Category: AI & MI

Which-Events-Justify-Writing-a-Press-Release

Challenges In AI adoption In Traditional Financial Services Companies

March 7, 2022

In the financial world, good old banks running traditional core systems are facing an uphill task – how to navigate through an ocean of data to understand customer behaviour, and then use those insights to better their offerings. Welcome Artificial Intelligence (AI), which, unlike humans, can parse tons of data to help traditional financial service providers uncover new product and service opportunities, while also red-flagging anti-money laundering patterns, and identifying fraud. And it’s a trend that is catching on fast. The use of AI in global banking is estimated to grow from a $41.1 billion business in 2018 to $300 billion by 2030. Per a McKinsey Global AI Survey report, nearly 60 percent of financial services companies have been utilising atleast one AI capability. Currently, AI technologies in vogue include virtual chat assistants for customer service interfaces, machine learning techniques to support risk management and robotic process automation for structuring daily operations. Incumbent banks primarily have two sets of objectives to fulfil with AI. First, they aim at speed, flexibility and agility, inherent in a fintech. Second, they must adhere to compliances, standards and regulatory requirements of a traditional financial service company. However, deploying AI to do the heavy lifting isn’t as easy as pushing a button. In fact, big challenges remain in building responsible and ethical AI systems and simultaneously, traditional financial institutions struggle to deploy in-depth AI capabilities to truly harness its full potential.  Here are some key challenges global financial companies face in implementing AI Data quality and weak core structures Research finds that the existing data sets in circulation are mostly third-party, unstructured, and a lack of due diligence makes it difficult for AI and ML systems to identify overlapping and conflicting entries. Also, the existing control frameworks lack support for AI-specific scale and volume. Plus, the algorithm results can even show biased results when written by developers with a biased mind. For instance, A 2020 report stated that Apple cards give upto 20 times less credit to women as the decision AI was fed with an untested, historically biased data set. Clearly, the financial domain lacks a clear and ethical AI framework to ensure data quality and strengthen the core data structures. Lack of standard processes and guidelines A clear strategy for AI in the financial domain is the need of the hour. Presently, the inflexible, incompetent and weak core structures are bound with fragmented data assets, hampering collaboration between business and technology teams, further resulting in outmoded operating models. It is pertinent that traditional financial organisations consider the context, use case, and the type of AI model implemented to analyse the appropriate approach while collaborating or upscaling their core tech systems. Lack of talent AI adoption maybe the talk of the town, but surveys evaluating AI success rates reveal a not-so-happy picture. Per O’Reilly’s 2021 AI Adoption In The Enterprise report, 25 percent of companies saw half their AI projects fail. Analysis reveals that a key reason for that failure is the lack of capable talent and the ability to reskill in line with a long-term vision. To make things worse, too many firms see talent strategies as an administrative hurdle versus a strategic enabler, resulting in a lack of proper framework around hiring and reskilling in the AI domain. Budget constraints An omnipresent challenge associated with AI investment is determining the source of money. Will it be an IT project, a change management project or an innovation project? The definitive answer is all three, but only a small fraction of the budget is assigned to AI projects. But there is some good news on this front. With organisations gaining interest, The Economist’s research team found that 86% of Financial Service’s executives plan to increase AI-related investment over the next five years, with the strongest intent expressed by firms in the APAC (90%) and the North American (89%) regions.  The road ahead A significant commitment towards AI investment is the need of the hour with a clear focus on bringing in the required human resource capabilities to the front. Businesses that scale with AI over time, with an unwavering focus on compliance, customer satisfaction, and retention will be the ones laughing all the way to the bank.

Read More »
semantic-search-nlp

What To Know About Semantic Search Using NLP

January 12, 2022

Have you used your application or search engine to understand the underlying meaning behind your query? If yes, the solution to this requirement is through Semantic Search. A couple of years ago, a simple keyword search would have yielded search results matching just the keywords. We call it ‘lexical search’. Today, we can have machines and applications understand the semantics behind a query through Natural Language Processing (NLP). The credit goes to the Artificial Intelligence revolution. Let’s say you search the nursery rhyme, ‘Hey Diddle Diddle’ on Google. And the search results will return both lexical and semantic instances of it. The former is an example of computational information retrieval below semantic search. So, we can say that “Semantic search describes a search engine’s attempt to generate the most accurate Search Engine Results Page (SERP) results possible by understanding based on searcher intent, query context, and the relationship between words.“ Powered by Machine Learning Through the superset of machine learning, we have the following abilities today: Natural Language Processing (NLP): It is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language. It can be further divided into 3 fields: Speech Recognition – The ability of machines is not limited to discerning and distinguishing audio. But the machines are capable of cognitively understanding the meaning too.  Natural Language Understanding – NLU, deals with machine reading comprehension Natural Language Generation – NLG, process that produces natural language output The above machines help to ‘comprehend’ both intent and context of human communication. Imagine the positive impact that this emerging technological paradigm has had on global trade, academics, scientific research, and culture. After all, there are over 6,500 human natural languages all over the world! The best part of this technology is that both speech and text can use it. However, we would stick to the dynamics of semantic search alone. It involves a pre-processing data stage called text processing. This allows the understanding and processing of large amounts of text data. It is the process of analyzing textual data into a computer-readable format for machine learning algorithms. How to implement semantic search using NLP and what is a language model? A language model is a tool to incorporate concise and abundant information reusable in an out-of-sample context by calculating a probability distribution over words or sequences of words. The problem of NLP cannot be explained without citing BERT (Bidirectional Encoder Representations from Transformers) as an example of a state-of-the-art pre-trained language model. The bidirectional encoder representations from transformers can answer more accurate and relevant results for semantic search using NLP. Jacob Devlin created a well-known state-of-the-art language model in 2018. And Google leveraged in 2019 to understand user searches. There are many open-source frameworks for solving NLP problems such as NLTK, GPT3, and spaCey. We at INT. use those frameworks for engineering NLP-driven software. GPT3 (Generative Pre-trained Transformer- think GAN of NLP) was a wonder framework released in 2020 by OpenAI. It has the power to thrill and scare people due to its accuracy in mimicking human natural language. It used a transformer, which is a deep learning model that adopts the mechanism of self-attention, differentially weighting the significance of each part of the input data. NLP and computer vision (CV) primarily use the GPT3 framework. Its ability to differentially weight features works out terrifically for us as the model can discern different words in a sample. Also, it can assign probabilities of them occurring in the past, present, and future. Strategies for obtaining data for language models Language models such as BERT need a truly humongous amount of data in the targeted language to fine-tune its general understanding of the language. Data engineering is an absolute need for the accuracy of a language model. Crowdsourcing is one such strategy to get abundant data. The other way is to have an application/algorithm crawl through targetted or available resources on the internet.  Lastly, companies specializing in the required data for NLP can provide data for purchasing.

Read More »
artificial-intelligence-logistics

How Artificial Intelligence Is Transforming Logistics

October 29, 2021

With the real-time optimization of AI is transforming the transport, logistics, and supply chain industry. This has led to the minimization of wastage and overall increased the efficiency of delivery. Along with IoT, AI has opened new doors to efficiently manage operations and schedules, and gain end-to-end visibility. Artificial intelligence plays a vital part in the logistics business. It helps in course advancement and optimization, to anticipate requests, oversee organizations, and offer a customer-friendly environment. With AI turning out to be more open and accessible, logistics leaders are investigating AI’s potential for back operations and client confronting activities to decrease costs and improve productivity. Today, logistics organizations are looking at AI capacities for automating routine assignments, separating information and experiences, and collaborating with clients essentially. Artificial Intelligence manages tremendous measures of information accessible over the web to bring valuable and useful insights of knowledge from it. In logistics, these insights and experiences can assist organizations to make better decisions and comprehend future market requests as well as client assumptions and expectations. The leading logistic software solution provider ‘Shipsy’ uses advanced artificial technology in its logistics management software to offer its consumers 100% satisfaction. The interesting course improvement work furnishes drivers with the most proficient course to arrive at the end-client’s location. Utilizing the tracking link, sent from Shipsy’s LMS, end-clients can reschedule delivery as per their accessibility; this drops down the RTO. Shipsy has made it its critical role to bring to the table successfully and smooth running services to its clients which makes it one of the most mind-blowing and best software providers available in the market. Apart from this, businesses can also use self-driving vehicles that use the potential of artificial intelligence and other relevant technologies such as machine learning and computer vision for image, pattern, and object recognition. These vehicles can be utilized for conveying or delivering orders. AI can also help in improving customer experience by offering automated support services. The technology can also be used for automating warehouse processes, which is one of the crucial segments of logistics management. Day by day, Artificial intelligence is becoming a need in practically all businesses. In the supply chain and logistics industry, AI will assist organizations with anticipating changes in the market drifts, clients’ assumptions for the conveyance of their products, changing logistics trends, and more. Aside from this, the innovation can likewise be utilized to upgrade warehouse tasks and plan the productive courses for the conveyance. Indeed, even logistics automation needs to depend on artificial intelligence. The core logistics functions are frequently outsourced through outsider sellers (third party vendors) involving colossal measures of solicitations and receipts. Manual endeavours to deal with a large number of invoices yearly turn oppressive and cost-devouring for both organizations and employees. AI will shape the future of logistics. Artificial intelligence depends on a process of technological learning as a matter of fact and improving and better at addressing complex inquiries. Artificial Intelligence offers the potential for an impressive decrease in labour costs. For consumers, it implies getting more reliable information and customized offers, also extensive time-investment funds for everybody. Previously, logistics teams were only able to predict roughly the quantities of products to order to keep shelves fully stocked using inventory levels and historical sales data. These days, AI can develop a much more accurate picture of exactly what types of products, sizes, and colours are likely to sell, by looking at multiple scenarios in real-time (fashion trends, consumer behaviour, the weather etc.) and drawing on data from the internet. Few spaces that signify the utilization of AI in the logistics industry are as follows: Automating Transport Live Tracking Black Boxes Workflow and Process Automation Robots Better Demand Prediction Address Supply Chain Problems The main advantage of AI is better forecasting. One example of logistics use is a transportation system that takes in data from various sources (weather, routes, and forecasts) and recommends actions based on this information through the use of AI and planning parameters. Another example is from a supply chain visibility platform that is building machine learning and into its platform to give the ecosystem better predictive capabilities, such as predicting average wait time by location and day, better predictions of appointment times and dynamic ETAs. Chatbots have arisen as one of the primary devices to ease correspondence and the executives for associations of different verticals. Specialists anticipate that 85% of all client associations will be taken care of with practically no human inclusion. As different enterprises are utilizing Chatbots, the logistics and supply chain business has likewise discovered plentiful utilization of this outcome-driven innovation. An AI-based Chatbot can help a logistics company in two possible ways: Improving customer-facing operations Supply chain operations Chatbots meet all expectations to guarantee ideal client assistance. Chatbots play a vital role in logistics to help serve the customers better: Request a Delivery Customers can directly interact with Chatbots to place an order. The Chatbot typically handles significant order details like pickup and delivery location, rates, dates, and more like a human specialist. It can also deal with orders and send a receipt straightforwardly to the client’s email. Track a Shipment Utilizing Chatbots the drawn-out course of following things turns out to be easy, simple and straightforward. Clients don’t have to type the entire tracking number every time to know the situation of their purchase. Instead, they can simply ask the Chatbot and get help right away with all the following issues. The Chatbot will consistently recollect the details to provide context-based support for your client. As we see, Artificial Intelligence (AI) is now becoming a part of our daily logistics conversations. Today, AI is not just a nice-to-have; it’s imperative to stay competitive. Its increases the efficiency and visibility of the process and system and plays a vital role in improving the customer experience.

Read More »
AI-First-Bank

The Rise Of Next-Gen Banking- AI-First Bank

September 28, 2021

In 2020, when the world was devasted by the deadliest pandemic of all the centuries, we saw a rise in the adoption rate of digital banking services. As per the Mckinsey report, a 20 to 50 per cent rise in adoption was recorded. The expectation is, customers will continue cutting on their branch visits, and they will demand more advanced services similar to the ones they experience from other customer-internet companies. AI being an integral part of daily life, it becomes necessary to be AI-First to remain relevant to the changing market conditions.  Why AI in banking? A humongous amount of data is lying unused in the banking structure, and the banks deploy AI to realise the new opportunities from the unused “vast troves of data”. In an environment of “low-interest rates”, it becomes imperative for the bank to integrate advanced technologies. More broadly speaking, if we can list various reasons that lead banks to adopt AI, there are endless reasons. To stay relevant, to stay unique, to compete and compel the ever-changing, tech-savvy customers, banks need to deploy the latest technologies at scale.  How AI is changing the banking process? When we think of AI in banking, there are three areas where it has been deployed at scale: conversational banking, underwriting, and detecting fraud.  Conversational banking In conversational banking, banks leverage AI to engage with customers with chatbots and Robo advisors to recommend products that will be totally personalised. To deepen the customer relationships, AI is deployed at scale in the front office and structure.  Also, it helps in enhancing customer interaction and experience: e.g., chatbots, voice banking, Robo-advice, customer service improvement, biometric authentication and authorisation, customer segmentation (e.g., by the customised website to ensure that the most relevant offer is presented), targeted customer offers. Underwriting Enhancing the efficiency of banking processes on building smart contracts: e.g., process automation/optimisation, reporting, predictive maintenance in IT, complaints management, document classification, automated data extraction, KYC (Know-Your-Customer) document processing, credit scoring, etc. Detection of Fraud AI is also leveraged to detect and prevent fraud. It enhances the security and risk control: e.g., enhanced risk control, compliance monitoring, any kind of anomaly detection, AML (Anti-Money Laundering) detection and monitoring, system capacity limit prediction, support of data quality assurance, fraud prevention, payment transaction monitoring, cyber risk prevention. In the banking industry, adoption of AI is necessary but there are incumbents that bank faces. To remain market-relevant, banks need to be fast, agile and flexible to compete with their nearest competitors- fintech. Again at the same time, they need to maintain security, transparency, regulation and compel the new age digital customers with an engaging experience.  According to global consultancy Mckinsey, “Disruptive AI technologies can dramatically improve banks’ ability to achieve four key outcomes: higher profits, at-scale personalisation, distinctive omnichannel experiences, and rapid innovation cycles”. The Future is here The Future of AI will look more promising when they will cater to the next-gen customers with their highly intelligent recommendation engine that will automate regular decision-making tasks, recommend the right product/service and many more. The future AI bank will also attain customers with their personalisation touch. It will analyse the past behaviour, the present condition and recommend a product based on these data. Again, we will see more consistent experience across various offline and online channels. In this article, we have given an overview of how our future banking might look like. To have a deeper understanding and insight into how open banking can revolutionise the banking industry when it is powered by AI, take a look at our latest ebook. To download, click here.

Read More »
future-solutions-data-challenges-insurance-sector-banner-01

The Rise Of Top Two Data Challenges In Insurance Industry With Future Solutions

August 12, 2021

Every day, more than 2.5 quintillion bytes of data are generated that are mostly unstructured and “some 40% to 50% of analysts spend their time wrangling the data, rather than finding meaningful insights”. But, these data need to be analysed and tracked to assess risk and inform fraud by the insurance industries. Moreover, Big data is not anymore a buzzword rather, it has become imperative for the insurance industry.  With market dynamics evolving at a tremendous pace, insurance industries are expected to stay aligned with the data-rich world. But, Quantiphi report suggests that “80 per cent of data received by underwriters is unstructured”. Mostly these are in the form of forms, email, pdf and images. Therefore extracting meaningful data from it leads to a huge processing time which lowers the efficiency of the underwriting team. Thus with opportunities arises challenges! As big data is transforming the core concepts of the insurance sector simultaneously, it is also facing challenges that prevent experiencing the full potential of the data insights. Here are most faced top two challenges and their imperative solutions to deal with them: Challenge 1: Confluence of unstructured data and legacy system prevents from making actionable insights In a traditional insurance system, there is a barrier to seamless integration among different data depositories. It is often noted that each business has its own way of capturing data which they fail to communicate or share with other business units. Therefore, preventing insurance companies from realising the full potential of data.  Solution: Build an integrated single platform that integrates new and existing data sources and makes data actionable by leveraging advanced analytical tools Challenge 2: Deployed actionable data insights only for the product level and not at a customer level Often customer insights are lost in silos more because they are scattered across the functional lines of the process. Also, there is a lack of predefined terms on customer insights; thus, insurance companies fail to recognise customers at different stages of the policy life cycle. Also, other business units fail to convey the insight for a particular customer to the other business unit, which further leads to an increase in expenses.  Solution: Build customer-centric analytics solution for precise marketing, customer retention and increasing profit. This will make each business unit enhance the customer value across the policy lifecycle.  Data itself has no value. To become information it needs to be processed and analysed thoroughly. With data generation increasing at an alarming rate, it becomes important to deploy new strategies and tools to make data work through actions. Learn how INT., with its proposition, is aiding and reshaping the underwriting landscape with an intense focus on customer experience.

Read More »

Intelligent Machine Learning Model Is Making Us Rethink The Underwriting Process

June 3, 2021

Choose the right premium, build the right marketing campaign and elevate the value! – insurers are facing challenges maintaining a balance between providing enhanced customer experience and operating at the profit margin. When we talk about the underwriting process and the traditional method, we can see much human intervention and manual paperwork. It can only make the process gruesome! Reports suggest that the key challenges insurance companies face are binding the data and advanced technological capabilities into one to build value in the Insurance Value Chain. A formation of a successful strategy occurs when insurers can identify the business value generated by the ML and how it can be aligned across the business domain. Solutioning to this challenge, today, insurers are joining hands with software development partners to bring a radical change in the sector through early adoption. When we talk about the insurance value chain, we understand the end-to-end process from product development to underwriting and claim processing. As ML is an integral part of data science, so is underwriting for insurance. The ongoing crisis has reinforced the urgency to modernise the underwriting process. The companies that are adopting end to end digitisation of the underwriting process are the ones that are overcoming slowing down factors and modernising the customer journey in the underwriting process. Let us see how the analytics can be leveraged in the underwriting process from reporting to binding policy: Descriptive analytics: Claims are deeply studied and patterns are identified. Based on past historical data, descriptive analytics flags if any new trends emerge.  Predictive analytics: As the process moves, underwriters use predictive analytics to evaluate the pricing competitiveness. It also alerts the underwriter through its risk scoring and assessing model.  Prescriptive analytics: Further underwriter deploys prescriptive analytics to build a model based on the future economic scenario and predicts the future risk of the policies. It applies the advanced statistical model to recommend solutions such as automated underwriting in case of the most predictable risks.  Recently machine learning is leveraged in the underwriting process, thus we have deeply studied the customer journey in the underwriting process to understand how it has improved and provided a seamless experience. Based on it, we have allotted the data science model, which can be leveraged by the insurers to effectively understand the journey and use it to the advantage of it. Submitting the TIFF/JPEG format form: When insurers confirm about digitally submitting the claim documents, they mean they are submitting image format documents. Data scientist deploys tools and models to parse the data and build a structured form.  Analysing the risk: It becomes essential for the underwriter to get a granular view of the risk based on the historical risk and cost drivers. Underwriters are deploying a machine learning model. Based on the data generated from the social media platforms, historical data, and data from a third-party platform, the model assesses and scores the risk accordingly. Also, the classification model segments the customers based on their likings, motivation to purchase, etc., which further helps evaluate the risk and quote the premium well.  Reviewing Rates and options: The rates depend upon the actuaries, and actuaries rely on the risk scoring model. Predictive analytics plays a more significant role when quoting the premium. Collection of the correct data, such as the likelihood of rash driving, sickness, defaulting, and other external data sources, are essential for the risk assessment. After the evaluation, if the underwriters find that the claim outcome is within the risk parameters, underwriters can easily quote the premium without much complexity. Through predictive analytics, underwriters are empowered with confidence due to certainty of risk.  To minimise the underwriting risk, there should be well-defined risk parameters by the underwriters. Predictive analytics is providing statistical reliability and a stable rule-based method for improving pricing decisions. It is also helping insurance companies to perform well at the margin during adverse underwriting environments and at INT. we provide end to end guidance so that our partners effectively manage the dashboard and use the analytics built on an advanced technological model. Book us for the demo!

Read More »

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

Read More »
AI-Powered Autonomous Vehicles Header

The Rise Of AI-Powered Autonomous Vehicles : Forces Powering The Evolution In The Auto Industry

December 13, 2019

In the world of technology when we talk about Self-driving cars it’s often paired with artificial intelligence. Simply put, you cannot really discuss one without the other. Though AI is being implemented at rapid speed in a variety of sectors, the way in which it’s being used in the automotive industry is a hot-button issue right now. 2020 is going to be a big year for autonomous transport. Tesla has tentatively announced that next year, it will be “feature complete” when it comes to self-driving, meaning that a car can find a passenger in one location and drive them to another without any human intervention. With other big advancements coming in the industry, driverless cars may well become more popular and may as well define the next 18 months in the AI community.  Impact of 5G on Autonomous Driving Technology  Fifth-generation cellular network technology or 5G has already been rolled out in certain areas around the world in the past year. In 2020 it is expected, there will be more countries embracing 5G technology with supporting devices the following suit. 5G technology allows for faster speeds as it operates at a higher frequency in the wireless spectrum thus resulting in quicker processing with lower latencies. Not only this, but it also operates at a lower power compared to previous generations along with providing mass connectivity between devices. So you may ask, what does this mean for autonomous vehicle? C-2VX (vehicle-to-everything) will harness the power of 5G for vehicle-vehicle (V2V), vehicle-to-infrastructure (V2I) and vehicle-to-network (V2N) communication. Low latency rates coupled with the fact that the infrastructure is likely to be in place by 2020, 5G will be the natural solution to take autonomous driving to the next level. 5G will allow driverless cars to react to surroundings, identify obstacles and relay such information to the computers on-board in real-time. To estimate the potential of autonomous vehicles and to approximate a timeline of it become the norm we must map the journey from zero autonomy to complete automation in the following steps: Autonomy in respect to self-driving vehicles comes about in 6 stages: No Autonomy: Zero autonomy, the driver performs all driving tasks. Driver Assistance: Vehicle comes with some driving assist features but is still under control from the driver Example: Cruise Control, wherein the car can accelerate/decelerate autonomously to maintain a set speed limit. Partial Automation: Vehicle has combined automated functions, like acceleration as well as the steering, but the driver must remain engaged with the driving task and monitor the environment at all times. Example: Current gen auto-pilot feature on Tesla cars where the car autonomously accelerates and steers to maintain lanes. Conditional Automation: Driver is a necessity, but is not required to monitor the environment. The driver must be ready to take control of the vehicle at all times within notice. High Automation: The vehicle is capable of performing all driving functions under certain circumstances. The driver may have the option to take control of the vehicle. Full automation: The vehicle is capable of performing all driving functions under all circumstances. The driver may have the option to take control of the vehicle. In January 2019, Nvidia announced ‘Nvidia Drive Autopilot’, which seeks to bring AI-powered Level 2+ autonomous driving with AI-assisted smart cockpits, to mainstream passenger vehicles. It has received support from OEM’s like Mercedes-Benz, Volvo and auto parts suppliers like ZF and Continental. The Dubai Future Foundation, along with Dubai’s Roads and Transport Authority, has already launched the Dubai Autonomous Transportation Strategy. The strategy hopes to transform 25% of all transport in Dubai autonomous by 2030. Autonomous technology firm FiveAI hopes to start passenger trials in 2020 for driverless cars. Businesses are more likely to invest in autonomous vehicles before consumers. Hence, industrial vehicles such as tractors, bulldozers and others will be the first ones turning autonomous. They may now accomplish commercial tasks efficiently with higher accuracy in a shorter period of time as the element of human fatigue is no more a hindrance. There’s a valid reason for businesses to embrace this technology first. We witness a general sense of doubt in the eyes of public regarding the safety of this technology, however, if the industries lead the initiative they will be able to establish the successful real-life application of this technology and gain public confidence. Corporations doing research and development in autonomous driving technology – such as Google, can make use of the learnings and proofs-of-concept in other domains of their business. To summarize- It’s established that there is a lot of potential in the technology to cut costs, make roads safer both for passengers as well as pedestrians, take manufacturing efficiency to its peak. What is lacking is supportive infrastructure, which in the form of 5G network and support from automotive OEM’s seems promising. Second, safety concerns regarding this technology which will be eliminated once businesses set new benchmarks of its industrial application. The 2020s probably won’t bring flying cars, solar roads or robotic gas pumps, like much of our current world and technology this decade will see a blend of the amazingly futuristic and frightfully analog. Level 5 automation is still far from reality but we will inch closer to it in the coming decade.

Read More »

Conversational AI in Insurance: Nullifying false positives

July 23, 2019

Conversational AI problem statement: Operating as an Insurer for the modern-day, on-the-spot solution craving customers is a constant battle between passing out relevant information in the quickest possible turn around. As a thumb rule, insurers can’t keep their customers waiting and then pass on incorrect or unwanted information (false positives). While having a dedicated customer service team for this problem may seem like a tried and tested formula, it brings along its own set of business implications which can hinder profitability as well as brand reputation. Who are conversing through AI, and how? Research shows insurance companies across the globe are investing in conversational AI tools and solutions to overcome this problem, with the broad objective of delivering customer delight and higher recommendation rate. Conversational AI has already proven its merits amongst several leading insurers: • Tryg, a Denmark based insurance operator enjoys 97% resolution rates of all internal chat queries; the company has successfully enhanced the abilities of its existing customer support staff. The number of calls from support to back-office teams has also dropped substantially in just one year, post adoption of Conversational AI. • In a similar instance, Storebrand also claims to have achieved a major break-through in the usage of AI laced chat interactions. Presently, the company powers more than 70% of its chat interactions through AI-chat tools and zero human interventions; additionally, the company also registered a 162% increase in customer interactions through chat mechanisms. Even though Tryg and Storebrand are two distinct companies operating in separate geographies, they have a strikingly similar strategy of adopting the AI tools. Both the companies have adopted the AI as a complementary solution to their customer servicing team. The two insurance giants have actually deployed their chat tools as the ‘first line of defence’ for the ping traffic inbound. The tools generally soak up all the repetitive and mundane queries which suck up employee bandwidth, unproductively. These tools have been carefully planned, crafted and developed to delimit themselves from more serious and business critical queries, which are eventually transferred to more experienced and skilled customer service executives, owing to obvious reasons. Who is driving AI-based conversations? The conversational AI adoption trend is basically fuelled by the fact that customers, especially the millennials prefer fast, efficient and reliable modern communication over traditional channels such as emails and telephonic conversations. Insurers will have to address this sense of instantaneous resolution among millennials in order to carve a better market share among this cohort, which is fast becoming rich and finding ways to manage their finances. According to Accenture, in the next 30 – 40 years, millennials are expected to inherit wealth worth over USD 30 trillion in North America alone. This makes them the next important chunk of population wherein insurers will have to focus more often and create a groundwork necessary for the future to avoid becoming irrelevant and a tech laggard. How do you jump into the bandwagon? To kick off a Conversation AI adoption journey, Insurance players will have to begin from their drawing boards and slate down opportunities to improve the critical interactions customers have. Some important areas may comprise the provision of human support over and above AI support, positioning of the tool in the customer’s digital journey in a manner which highlights the scope of the AI chat assistant, induction of “pre-trained’’ insurance specific modules, deciding the key performance indicators for the solution and lastly, a reliable technology partner that can factor in all of the above and come up with a solution that can efficiently manage near 100% of all customer interactions and void of any false positives.

Read More »

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.

Read More »

Services

Industries

Company

Resources

Press Kit

MENU
Book a Free Consultation
CONTACT US

Let’s connect!

Loading form…

Book a Free Consultation
CONTACT US

Let’s connect!

    Privacy Policy.

    Almost there!

    Download the report

      Privacy Policy.

      Book a Free Consultation