Category: Data Analytics

For Enterprises, what does it mean to be AI ready?

February 28, 2020

It would be so cool if we can ask Amazon what shoes should I buy, ask Siri about the cause of the delay in my cab or even request Google AI to fix my fuse. AI is expected to dramatically reshape fundamental business processes that serve faithfully in the background, enabling digitization to fully penetrate key business interactions and transactions. Why do enterprises need AI? Businesses today need to run at the same pace to stay in this competitive market while balancing the wavering factors like knowledge retention, sustainability, scalability, etc. To achieve the pace and agility, an enterprise requires exemplary harmony, self-governing data, content and strong management in such a way that it provides meaningful support to all the business problems. As the volume of this data keeps increasing exponentially, it becomes a little difficult to derive valuable insights from them. As a result, it affects the decision-making process. AI is helping in creating the maximum opportunities by being a great business driver. A survey conducted by BCG (Boston Consulting Group) among 112 CIOs across multiple industries saw that Artificial Intelligence technologies could significantly improve the cost-effectiveness and performance of IT operations, allowing organizations to stimulate innovation rapidly without making any sacrifice on service, security, or stability. Where does the setback originate? AI has proved itself to be revolutionary but if we look at the other side, it leads to certain setbacks too. Data crisis can lead to hazardous errors Development and training go hand in hand. When you develop an AI-based product, it needs to be supported by both monetary and non-monetary factors like training. A recent failure tasted by IBM purely justifies the statement. In 2013, IBM partnered with The University of Texas MD Anderson Cancer Center and developed a new health care system called “Oncology Expert Advisor” with a mission to eradicate cancer. In July 2018, it was found that the AI was recommending erroneous treatment solutions. The technical experts suggested that the main reason behind it was the lack of training on real cancer patients. Too fragile to respond to the supporting data There are several instances where the outcome of AI and machine learning proves to be biased, sexist and misogynistic. There cannot be a better case study than Amazon’s AI for recruitment to justify the statement. Amazon has developed an AI that shortlists the best 5 candidates out of the given 100 resumes. They trained their AI based on current engineering employees who were male and white. Therefore AI learned that white men are the perfect fit for engineering jobs. Lacks a mind of its own AI is artificially sensed, as it does not have a brain of its own. It was witnessed in the case of our commuter partner Uber. Uber’s self-driving car was running at a speed of 61kmph and could not recognize the lady who appeared from nowhere in the dark, resulting in a crash. In the previous couple of years, enterprises have seen many cases of AI failures. The tiniest of loopholes can lead to big problems. The product managers need to spend the maximum time testing the product. Can AI still be game-changing? There are two areas of artificial intelligence that are most applicable, such as Machine learning and Natural Language Processing. Machine learning is a subset of AI techniques that uses statistical methods to enable machines to improve with experiences. Whereas Natural Language Processing is a subsidiary technology of AI that understands and responds to everyday conversational language such as Alexa, Google assistant, chatbot. These artistic features are world-changing algorithms. According to a report, It is claimed that, by 2035, Artificial Intelligence will have the power to increase productivity by 40% or more. Enterprises who have explored the core use of artificial intelligence have reached a long way. There are a few examples that satisfy the statement pretty well. Dominate the global business The e-commerce giant Alibaba used Artificial intelligence and machine learning to expand its business operations all over the world. They collect the data related to the purchasing habits of the customers. With natural language processing, it automatically generates product descriptions for the site. It has also used AI algorithms to reduce traffic jams by monitoring every single vehicle in the city. Additionally, with the help of its cloud computing division called Alibaba Cloud, it is helping the farmers to monitor crops to improve yield cost-effectively. China is planning to be a dominant AI player and build an AI industry worth $1 trillion by 2030. Back up huge profits An AI software company, Sidetrade, has built a core AI platform known as AIMIE (Artificial Intelligence Mastering Intercompany Exchanges) that processes 230 million B2B transactions. That’s equal to around $700 billion sales and finance undertakings over the last three years. Web-crawling robots further enrich this data with 50 billion data points collected through websites, social networks, and online media sources that are relevant to the activity of 23 million European companies. What AI can do for your enterprise? If a business looks at Artificial intelligence with the lense of business capabilities, it will wipe off 50% off their pressure. AI majorly contributes to three major areas in business: Business process automation, Customer engagement, and Insight development through data analysis. AI can help an organization in creating new products, enhancing its features, providing a creative workspace to the employees by automating tasks, making better decisions, optimizing market operations, etc. In a survey conducted among 250 employees of a particular organization, the percentage of the contribution made by AI in an organization was found. Is your Organisation AI ready? Every enterprise needs to analyze if they are ready for the AI revolution. To bring AI into the business mainstream, companies need to complement their technology advances with a focus on governance that drives ethics and trust. If they fail to do so, their AI efforts will fall short of expectations and lag the business results delivered by competitors that responsibly embrace machine intelligence. Organization gotta embrace the

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Evolution Of Augmented Analytics. Actuality Or Just Another Hoax?

November 29, 2019

The fundamentals of augmented analytics are pretty straightforward. Gartner’s definition from its IT Glossary is as follows: “Augmented analytics is the use of enabling technologies such as machine learning and AI to assist with data preparation, insight generation, and insight explanation to augment how people explore and analyze data in analytics and BI platforms. It also augments the expert and citizen data scientists by automating many aspects of data science, machine learning, and AI model development, management and deployment.”  A big part of augmented is also Natural Language Query/Processing (NLP/NLQ) and Natural Language Generation (NLG) that creates dynamic narratives to describe what’s happening in your data. AI means Applied and Invisible  For augmented analytics to progress, it needs to be fabricated with your data and analytics regimen program. That means the system will offer you recommendations, make the complex easy, suggest options you hadn’t thought of. It may be a dream for many, yet for others, it’s sort of a “black box”—they need to be aware of what’s happening around at each and every stage. These divergent opinions mean that adoption (and acceptance) will take time to mature. It needs to apply yet it will stay invisible to the users.  From Data to Insights to Actions  Analytic workflows start when the first bit of data is captured to when action is taken to achieve your desired outcome. That’s a pervasive flow that incorporates everything from data management through business applications. Augmented analytics plays a major role in the complete process. At many industrial firms, the aging workforce is starting to become a big concern, said Heena Purohit, senior product manager for IBM Watson IoT. “Companies are now looking for innovative ways to retain their tribal knowledge and expertise, and augmented intelligence is helping them in this pursuit,” she said. One Australian oil and gas company had this augmented analytics feature absorb 30 years of engineering and drilling knowledge to help technicians tap into it to make fact-driven decisions about complex projects. “Using this solution, technicians and operators reduced the time spent finding data by 75%, which translates to a $10 million savings in employee costs because of faster access to information and more intuitive analysis of engineering records for the organization,” Purohit said Another instance includes Adobe which built a machine learning tool called Segment IQ that offers a button-click comparison of two groups of customers and compares them across hundreds of different dimensions. It’s a common analysis outside of machine learning, but one which takes teams months or years to do. Having that tool available makes it possible to confirm or refute intuition-based hunches from leadership on the fly. So, how will augmented analytics evolve? In our opinion, it will follow a typical Capabilities Maturity Model (CMM) six-stage progression:   Augmented Analytics Evolution: Source: Business Insider – Oracle Corporation Level 0 – Artisan: Everything is hand-crafted, much like it has been for decades. Level 1 – Self Service: Data management is still largely manual, but the human interaction with data will be enabled with Natural Language Query, one-click statistics for easy forecasting, outliers, etc. and recommended visualizations based on the type of data. Level 2 – Deeper Insights: Early stages of augmented data management appear (recommended sources, joins, crowdsourced suggestions, smart cataloging), and augmented discovery uncovers insights that do the heavy lifting, so you don’t have to. Level 3 – Data Foundation: The second wave of augmented data management begins, with corrections, and enrichments. Analytic automation hits its stride with narratives at every level—a visualization, a canvas, a data set. Level 4 – Collective Intelligence: The system becomes instrumented and learns patterns of metrics and key performance indications (KPIs) that alert when conditions need attention. These are both business KPIs and system KPIs. Insights become pervasive, business intent goes from an idea to a reality, and outcomes are predicted, actions are recommended—but humans still take the action. Level 5 – Autonomous Action: Everything truly becomes data-driven, with the next best actions executed based on predictions, insights, and intent. The system is the engine of change. The analogy of this is autonomous vehicles. Smart capabilities gradually extend to assist the driver, expanding over time, with the eventual goal that the “driver” is the machine. While we are a long way from “driverless” completely controlling data and analytics, there are situations where it already exists: Real-time offers that are served up automatically to buyers online, all driven by rules and algorithms. Programmatic trading in investments, where algorithms trigger buys and sell orders to public markets. Self-tuning, self-managing databases, as in Oracle Autonomous Data Warehouse, that automates many activities. Like any maturity progression, it’s not always a strict “step by step”. You may adopt capabilities in a different order or choose to bypass some capabilities all-together. So, no worries if this doesn’t reflect your current journey or future plan. What do you think? We’d love to hear your ideas about Augmented Analytics maturity. Do you agree? Have a difference of opinion? We’ll continue to refine this over time, with your input along the way.

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How Disruptive FinTechs are Solving MSME Credit Crunch? –An Indian Perspective

August 23, 2019

Snapshot of the current Indian MSME sector  8 million enterprises Employs approx. 124 million people 14 % are women-led enterprises 59.48% of total establishments were found in rural areas Accounts for 31% of India’s GDP Accounts for 45% of exports Biggest Challenge: Lack of adequate and timely access to finance Source: MSME Annual Report Source: MSME Annual report Traditional Financial institutions remain wary from providing loans because Small ticket size Higher cost of servicing the sector Limited ability of MSMEs to provide collateral The overall demand for both debt and equity finance by MSMEs is estimated to be INR 87.7 trillion (USD 1.4 trillion), which comprises INR 69.3 trillion (USD 1.1 trillion) of debt demand and INR 18.4 trillion (USD 283 billion) of equity demand. This need for technology disruption in formal debt financing has been underscored by a credit gap – estimated at USD230 billion in 2017 – coupled with demand and supply-side issues in financing for MSMEs. Source: Estimation-of-Debt-Requireme-nt-of-MSMEs-in_India report The Role of Technology in Driving Digital Finance According to PWC, MSME banking is likely to be the fourth-largest sector to be “disrupted” by Fintech in the next five years after consumer banking, payments, and investment/ wealth management. Fintech companies are offering solutions that can substantially improve efficiencies at every step of the lending process. Fintech models can provide end-to-end solutions for the lending value chain or “full stack lending models” such as peer-to-peer (P2P) lending, marketplace lending, crowdfunding, invoice based financing and so forth. Currently, a number of Fintech companies are providing for small-ticket loans focused on MSMEs that have limited credit history and need formal funding. Vistaar Finance – a Bangalore based company has created online sector-specific credit rating templates for the MSM businesses it serves. With a loan portfolio of INR 1,270 Crores, it has digital branch option for those businesses who wish to transact online and have access to technology to do so. Vistaar Finance has developed a template to list all categories of products as well as the margins a store makes on each product even with such basic information. Its credit managers evaluate customers based on this information. Vistaar has also tied up with Indian e-commerce platform in the B2B segment, mjunction in order to serve its small purchasers on the e-auction platform. The partnership is designed to augment the services of mjunction to its customers and also provide a healthy mix of portfolio for Vistaar in the MSME segment. Another Pune-based Kudos Finance that provides microfinance services to small businesses carried applicant sourcing, credit assessment and disbursal offline since customers do not have access to smart phones and need in-person communication channels with lenders, the company uses stores its credit data and documents, making communication within the NBFC more efficient, ensuring disbursal within 5-7 days. Before approving loans Kudos perform proprietary method of assessment by following robust underwriting process and also performs last mile customer verification to avoid frauds. Kudos has been continuously working on adopting technology to automate processes and has implemented systems to improve customer on boarding experience, decision making quality and reduce turn-around time of a transaction. Kudos Finance & Investments is actively using 28 technologies for its website. These include Viewport Meta, IPhone / Mobile Compatible, and SPF. NeoGrowth is a good example of a pioneer lender leveraging deep data insight and analytics to drive customer sourcing, underwriting, and monitoring, supported through a best-in-class tech stack. NeoGrowth serves MSME retailers, applying smart analytics on their bank account and financial data, along with insights from the retailer point-of-sale (credit card) system to predict customer patterns and behavior. The company also offers flexible and innovative repayment options to customers which are linked to their actual business revenues and performance. NeoGrowth assesses a borrower basis the digital spends happening on POS machines at his outlet. The proprietary technology platform of NeoGrowth, helps in analytical underwriting around the digital spends data and other alternate data. With its tech enabled underwriting it provides tailor made loans to various merchants as per their industry segments ranging from food& beverage, apparel, Salon, petrol pumps, automobile dealers etc. NeoGrowth’s card statement based scoring algorithms provide a better assessment of credit – worthiness of small businesses as compared to traditional balance sheet based lending. How are incumbents using Fintech solutions for digital lending? The advent of digital lending has addressed some of the major customer on-boarding hurdles faced by loan seekers in India. Kotak Mahindra Bank which launched its flagship Fintech product Kotak 811 to offer instant credit card issuance states that there was an 85% customer opt-in for the free credit score assessment. In addition to retail credit offerings to individuals, lending startups can play a vital role in formalising credit delivery to MSME, where 40% of the borrowing is still being carried out through informal channels and cash transactions. Bengaluru-based Shubh Loans has started the process to apply for an NBFC licence. The platform, founded by former banker Monish Anand and Goldman Sachs executive Rahul Sekar, is targeting the financially underserved segment. Shubh Loans has reached a monthly disbursal rate of INR 15 crore and is doing around 5,000 loans per month. Another tech startup Moneytap had begun by offering a credit line to consumers in partnership with private sector lender RBL Bank. The Moneytap app was launched in 2015 with RBL Bank, that lets lenders procure a sum between INR 3,000 to INR 5 lakh at a lower interest rate than incumbents. Once the amount has been payed-off, the app let its lender apply for more sum. Way Forward The future of Financial Services industry is bound to be customer-centric, technologically up-to-date( driven by the world of digital) and supportive of internal and external innovation efforts. According to PWC, more than 90 percent of MSME digital borrowers in the next five years will be first-timers. Consequently, there will be more drop-offs as MSMEs become frustrated or confused by the journey and abandon their efforts. Roughly 30 percent of MSME borrowers expressed increased

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Digital Success Summit 2019: What to Expect

July 18, 2019

A stimulating conference program focused on digital innovation and transformation. Over the years Indus Net has partnered in digital systems with a number of established players. We regularly innovate in Cloud/Data center, AI, Cyber Security, IoT & Mobile Technologies providing organization with the opportunity to keep in pace with global trends. Our Digital Success Summit V2.0 on 8-9 August 2019 with the theme, ‘Growing Digitally, Growing Profitably’ will encompass 7 deep dive workshops on Day 1 and back to back sessions by speakers from around the country on Day 2. Attend Day 1 to gain actionable insights on How to leverage businesses through powerful storytelling with Indranil Chakraborty The need and know hows for digital innovation with Abhishek Rungta How you can leverage content marketing to build your consumer base with Shubho Sengupta Learn the sorceries of social selling with Kiruba Shankar Get your digital marketing right with Aji Issac Mathew How can you use a combination of storytelling, imagery, and testimonials to boost business with Soumitra Paul Learn the best branding practices for your brand sustenance with Laeeq Ali Attend Day 2 to learn from discussions with the likes of Amit Ranjan is Co-Founder of SlideShare, which got acquired by LinkedIn. Since then he has worked with Government of India to build the DigiLocker project, which is used by more than 10 million citizens. Amit has always been passionate about building outstanding products that sell themselves. Learn from him about building virality into the product or service design. A star teen entrepreneur, Atreyam (Leo) Sharma who has been addressing leading technology events globally since 2014, including TEDx talks in India and Luxembourg.He started coding at the age of 11 and the following year, Co-Founded Workshop4Me. Vikas Malpani, a serial entrepreneur who also co-founded, India’s leading property listing platform-CommonFloor.Com. He is ranked as Business World’s India’s Hottest Young Entrepreneur & has won MIT TR35 Young Innovator Award for his growth advices. And hear many others speak on how to make your product or service viral, sales team management hacks, how to build and manage a remote team, consumer marketing on a shoestring budget and about personal branding Why Should You Attend? We are flying in a delegation of 500+ representing 100+ business covering the country, providing your organization with the opportunity to reach an audience including Enterprise CEOs, CIOs, CISOs, MIS / IT Directors PLUS Cloud Operators, Telcos, SPs, etc. Our Summit provide a highly efficient, cost-effective and proven formula for tech industry CEOs and senior execs, to learn through networking in a single location, in just 48 hours.

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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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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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How Data Influences Media and Marketing Today

September 27, 2018

Market research has always been the basic tool to design and develop strategies and campaigns. However, traditional market research consumes a lot of time and requires special skills to process and analyze and to derive insights. Marketing campaigns in the past weren’t accurate because market samples did not truly represent a population, and both advertising and marketing strategies weren’t quite accurate. Campaign failures and losses can be tied down to incorrect insight or partial insight into a market’s needs and demands. In addition, most marketing agencies depended on print and TV to disperse marketing messages until the recent past. Digital media changed all that and democratized the process of marketing and advertising, while contemporary data techniques have taken digital marketing to the next level. Thankfully, newer data analytics techniques have not only reduced marketers efforts to crunch data but have also ushered in a new era in which marketing campaigns are highly personalized, scalable and democratic. In this article, let us take a look at how data has influenced media and marketing, and how there has been a complete paradigm shift. Integration of tools Software integration has led to richer insights and predictions, as there is a larger sample of data to analyze. Cloud-based solutions have helped companies to implement affordable integration solutions across departments. Integration has also brought together disparate software solutions such as CRM, ERP, and HRMS which help businesses to access more detailed data and predict outcomes accurately even on the go. Current marketing and advertising initiatives depend on such an integrated approach to make the correct move. An increasing number of agencies use MarTech solutions to predict better campaign outcomes, and this is possible because of modern data analytics. MarTech consists of marketing automation tools such as Marketo, HubSpot, MailChimp, SalesForce, and Insightly. It also includes data and intelligence tools such as FullContact, Cloudinary, Decibel, among many others. In addition, predictive analysis tools help us make better predictions and foresee campaigns even before campaigns are launched. This allows us to have defined outcomes in mind. Some of the most important predictive analysis tools used today are Microsoft’s Azure Machine Learning Studio, SAP Predictive Analytics Software, IBM Predictive Insights, among many others.  These tools can be integrated with each other, or with other enterprise software solutions for richer insights. Personalized marketing and advertising Earlier, personalized marketing was a challenge and a number of efforts never yielded the desired results. However, thanks to social media, it is easier to curate customers and people with specific interests and capture their sentiments easily. All this data can now be crunched and analyzed for better insight, leading to highly specific marketing and advertising campaigns. There are a number of marketing automation tools that help you personalize advertising. HubSpot and MailChimp can be used by both small and medium-sized businesses to personalize campaigns, while Marketo is a value addition for larger organizations. All these tools use data to take personalization to the next level. In addition, you can use Google Optimize 360, which helps you create custom segmented customer experiences. Forbes also listed Clearbit, Kickbox, Quickmail, Buzzstream, and other tools in its list of tools that help personalize marketing and advertising. In short, these tools help to gain better insight about customers and market, which helps personalize marketing and ad campaigns even at the micro level. The advent of MarTech and AdTech In the last couple of years, technologies that assist in automating and turbocharging marketing and advertising processes have been given the terms of MarTech and AdTech. Both these technologies have helped thousands of agencies to provide better campaign results, automate most marketing processes, and process data in a useful manner. The advent of MarTech and AdTech has also resulted in marketing Big Data. Various market-related data is constantly added to Big Data, and data analytics continue to derive richer insights. Most importantly, MarTech tools like GetResponse, Autopilot, iContactPro can be integrated with ERP and CRM for more coherent insight. After all, both frontend and backend need to be in sync with marketing campaigns for the message to reach effectively to the right audience. It is important to note that while marketing technology tools can up your data game, it is really up to you how to use the insight your derive. For example, integrating a digital asset management (DAM) with Adobe Creative Cloud can provide insights into how designers influence the marketing process. Or, you can choose to integrate Oracle Eloqua with an ERP like Sage 100 ERP or SAP Business One to better understand how order processing trends can improve future campaigns. Data helps launch hybrid and omnichannel marketing campaigns Most marketing campaigns tend to take a hybrid approach, combining online with offline. A survey conducted by Vistaprint Digital showed that 29% of businesses ignore either offline or online marketing practices, favoring one practice more than the other. However, a hybrid approach that uses both the practices is always more beneficial. Some of the ways you can use data analytics to spur offline marketing success are by analyzing QR code usage, proximity marketing using Bluetooth technology, and tracking URLs and web traffic generated from offline visits to actual stores. Using data analytics to track these behaviors will help to launch more cohesive omnichannel marketing campaigns, which bring an integrated shopping experience to customers. Facebook and Google have come up with tools which help advertisers to understand the effect of online advertising on offline sales. They can predict and track online to offline conversions. Data is here to stay for the long haul As you can see, data tools have changed the game when it comes to marketing and utilizing media tools. While we are no longer reliant on traditional media platforms, and digital platforms have long become mainstream, data analytics has ensured that digital marketing will continue in a forward path in the months to come. All these trends will help agencies to develop and implement marketing and ad campaigns quickly across digital media platforms. Dissemination of marketing communication

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Five Ways to Transform Your Business through Right Digital Infrastructure

September 4, 2018

While most businesses have adopted digital infrastructure to some extent, many still do not have a holistic plan to transform their business using the right digital infrastructure. Studies show that most businesses tend to retain legacy tools while implementing the digital strategy, and what really happens is, there is a complete mismatch between digital and non-digital, leading to subpar organizational performance. In result, companies see results that are satisfying in some areas, while creating new risks and liabilities in others. Businesses need to have a 5-pronged approach towards digital infrastructure. This will help look at their entire business infrastructure as a single entity and build a strategy around it. In fact, the right strategy will help your business to transform itself into a lean and uber-efficient machine that can give your competitors a run for their money. Most importantly, having a digital strategy will help you to focus better on your business and enter new avenues. In this article, we describe our 5-pronged approach to make use of digital infrastructure in the right way and optimize your business to achieve new heights. Evaluate existing hardware and go for a major upgrade One of the first things you need to do in order to assess your hardware situation is to examine how your network infrastructure is. Network infrastructure includes both software and hardware components and is an integral part of IT infrastructure. For your software components to work efficiently, your enterprise network needs to be top notch. Evaluate existing routers, operating systems, network security applications, network operations, IP addressing, wireless protocols, etc. At the same time, evaluate data centers as well, as most businesses use subpar services which are often exorbitant. Consider seeking an external vendor’s help in choosing the right data center for your business requirements. To transform your business using the right data center, begin with creating a strategy. Implementing agile IT organization is crucial to this process, as is virtualization and cloud. Intel’s whitepaper recommends evaluating aging infrastructure by computing various metrics and KPIs. Please bear in mind that agile infrastructure can either be virtualized or nonvirtualized, and this solely depends on your organizational requirements. Nevertheless, virtualization is key to having a successful cloud strategy, which we shall discuss next. Move to the cloud whenever possible Today, you can practically move every legacy technology to the cloud and reap the benefits of reduced costs, increased efficiency, and access to technology which you previously didn’t have. Software as a Service (SaaS) helps you to access and use software programs which were probably out of reach for you if you are a small business. If you are a large business, SaaS is equally important to reduce dependency on physical infrastructure and keep your business agile and scalable. SaaS models offer pay-as-you-go schemes, which allow businesses of all sizes to scale or downscale depending on their situation. In addition, the cloud can also help you to access infrastructure via the cloud. Storage, data centers, and even networks can be used on an infrastructure as a Service (IaaS) model. Cloud computing helps businesses to eliminate organizational flab and grow lean and agile. If you are a service provider yourself, consider using PaaS (platform as a Service), which helps you to develop new applications and tools on cloud-based platforms, instead of having to invest in expensive platforms. In short, cloud computing provides the technological impetus required to make your business grow quickly. Integrate what you can With more businesses using tools to automate processes, ERP, CRM, and HRMS tools are almost an integral part of every successful business. However, they create unique problems of duplicate entries, repetitive manual exchange of information, and a continued lack of coordination between departments. Integrating these tools using available APIs is a popular method to reduce duplicate entries and increase automation. Most importantly, data can be shared between integrated tools, leading to richer insight and more accurate predictions. If you have an eCommerce business, for instance, you can integrate your ERP with your CRM, so that purchases made by customers online is immediately reflected the inventory department, which can replenish stocks automatically. The possibilities are endless, and such a heightened level of coordination is only possible when you integrate software tools. Before you decide to integrate, make sure that you have an integration strategy, and that you have spoken to vendors who will be able to do it for you efficiently. Integration strategy also involves training your staff so that they use the new interface effectively. In addition, you will also have to account in for security-related ramifications. Implement Internet of Things, Blockchain, and Artificial Intelligence These may seem like disparate terms often used by IT honchos, but they are very important for businesses of all sizes. IoT, or Internet of Things, uses sensors embedded in devices to intelligently communicate with servers and perform functions that ordinary devices cannot. These can further be connected to smartphones so that device-users have more control over how they interact with it. In a business perspective, sales and marketing teams can use IoT-enabled devices during promotional events, while logistics and product-handlers can use IoT enabled product tracking. Blockchain is another digital technology which can help businesses immensely. You can use smart contracts to ensure security, and distributed ledgers allow you to process transactions in a safe and secure manner. Blockchain has a number of applications for businesses, right from identity verification to automated approvals. Artificial intelligence is another emerging technology that has now become mainstream for business use. Regardless of the size of your company, you can use AI-enabled chatbots for customer service, social media management, and certain marketing tasks. These technologies are accessible, affordable, and easy to implement. Businesses only need to decide to embrace them before their competitor does. Focus on digital governance and security To ensure business success, it is not enough to have the best infrastructure in place. Digital infrastructure’s success depends on how secure it is against various kinds of threats, and how wisely you are

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Blockchain as the Newest RegTech Application – An Opportunity to Reduce Financial Institutions’ Burden of KYC

August 17, 2018

Regulatory challenges have often caused unnecessary inconvenience and delays within the financial services industry across the world. Compliances issues affect every financial service today, and many businesses have often paid enormous amounts in terms of fines, legal fees, and loss of business. The need for compliance and stringent regulations are necessary, especially in a world that is increasingly becoming prone to fraud, security threats, and cyber threats. Governments and regulatory bodies are right on their part to expect compliance from financial institutions, one of which is the ubiquitous KYC document (Know your Customer document). While financial service providers have meticulously collected KYC documents and ensured that they comply, the process has often been long drawn out, complex, and often mired with bugs and technical issues. Most KYC compliance happens digitally, and companies often repeatedly seek KYC from customers, often leading to opt-outs. Technology can help fix this issue for financial service companies, and one way is using RegTech. RegTech is short for regulatory technology and makes use of cloud computing technology delivered via a Software as a Service (SaaS) model so that businesses can easily process KYC documentation quickly and efficiently, at a lower cost. What is going to change RegTech even further is the use of Blockchain. In this article, let us take a look at how Blockchain is changing RegTech, and helping financial institutions to process KYC documentation quickly and efficiently. What exactly do the RegTech companies do? So far, companies that offer RegTech solutions have been working with regulatory bodies alongside their clients, financial institutions. By combining the goodness of cloud computing and big data, RegTech companies have made available sensitive information often required to validate KYC documents. Many RegTech companies have also used predictive analytics and big data to comb through previous regulatory failures and predict future risks that financial institutions should consider. Most RegTech companies have focused on creating analytical tools that sift through big data to pick sensitive information that could help financial institutions to comply better with regulatory authorities. RegTech companies offer solutions ranging from KYC validation to alerting money laundering activities and preventing cyber hacks and data breaches. Simply put, RegTech companies monitor digital transactions in real time and identify irregularities to prevent fraud and other risky events from taking place. Financial institutions alone cannot identify, predict, or avert these risks, nor will they be able to comply with all the regulations, including KYC processing. Using Blockchain for KYC processing Blockchain, which is a distributed database stored on a particular network, and accessible on all computers authorized to do so, is a technology that is picture-perfect for regulatory compliance. In Blockchain technology, every file is split into parts called blocks, and each block needs to be validated individually by the entire network. Smart contracts are based on this technology, and for a contract to be processed, all parties involved need to provide their digital signatures. As all data is encrypted, security is always ensured. In addition, as data is stored across a network and not just on a single computer, hacking or tampering with data is impossible. Most importantly, Blockchain data is immutable, and all changes made to the original database can be tracked. In the financial services arena, this quality is very important because customers simply cannot make changes to their financial history if something had gone awry previously. KYC documents can be processed in an error-free, encrypted, and automated environment, which simply is not possible in other technologies. RegTech applications using Blockchain can integrate both KYC and anti-money laundering steps for commercial usage, and this can be made available to companies both publicly and privately, depending on regulatory requirements. How Blockchain helps companies to reduce KYC burden Blockchain applications can be delivered as cloud-based RegTech apps via a SaaS model to financial institutions so that they can conduct their KYC operations to meet regulatory compliance. Let us take a look at how Blockchain can help financial institutions to reduce the burden of KYC: Identify and verify client information KYC requires financial institutions to identify their customers’ personal details such as name, address, nationality, birthdate, etc. Such basic data can be verified with the help of an identification card that is approved by regulatory bodies. Blockchain digitalizes information and validates such information by cross-verifying digital identities from various sources already available to the Blockchain. In other words, Blockchain not only helps customers to manage their digital identities, it also helps financial institutions to conduct basic KYC seamlessly. While KYC for individual clients using Blockchain is quite straightforward, it gets a little complex for professional entities. Professional entities require the KYC processing of directors’ identities, and other key persons (or corporations themselves) involved. Avoid risk by screening high-risk individuals Most financial institutions gain access to only the basic information of a customer. This basic KYC is not enough to avert risky situations such as money laundering, payment defaults, frauds, financial bankruptcy, etc. Banks can easily screen high-risk individuals if they subscribe to a Blockchain database that stores and validates information related to previous risky financial behavior. In addition, Blockchain-based RegTech apps can also predict future risky behavior by combining predictive analytics and big data with Blockchain. If a customer has had a questionable financial history, for instance, a Blockchain would confirm this to the bank or insurance company, which can decide not to lend a loan or approve an insurance claim. This mechanism can also help in averting money laundering and fraudulent activities, helping financial institutions to comply with regulations. Determine the inherent risk of customers A number of financial relationships require a much deeper insight about the customer or client. The KYC team will need to process questionnaires that probe negative press releases, criminal activity, political opinions and alliances, and a variety of other publicly available information. However, the KYC team simply cannot put all these unrelated datasets together and arrive at conclusions regarding the risk a customer poses. Regulators often prescribe the criteria for determining a customer’s inherent risk, and

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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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