questions. And it is curiosity that helps them play with the data, push it, wrangle it and twist it and turn it in multiple ways to get answers that we get ace data scientists. Communication Ace data scientists have to be ace storytellers because they have to fluently translate their technical findings in the language that the non-technical user, such as the sales and marketing teams, can comprehend. The ace data scientist enables non-technical teams with quantified insights and also understands their needs, problem areas, and desired outcomes to wrangle the data appropriately. Creating a storyline around the data helps in communicating the findings easily across the organization, making it easy for everyone to enjoy the fruits of data-driven decision making. Ace data scientists are all this and more. But above all, they are also team players. They understand that they have to work across teams and people to help develop strategies, create new products, launch better campaigns, drive more sales, or improve business processes. While we expect to see this upswing in demand for data scientists, we also feel that organizations have to give their workforce the power to glean intelligent insights from data and make data-driven decision making and organization-wide practice. For this, they need citizen data scientists, who are the everyday employees, and give them the capacity to convert data into insights

The last few years have cemented the importance of data in any business. Owing to the phenomenal contribution data makes to any business, it is hardly a surprise that data scientists are the new superheroes. Glassdoor ranks the job role of the Data Scientist as the ‘Hottest job of the 21st Century’ three years running.  The Deloitte Access Economics report highlights that 76% of companies plan to increase their spending on analytics capabilities over 2019 and 2020. They also forecast that by 2025, the data science professionals with a post-graduate degree will be earning an average $130,176 p.a. The role of the data scientist has shot to prominence owing to the growing importance of data. Data is now becoming part of organizational DNA becoming not only a part of the decision-making process but also of product development, identifying business value and new business propositions, and evaluating risks amongst other things. Data science prepares organizations to find more value in technology. And it is the data scientists who so far have been the enablers of the same. However, as we delve deeper into the tech and the data economy, one thing becomes clear – data science is not a ‘back office’ thing. It cannot exist in the form of horizontal diversity with the organization. As data becomes critical for every organizational function, the traditional role of the data scientist has to evolve. And while organizations will have to have their set of data scientists, they will also need their band of Citizen Data Scientists – the ones who will be using data to empower business decisions and contribute to the bottom line. Data Science – then and now Data Science is no longer a niche role. It does not belong to the hallowed portals of a select few organizations alone. From healthcare to eCommerce, every business and every industry has the need for data science and consequently data scientists. These data scientists have advanced training in math, statistics, and computer science. They have to have in-depth technical knowledge of languages such as R and Python to create robust data models. And what good is this knowledge if they do not have great domain expertise? It is the domain expertise that helps these data scientists manipulate the vast sea of data at hand to glean intelligent business intelligence and insights. The role of the data scientist   The data scientist models and rearranges the data with a visual front end. This is traditionally operated in the scripting interface. The current technology being favored here is R3. Along with this, data scientists also have to manipulate different tools built for specific purposes to get the desired answers from the data at hand. They have to work with Knowledge Discovery Datasets, conduct data exploration as well as data visualization. R and Python have been the favorable programming languages to fulfill the programming needs amongst the data scientists. This has been mostly because these languages are user-friendly and have a big support network amongst other benefits. Clearly, to develop robust data models and fulfill data exploration, visualization, analytics demands etc., the data scientist has to have deep programming and scripting knowledge to use the available tools. This also establishes how niche the role of the data scientist is. The growing need for ‘citizen data scientists’ Given the growing role of data in every organizational aspect, can the role of the data scientist remain as niche as it is presently? In my opinion, organizations now need the non-data scientists, basically, the business users to assume the capabilities of the data scientist. They are the actual users. They are the domain experts. They have the right questions. They know the business problems they need to solve. What they need is the capability to exploit the data at hand to drive their decision making. If empowered, any business user can become a citizen data scientist and apply the right data models to the right data to predict business outcomes. They need the flexibility and the bandwidth to connect with the database and create compelling visualizations, reports, and dashboards. They need the capability to play with the data they have and explore the myriad possibilities associated with that data to answer their own questions. No dependencies involved. Data science has to come to the ground level to create a data-driven organization and develop a data culture. All that organizations need to enable the same is a platform that allows Open Source, algorithms, computation, and business users work harmoniously. The trick lies in ensuring that organizations don’t have to change their processes. The integrated platform should come with high interoperability – one which has a host of pre-built functions, employs popular algorithms, provides toolsets to analyze structured and unstructured data and social data, provides static models for changing data sources, and can handle changing data sources and volumes. Rubiscape is such a platform that empowers everyone to become a data scientist. So yes, with such a tool, your business head could be your new data scientist. Or your manufacturing head or your programmer…anyone in the organization can become a citizen data scientist. And this brings data science to the ground level creating an organization that is ‘data-driven’ in the truest and the broadest sense of the term.

We live in an era where even the mixer grinder in the kitchen produces considerable data and shares it over the internet to a host of apps and platforms. More than 2.5 quintillion bytes of data are generated every single day according to estimates, which translates into an average of 1.7MB of data created every second by every human on the planet! So, what do we do with all this data? The answer is simple – use it to improve our lives in multiple areas. Now comes the question of how to achieve this feat? The solution lies in predictive analytics, which deals with predicting outcomes of scenarios using statistical modeling of historic data. This is one of the key pillars of artificial intelligence and machine learning. Globally the market for predictive analytics was around USD 7.32 Billion in 2019, and it is expected that this figure will reach a staggering USD 35.45 Billion by 2027. In the past, predictive analytics has been perceived as an experimental concept or one that had limited practical usage within a business environment. However, it has now transitioned into a mainstream business enabling capability that is sought out by firms of all sizes. On this note let’s have a detailed look at some of the top real-world use cases of predictive analytics witnessed today: Autonomous Maintenance in Manufacturing   Being heavily dependent on machines, the manufacturing sector constantly invests in keeping their hardware at optimum health to ensure their production commitments are always met. This is precisely the area where predictive analytics has witnessed its most crucial real-time application in the manufacturing sector. By continuously monitoring data from factory components, manufacturers can predict maintenance routines needed. For example, General Motors runs AI-based predictive analytics on images from cameras mounted on factory robots to estimate their failure rates based on signs of wear and tear of components. These may be difficult to be spotted by the human eye without a thorough inspection, but an AI-based system can analyze and predict the chances of failure instantaneously. By identifying such problems at an earlier stage, they will be able to replace the components in due time before it leads to unplanned stoppages or disruptions in the assembly line. Improving Critical Decisions in Healthcare   Nearly 60% of healthcare executives have said that their organization uses predictive analytics to improve patient care and streamline their operations. One of the biggest use cases for predictive analytics in recent times came in the wake of COVID-19 when several hospitals used predictive analytics to track the deterioration of patients in general wards and predicted when they would require ICU support. There have been instances where a hospital reduced serious events by over 35% with proactive monitoring and predictive warning systems. Patient deterioration is a key factor that needs constant attention and in times like these when the healthcare facilities are overwhelmed with patients requiring care, events, where a patient undergoing serious deterioration goes unnoticed by staff, is common. Autonomous monitoring and prediction of criticality ill thus become vital use cases for predictive analytics in healthcare. Better Sales in Retail   In the retail sector, the more you know about your customer, the better will you be able to sell products faster to them. Predictive analytics can be the game-changer in deriving preferences of your customers from the vast amounts of data that they have already shared with brands in their past interactions. One of the best examples of how predictive analytics enabled a retail brand to improve in-store sales was how a Harley Davidson dealership in New York increased its sales leads by 2930% using an AI-based marketing platform. By analyzing customer information from across their CRM, point of sales, and website inquiries, the platform was able to identify the most relevant audience to target with marketing campaigns. This target base was found to have more interest in talking to an in-store salesperson, and the dealership was soon flooded with customer inquiries. Transforming Supply Chains in eCommerce   The eCommerce boom witnessed ever since the early 2000s has created a large-scale impact on how product availability is managed across buyer geographies. Customers expect their shipments to arrive in the fastest possible time and without any damages or misplaced order items. This has created the need for a massive supply chain system for eCommerce companies who rely on different stakeholders in their supply chain to ensure that the product is made available to the customer in the fastest and most economical timeframe. Predictive analytics is bringing about a revolutionary shift in the eCommerce supply chain market by helping businesses anticipate demand and optimize their supplies accordingly to ensure customer satisfaction for every order. Take a look at Amazon which has rolled out a new predictive analytics-based supply chain initiative called anticipatory shipping. In this, wherein data from past orders is used to determine where customers are likely to have more demand for certain products soon and then shipping the products to warehouses or storage destinations closer to the demand areas even before the order is placed. In this way, when the actual order is placed, customers can get hold of their desired product in a matter of hours rather than waiting for days for delivery. This mode of optimization of the supply chain is extremely useful for eCommerce businesses that handle perishable goods like groceries, meat, dairy, and daily-use vegetables and fruits. Better Governance in Smart Cities   The digital transformation wave has hit the shores of civic administration as well. Across the world, smart cities are being envisaged by governments to help create a sustainable living ecosystem for residents while at the same time preserving resources through optimal utilization. Predictive analytics can set the stage for propelling smart cities into the next level and make lives easier for its residents. For example, the city of Beijing uses data from around 35 air quality monitoring stations in its boundaries to forecast the chances of smog. Using predictive analytics, the city officials

The wealth management industry has always been an embodiment of data crunching. During the periods of recessions, it has become more so as data can bring in the requisite foresightedness. Since then, owing to the growing regulatory requirements and the fast evolution of technologies, the industry has been in constant flux. That apart, the market dynamics are also changing. Due to the rise of the new world order post the 2009 crash, there has been a rise in the HNIs and UHNIs in the Asian tiger economies. The new clients come with their unique and diverse set of requirements. This puts additional pressure on the wealth managers. They need to tailor the products as per the new requirements and also search for newer commodities to invest in. Let us look at a few use cases where Data Science can play a major role in making wealth management more data-driven and risk-proof. Risk Management: This word is synonymous with the wealth management industry. Clients are advised to spend their savings on stocks and shares of other companies. The UHNIs and HNIs want their risks to be covered and also get ensured return on investment. Predictive analytics can study past trends and historical data to analyze how a particular stock/ share it going to behave in the future. This predicted future value can be an essential criterion for the buy or leave of the decision-making process. Compliance: As already stated, there has been a lot of compliance and regulatory pressure on the wealth management post the economic depression, to curb the anarchy that prevailed till then. That apart, there are other regulatory requirements such as GDPR to comply with. It would have been one painstaking process if the new tools and technologies for effective data management hadn’t been made available to them. These tools and technologies help in data masking and data anonymization. Workflow Management and Process Automation: Needless to say, many organizations are under tremendous pressure to cut down costs and reduce their operational overheads. The wealth management companies are no exception. They are also exploring Robotic Process Automation (RPA) to streamline their workflows and automate a majority of the redundant tasks. Of late, their use cases are further expanding beyond the rudimentary check clearance and helpdesk activities. RPA and NLP are being used to identify anomalies and also monitor the dashboards alerting the stakeholders whenever something goes beyond the marked threshold. This particular attribute is coming in handy in curbing fraudulent activities too. Segmentation and Targeting: The number the billionaires from the Asian tiger’s economies has been on the rise in recent years, and it will continue doing so. Data Science helps in the behavioral segmentation of the clients. Data can provide insights into the most probable targets and low hanging fruits. It can also help identify the customers who are most probably going to jump the ship. By segmentation and proper targeting of the clients, one can easily have targeted campaigns that will reduce the operational costs of the organizations. This becomes the direct feed for the algorithms which are being leveraged to improved sales productivity. Investment portfolios are also sometimes given the shape of a product, which is something akin to the mutual funds. Given the algorithms, the products are also tailored to suit the UHNIs, and HNIs needs better. Research: When the wealth managers walk into the office of a client, they need to have their research and homework handy. This needs a lot of effort. Even though they have access to data, it is lying discretely in a different repository. Data analytics makes it easier to ingest data into one common pool. From there, NLP-based research can let the wealth managers have a view of the data they have and do the needful analysis. The rise of data visualization tools in the market can help the wealth managers look into the data in a visual format, which further assists in their decision-making process. Asset management: It is the core of all the services provided by wealth management firms. Analytics can provide a view as to which ones are being profitable, which one needs to be let go of, and are their new products which can be added to the kitty. Data analytics, driven by the market sentiments and powered by forecasting engines, can also find out newer assets, which have been traditionally being ignored until now. Movies like the Big Shot have already exemplified how data analytics can help make wealth management more foolproof. There are other organizational areas like workforce management and customer analytics, where they are being utilized inside the organization. We have also seen that data science helps wealth management companies make the operations leaner while complying with the regulatory requirements. Are you ready for the data-driven transformation?

The spending on Artificial Intelligence is expected to reach $57.6 Bn by 2025. Additionally, the current adoption of fintech is estimated to be at 33 percent around the world. It’s no surprise that IoT devices, in conjunction with data-fueled AI systems, have the groundbreaking potential for all industries, including fintech. With everything getting digital and automated, the finance and banking sector is set to radically change by the combined effect of machine learning and the Internet of things. To gauge the scope of potential, let’s look at some interesting ways how ML and IoT are transforming the fintech space. Leveraging ML and IoT to Birth Possibilities Personalized Wealth Management According to JD Power’s 2018 Retail Banking Advice study, 78% of consumers want financial advice and guidance from their bank. However, only 28% of consumers feel they are getting the same. The survey also unearthed the matters that concern customers the most. It found that customers seek advice more commonly about investment, retirement, savings, and keeping track of expenses. In response to this growing trend, almost every other major bank is now using AI and IoT to personalize wealth management for its customers. Personalized wealth management includes services such as tailored retirement advice, products, and plans that fit in well into a customer’s financial portfolio and offer them value considering their current standing. By offering personalized recommendations for products and services, banks can improve overall revenue, business from individual customers, and profit margins – all the while providing a better experience to their customers. Fraud Detection + Cybersecurity Risk Detection ML tools can analyze existing fraudulent cases, detect common patterns among them, and evaluate whether a particular transaction exhibits specific characteristic. he banking industry is the most obvious target for online hacks and frauds. It is, therefore, tempting to look at emerging and arriving technologies to help avoid such associated risks. The financial gain for fintech companies to use ML and IoT in this direction is massive. According to a 2018 report by LexisNexis, for every dollar of fraud, companies have to spend $3.37 in resolving it and appeasing the customer. Banks can address issues such as these by developing programs that use machine learning and deep learning to identify the nature of each transaction before it finalizes. Another possibility for banks is to use ML to learn and identify a customer’s behavior and notify authorities when a customer exhibits a pattern unusual to her. The trick would be to do this intuitively and accurately, so as not to pose an inconvenience to a customer who is not acting fishy. Personalized Customer Experience Banks have remained cold and distant for a long time when other industries have known and reacted to the fact that customer convenience is superior, just like product quality or service delivery is. The average customer still operates in the dark at their bank and is least aware of the policies, terms, and conditions that their bank follows. ML and IoT, along with data analytics, can help create a more friendly atmosphere within banks and other financial institutions, delivering more delightful customer experience. For instance, banks are now looking at customer spending habits and buying behaviors to provide personalized suggestions and saving plans to them that they might have missed. ML and IoT can become the drivers of personalization within the fintech industry, leading banks to better customer engagement. Better Customer Service For customers, getting on the phone with bank personnel can lead to a lot of miscommunication and misunderstanding, often resulting in visiting the bank in person. When it comes to customer service, banks can use AI to automate several tasks, leading to a more efficient, faster, and productive service. According to a report by PwC titled, “Financial Services Technology: 2020 and Beyond”, self-service dashboards are the path to smarter services and smarter sales for banks and FIs, which also look alluring to customers. Several studies have shown that customers now like to take matters in their own hands rather than having to speak with a customer service agent. AI-powered customer service can help banks cut down costs and save man-hours. Wireless Payments, Security, and Authentication The Internet of Things can have a huge impact on how we interact with applications in the fintech domain. As such, wearables have the potential to transform cash withdrawal and payment by replacing traditional cards and smartphones with smart devices. Not only that, wearables and IoT can mean better security in fintech, as banks start to use wristbands and smartwatches to track the person’s heartbeat as a biometric authentication key. Solutions such as Kerv position themselves as the first contactless payment ring, allowing us to be optimistic about the possibilities of IoT and wearables in the fintech industry. According to Forrester Research, AI and IoT are the technologies that will provide an edge to fintech companies by 2025, allowing them a massive opportunity to grow and expand on the customer engagement front. With the growth in technology and the ever-changing demands of financial markets, the revolution was inevitable. Combining Artificial Intelligence, Machine Learning, and the Internet of Things in banking can prove crucial in attracting customers, retaining them, and offering them value coupled with stellar customer experiences. Unsure about the next move? Rubiscape helps fintech companies with technologies that add value to their digital transformation efforts. Get in touch with us to explore synergies. Linkedin X-twitter Facebook

Images are the new currency. Don’t believe me? Take a look at these statistics 95 million images are uploaded on Instagram daily – Source There are over 330 million active Twitter users and tweets with images receive 150% more retweets – Source 60 million emojis are used on Facebook daily – Source The proverb ‘a picture paints a thousand words’ has become even more relevant in today’s sharing age. But why? We could say that it is easier to express a feeling with an image than text. Or that the human brain processes visual content faster. We are now living in the world of hashtags, emojis, limited text characters. Images complete the stories these are trying to tell. Images are not only enhancing text but are often standing in place of it entirely. And wouldn’t it be a shame if organizations today did not leverage the information from this huge ocean of visual data? It is estimated that by 2025, the global image recognition market is expected to touch $38.92 Billion. The video analytics market is expected to touch USD 8.55 Billion by 2023. So where does image analytics feature here? Much like how sentiment analysis raised the bar for social monitoring, image analytics is raising the bar for social listening. There is a goldmine of data stored in these images as these help organizations understand visual sentiment especially when text is absent. Take this tweet as an example. The entire sentiment of the tweet is summed up in the hashtag #PerfectDay. Nowhere is the airline brand mentioned but only the word ‘plane’. Now imagine the kind of opportunity Emirates could have created by leveraging logo recognition resulting from image analysis! A tweet like “Thank you for the click! You’re such an amazing mom. We hope you and your son had an amazing time plane spotting”.  It is this human-to-human kind of conversations that customers are looking for. So, what exactly is image analytics? Image analytics isn’t some futuristic technology. You are probably using it without even knowing it. Your smartphone is already categorizing your photos. Your iPhone will tell you easily which photos are from your office party or from your latest adventure trip. What image analytics essentially does is categorizes images from different sources and sorts them according to contexts such as facial expression, age, action, topics, sentiment, gender, and brand logo. How does it do so? Quite simply by leveraging automatic algorithmic extraction and consequent logical analysis of information found in the image employing digital image processing techniques. And why should you care about image analytics? Fritz Venter and Andrew Stein say that the objective of image analytics is to “bring an unstructured rendition of reality in the form of images and videos into a machine analyzable representation of a set of variables.” Here are a few reasons why image analytics is something to look out for Source authentic data There is data everywhere. But how authentic is that data? Organizations across the US spend almost $10 billion each year on third-party authentic data. This sourced data has its accuracy limitations and yet forms the basis of many personalized marketing campaigns. The result? Limited accuracy. With image analytics, the data organizations source will not be mere numbers from a survey, but actual customer data derived from first-hand sources, think facial expressions. The data is also captured real-time when the customer is experiences something, often before she makes a purchase. Such data becomes more relevant when personalizing offers as the insights derived are deeper, more accurate and also real-time. Improve customer journeys The age of digital transformation puts the customer in the centre of all focus. And for that, it is imperative to improve customer journeys and customer experience. Leveraging facial recognition businesses can create a positive impact on the same in several different ways. The airline industry, for example, can leverage image analytics to replace passport checks or deliver travelers from annoyance caused by straggling passengers. Changi airport, for example, is putting facial recognition technology to work to find lost passengers, detect and find people who are on a particular flight or leverage camera-based scanners to automate passport gates. The airport is also using this technology to offer self-service at check-in, immigration, and boarding. Insurance companies can use image analytics and facial recognition to improve the insurance claims process. An insurance provider in the US is doing the same by allowing customers to upload pictures of their damaged vehicles. The company analyzes these pictures and processes the claims. Insurance companies can also leverage this technology to reduce liabilities from workmen injuries from dangerous risk assessment areas such as rooftops and use drones instead. They can also determine the extent of damage and claims estimation reporting and enable faster assessment of claims especially in the wake of natural disasters. The retail sector can unlock the power of image analytics to validate customer identity at stores using cards such as Mastercard. This can help prevent cart abandonment by eliminating challenges such as OTP’s sent via text messages. Facial recognition and image analytics can also be leveraged to gauge customer dissatisfaction by analyzing the customers’ facial expressions and movement. It can also be used to activate customer loyalty programs and preferences and improve the customer experience and customer journey. Optimize retail initiatives Image analytics has a huge potential in the retail industry. The Consumer-Packaged Goods industry, for example, could leverage some technology advantage to optimize shelf monitoring, store checks and audits. With this technology, retail outlets can Effectively track and monitor in-store operations using shelf images. Get real-time insights into key performance indicators such as stock outs, on-shelf availability, compliance metrics or pricing changes. Improve store coverage for field sales representatives by replacing manual checks with image recognition. The analytics derived from accurate in-store insights help to optimize store coverage, identifying performance issues and thereby improving retail execution and recovering lost sales. Improved sentiment analysis Sentiment analysis is gradually becoming an essential contributor to improving customer journeys. Using image

For the past few years, several industries have leveraged the power of data science in marketing, risk management, fraud detection, business operations, supply chain, business model innovations, and many other such areas. By using statistics, predictive modeling, and machine learning, data science helps enterprises resolve various challenges within the industrial sectors and for the economy at large. When implemented correctly, data science is a powerful tool and brings positive results. We already witnessed massive changes in how the western countries and industries work and how data science is empowering that change. But when it comes to India, we are still lagging behind the western counterparts. Dr. Avik, Head, Data Analytics, Niti Ayog, stated that “We are trying to make sense of the operational data to get a good picture of the state of the economy.” He also shared his challenges over the collection and implementation of data, as most of the collected data is highly unstructured and difficult to make any sense. Data science, as we understand, is the study of information such as where it comes from, what it represents, and how to transform it into a valuable resource in the creation of business and economic strategies. It also includes mining of extensive structured and unstructured data. It identifies the patterns which can help businesses or government bodies to minimize costs, increase efficiency, recognize new opportunities, and take other development advantages. While the data collection is vital, it is only the first step in the process. The ultimate use of data is to derive meaningful and actionable insights. How data science can transform India For centuries, data has been the backbone of all the research. Now with the proliferation of advanced technologies, programming languages, and availability high computing power, its use has spread to countless businesses as well as for formulating better government policies. Let’s take a look at various ways data science can transform India – Conserving Water According to the Niti Ayog water index report, 21 cities of India including four major cities namely Delhi, Hyderabad, Bengaluru, and Chennai, will run out of groundwater by 2020. Every summer, we see drought, and the situation is worse in rural areas. The water usage is also increasing, and less rainfall could end up turning it into a severe crisis very soon. The Indian government can use data science to predict water level and the water usage patterns in certain areas. The government can also organize water supply tanks in time after making an informed decision with the help of collected data. Armed with more information and data, it can become easier for the government bodies to come up with more ideas for rainwater harvesting and to increase the groundwater level. Controlling Air Pollution As per the World Health Organization report, 11 out of the 12 most polluted cities are in India. India ranked 141 out of 180 nations in the environmental performance index. This shows that the problem is grave, and the authorities need a permeant solution to control air pollution as soon as possible. With the help of data science, the government can take preventive measures to control different variables like pollution from vehicles, crop burning, industry fuel, and biomass burning. Generating Electricity Did you know? India is the third largest producer and consumer of electricity? India’s gross electricity consumption during the 2017-18 was 1,149 kWh per capita. The demand for power is soaring every year. The Government of India has also launched the “Power for All” program to provide adequate electricity supply to all the people in the country. Data science can help authorities to understand the consumption pattern of the households classified by states, districts, cities, regions, streets. After interpreting the demand and the pattern of consumption, the authorities can predict the usage of electricity and take appropriate measurements to organize for the required electricity demands and optimize the usage. Improved Healthcare Indian healthcare system offers a unique approach to leverage data science to conduct research, clinical trials, and medical data to plan public policies. The healthcare system providers generate data through various resources, including the biometric, patient’s record, medicines, prescriptions, and many others. When stored centrally and analyzed in real-time, this data can provide actionable insights, predict outcomes, and help in better planning of treatment protocols for improved public health. Enhanced National Security The security situation in India is unpredictable. The general security officers face a lot of challenges to understand how the information is analyzed, collected, and implemented to protect the country against unforeseen situations. They can use data science to collect information and identify the gray areas associated with security. The police and security agencies can collect and analyze the data and tackle crimes, attacks, and other dangerous circumstances in the country. Traffic Jam Solutions Long traffic jams are frequent in India. The Niti Aayog team is working to understand the causes of traffic jam and options to deal with them. The government bodies can use big data to analyze some of the significant aspects of traffic jam like choke points, narrow or broken roads, lack of traffic management professionals, failure of traffic lights, and other such elements. The traffic policemen can use CCTV cameras and sensors to monitor traffic and immediately solve the problem. Road accidents kill more than 400 people every day. The figure is disappointing, and the traffic authorities can use data science to analyze the pattern and take immediate action to prevent accidents. Build a Better Nation Data is an essential asset and backbone for the smooth functioning of various government bodies. Government agencies collect a significant amount of data from surveys, programs, public banks, and administration. The roadmap to build an analytical framework to integrate data in public departments is quite promising and will transform the entire nation digitally. The government needs a strategy which merges industries to build successful data and analytical capabilities. This would offer a wide range of social benefits to the citizens and improve their lives considerably.

The finance ministry, in February 2018, cleared National Institution for Transforming India (NITI) Aayog’s Rs 7,000-crore plan. The funding will support a national Artificial Intelligence (AI) program. This program will include the identification of certain projects as well as initiatives, in which the government is going to implement AI technology. This announcement was based on the launch of a Task Force on Artificial Intelligence for India’s Economic Transformation by the Commerce and Industry Department of the Government of India in 2017. Before we proceed ahead, here’s a brief about AI in India – The AI industry is currently valued at $230 Million in revenue approximately. There are tremendous opportunities and substantial economic impact of AI, which can boost India’s annual growth rate by 1.3% by 2035. This roughly translates to the addition of approximately one trillion USD to India’s economy. There are about 40,000 AI professionals in India, with Bengaluru being one of the leading AI hubs in the country. Over 1000 firms in India claim to work on AI in some form. The Central Board of Secondary Education (CBSE) has approved the implementation of AI as a subject for students in classes 8,9 and 10. While these have set things in motion, it is the national strategy for AI that has everyone hooked. The NITI Aayog has carefully evaluated several sectors that are going to be impacted by AI. In no particular order, these are: AI in Healthcare Healthcare in India is one of the most crucial sectors in the country, which is expected to grow to USD 280 billion by 2020. The challenges in this sector include accessibility, quality, and affordability for the         majority of the population. So, what is being done in this direction? The Union Health Ministry is working towards creating an effective roadmap of public healthcare in the country by addressing various gaps. It is realizing the economic impact of AI and prioritizing the building of AI technology capabilities. For instance, one of the initiatives that the central government has undertaken includes – an Imaging Biobank for Cancer. Through this initiative, the NITI Aayog, along with the Department of Biotechnology, can realize the goal of building an extensive database of cancer-related radiology and pathology images. This database will be inclusive of over 20,000 profiles of cancer patients. This can be effective in the treatment of the disease at a lower cost by enhancing the decision-support. Another application of AI in healthcare is that of Diabetic Retinopathy. NITI Aayog is already working with tech-biggie Microsoft and Forus Health for rolling out a technology that can lead to early detection of the disease. All in all, it will help the country progress towards more proactive healthcare services. AI in Agriculture Back in February 2016, Hon’ble PM Shri. Narendra Modi launched the Prime Minister’s Crop Insurance Scheme or Pradhan Mantri Fasal Bima Yojana. This scheme covered various long-standing issues of the farmers, including calamities, loss of crops, germination risks, and so on. Several agencies were roped in for the pilot projects using AI. Sample this, NITI Aayog and IBM have partnered to bring forth a crop yield production model using AI. This will be used to offer real-time advisory to the farmers. The predictive analysis will generate insights which can enhance soil yield, crop productivity, and also offer early warnings from remote sensing, weather prediction, crop phenology, etc. The farmers are all set to benefit from accurate advice. The strategy here is to use AI for solutions that will involve projects from several agricultural, technology-based startups. AI in Education Education is undeniably the most important sector in India. The country, at present, faces challenges that include low-retention rates in rural schools as well as poor learning outcomes. To rectify this, India needs to leverage AI tools to overcome these issues. These can be in terms of interactive tutoring systems and adaptive learning tools that can customize learning for the students. For this purpose, the IITs are partnering with MHRD for democratizing education with the IIT -PAL initiative. The aim is to create an AI-powered education sector, which will cater to the students in smaller cities and towns. Another case in point is that of a hackathon that was conducted by the NITI Aayog, which featured ReadEx, an Android application that generates questions in real-time using NLP (Natural Language Processing), creates flashcards, and offers content recommendations. Also, the AP government has teamed up with Microsoft to predict the number of dropouts in the state based on ML and analytics. Based on the reports, they can take measures such as conducting programs and counseling sessions to bring down the overall drop out rate. AI for Smart Cities and Infrastructure Let’s face it. The urban population is growing by leaps and bounds, and in the coming decades, the number will be exponentially high. According to data by the UN, the world population will reach up to a limit of 9.7 billion by the end of 2050. Therefore, when there is a surge in unplanned urbanization as well as issues like congestion, over pollution, and sub-par living standards, then it is going to create a major issue for the citizens in the future. AI can be handy in urban planning, efficient utility distribution, enhanced delivery of services, and help with many more solutions for the smart city and infrastructure woes. The Government of India is on a mission to set up several Smart Cities Pan India to drive better quality of life and economic growth. For this, 99 cities have been selected with the investment of INR 2.04 lakh crores. The strategy to be implemented in these cities includes the city extension, city renewal, city improvement, and also, covering the cities with smart solutions. One of the key initiatives for this purpose is the Atal Mission for Rejuvenation and Urban Transformation. AI plays a major role here by helping with predictive intelligence and augments the features of smart cities. These features can be – crowd management, smart parks, public facilities, and