Category: AI & MI

How AI and Risk Management Can Work Together?

How Can AI And Risk Management Work Together?

August 16, 2022

The AI risk management combination has been making waves in recent times. No, it doesn’t indicate any Man vs. Machine war in the future, or a takeover of the world by intelligent computing devices. What it does indicate is that AI (artificial intelligence) has been steadily rising up the ranks in terms of its applicability to varied functions. From personal assistants and self-driving vehicles to shopping, there are several functions backed by AI-technologies. In fact, AI-based models may help in training computers for the recognition and identification of risks and other complex scenarios. AI driven risk management is always beneficial for enterprises, helping in the smooth tackling and evaluation of data which is primarily unstructured, i.e. which does not fit into structured columns or rows. Cognitive AI tech including NLP (natural language processing) makes use of cutting-edge algorithms for unstructured data analysis. With estimates pegging 90% of business data in the unstructured category, cognitive AI may help in positioning enterprises better as compared to their rivals. Fintech players, banks, insurance entities, and other companies execute solutions for risk management with AI for enabling better decision-making, lowering credit risks, and offering customized financial solutions for customers. Machine Learning and AI For Risk Management- The Biggest Benefits From AI in credit risk management to overall enterprise risk management functions, there is a lot that can be accomplished in this regard. Here are some of the biggest advantages of using AI and ML for managing risks. Evaluating Security Threats and Their Management Machine Learning (ML) algorithms may help in the evaluation and analysis of data in sizable amounts from various sources. Real-time models of prediction created from this information enable security teams and risk managers to tackle threats swiftly. These models also double up as systems of early warnings and alerts, enabling seamless operations of enterprise, while boosting data protection and privacy alike. Lowering Enterprise Risks AI plays a vital role in enterprise risk management. It helps companies analyze unstructured information, identifying risky patterns, activities, and behavioral aspects throughout operations. ML-based algorithms may help identify earlier behavioral patterns of a risky nature, while transposing the same as models of prediction. Detecting Frauds AI-based models can help in lowering workloads for companies with regard to detecting frauds. These algorithms can help with text mining, social media evaluation, and searches across databases, while lowering IT-security threats considerably. Data Classification AI may help in the superior processing and classification of data as per pre-fixed classification models and patterns. It may also help in tracking access to the data sets accordingly. Management of Security for Events Using log data and specific events, teams can swiftly identify any risk triggers, patterns, and indicators. This helps enable better alerts and detection alike. Lowering Workforce Risks Workers in high-risk zones will benefit from the deployment of AI technologies. They can help in analyzing data linked to all activities in such environments, where accidents may become fatal or catastrophic. Through the analysis of behavioral trends before accidents, there could be predictive scenarios modeled for enhancing safety systems and reducing the risks of such incidents. Of course, there are still hurdles related to large-scale processing of data, especially in terms of its cost and also privacy-linked concerns. However, these may be ironed out in the near future, relying on ML and AI to become mainstream in the near future. This could be a shot in the arm for security and risk management teams across enterprises, lowering their workloads and scaling up process-based efficiencies considerably. About the author: Dipak Singh is a thought leader and data cruncher, currently, he heads the Analytics Wings at INT. To know more do check out his LinkedIn profile here.

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Sentiment Analysis

How Sentiment Analysis Can Drive Insurance Industry

July 11, 2022

Sentiment analysis in insurance is emerging as a potent tool for companies in multifarious ways. Insurance companies have tons of unstructured information that they have at hand.  Following a suitable sentiment analytics process may help insurers enhance retention of policyholders and also in the identification of opportunities pertaining to up-selling and cross-selling. Sentiment analytics have already turned into a vital aspect of strategies pertaining to customer feedback for companies of diverse sizes. Sentiment analytics in insurance fuse Machine Learning (ML) and Natural Language Processing (NLP) along with deep-text analytics for illuminating intrinsic nuances of texts.  Sentiments can be translated more easily and analyzed seamlessly than expressions. The sentiment analytics process is also known as opinion mining.  Customer data is unstructured and comes in several forms including claim data, voice messages, surveys, emails, social media posts. The entire system is tailored not only to analyze feedback and its nature, but also to put it against the right context. Benefits of Sentiment Analytics Insurers can reap multiple benefits from suitable sentiment analysis procedures. Here’s looking at some of them: 1. Detecting Fraud Reports indicate how insurers lose millions annually on account of fraud. These are estimated at anywhere around 5-10% of total compensation payouts by insurers in a year at least.  These are claims that flew under the radar. However, predictive analytics and other tools can help detect the same. A sentimental analysis dataset will help insurance companies track and assess insurance settlement and claim patterns.  It will help in quicker decision-making on the basis of crucial parameters or key performance indicators. This will help in arresting fraudulent claims and enhance the insurer’s earnings.  Text analytics also enables better decision-making through dashboards and access to other necessary data. 2. Customer Understanding  Social media sentiment analysis will help in the classification and identification of customer interactions on the basis of parameters like the services/products being provided, the marketing platforms or channels that are used, the operations in place and so on.  What sentiment analysis does is help insurers understand the voices of their customers.  It fosters superior customer understanding above everything else. Social media datasets will help in the identification of specific aspects concerning any product, process, or service.  Whenever this analysis is implemented for social media comments, it helps in clearly delineating trends in the industry and perceptions of companies along with enabling timely alerts on any reputation related issues as well. 3. Managing Claims The analysis of complaints and claims is another natural segment for using such datasets. Complaints may be automatically identified and classified on the basis of the service, product and other parameters.  This enables passing them onto suitable agents/departments in order to ensure swift action on the same. Relating those to real world Sentiment analysis in insurance reduces costs, combats fraudulent claims, helps insurance companies understand patterns, trends and customer preferences, and also lowers overall workload and the time taken to respond to customer issues.  Simultaneously, social media sentiment analysis helps in enhancing satisfaction levels of both employees and clients, while enhancing client retention, brand-building, recommendations.  It also goes a long way towards lowering indirect expenditure. Sentiment analytics can help insurance companies keep leveraging unstructured information for identification of revenue-enhancing opportunities and industry/customer trends.  Although analytics is not perfect as of yet, it is continually evolving towards the same. In this case, the sustained focus on a specific domain (insurance) can help in enhancing the overall accuracy levels as well. Indus Net Technologies offers an array of solutions tailored towards the needs of insurance companies and the industry at large, right from cutting-edge analytics and other technological tools to back-end automation, risk profiling, customizable analytics, and modernization of legacy applications.  Having worked on diverse task requirements for insurers over the years, INT has the ability to tailor industry and company-specific solutions that harness the power of data, free up company resources, and ultimately boost company revenues and growth alike. About the author: Dipak Singh is a thought leader and data cruncher, currently, he heads the Analytics Wings at INT. To know more do check out his LinkedIn profile here.

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AI – The Winner of Attracting Top Talents

May 5, 2022

Aside from being the biggest game-changer in multiple segments, it is undeniable that AI (Artificial Intelligence) has unearthed multiple use cases and possibilities today. Thinking along those lines, how would AI fare for recruitment? Let the discussion begin. Is it really what it’s cracked up to be? Job markets have been reeling globally in the aftermath of the COVID-19 pandemic. At the same time, the industry has been confronted with a surprising exodus of workers in the quest for something more meaningful in life. However, the shift hasn’t opened up a wealth of opportunities for aspirants. They’re finding it harder to crack jobs today. What could be the reason? Many organisations have been fine-tuning recruitment processes via artificial intelligence. By automating pre-screening for qualifications, checking credentials/certifications and scheduling interviews, employers are hoping to make recruitment procedures more efficient. In reality, these systems filter applications by screening CVs and cover letters for particular sets of keywords. The absence of the same in these documents is leading to the instant elimination of otherwise-skilled candidates. In short, if resumes aren’t being seen by human recruiters, then it poses an issue. With machines rejecting candidates on such grounds, companies face risks of missing skilled talent. Some AI systems even scrutinize gaps in resumes which could otherwise be explained by candidates. A Harvard Business School and Accenture report outlines how in 2021, 27 million people were hindered from finding jobs in their preferred sectors due to AI tools. The only probable solution is an expansion of candidate pools via algorithms, along with deploying lookalike matching based on the highest-performing talent. Humans are still indispensable in examining resumes and determining the best fit. How do candidates feel about faceless hiring? It is more than a mixed bag in reality; most candidates feel anxious about being able to find an audience with employers in the face of being scanned by AI tools. Many of them, however, testify to faster and more streamlined methods of recruitment for those with stronger CVs. AI capabilities can considerably fast-track communication, getting stronger applicants directly before potential employers. Other tools also help in accelerating onboarding, training, orientation and tech set-ups. Are automated hiring systems ‘hiding’ candidates from recruiters? As mentioned earlier, millions of workers are being instantly rejected or filtered out by AI tools owing to reasons such as the absence of specific keywords, gaps and so on. Automated hiring mechanisms sometimes reject genuine and skilled candidates as per several reports. These are hidden workers who desire employment but are being rejected regularly through processes emphasizing more on what they lack instead of their intrinsic value to an organisation. Immigrants, those with disabilities, caregivers, veterans, those who served prison sentences and those with relocating spouses are bearing the brunt of these mechanisms along with people in more categories. While the problem is clear, the solution lies only in a shift towards more positive or affirmative job filters by companies from negative filters when scanning resumes. These include the skills to be brought by candidates to any job position instead of focusing on not having experience, degrees and so on. Experts also recommend easier application procedures for drawing skilled talent along with clarity for applicants on when the company will respond. Use AI in recruitment but responsibly While AI usage in hiring procedures has accelerated over the last few years, responsible usage is the need of the hour. Companies are relying on AI for automated screening and evaluation, data analytics and virtual interviews. Yet, AI can hinder their access to skilled and genuine talent if they are not careful enough with their strategy. In the absence of historical data for training and equipping AI-based algorithms, recruitment tools will carry biases more predominantly than before. However, with efficient and responsible usage, AI can help in creating a wider, fairer and easier recruitment procedure as per industry watchers. Companies have to stop seeing AI as a quick fix while implementing it in a half-baked manner which does more harm than good. The onus lies on recruiters to ensure ethical, widespread and diverse usage of AI for hiring. It is a common perception that since HR departments do not directly garner revenues, leaders are more amenable to automation for cutting costs. However, at this point, there is a need to align human and technological resources for ensuring the best results. There are anxieties regarding the data collected by AI on candidates and regulations on management of the same. While addressing these concerns, companies should go all out to responsibly deploy AI tools. Some are taking the right steps by using the technology to find problematic content in JDs and other briefs, ensuring inclusivity and gender neutrality. AI is also being used by many companies to help new employees get access to swift onboarding systems and organisational information. Instead of replacing human beings entirely, AI can be a potent tool for helping them work more efficiently, thereby saving on costs and time in the long run. Some companies, for instance, are looking at AI tools to only identify applicants based on specific skill sets, without looking at conventional education, name, gender, etc. A double-checking mechanism may also work as a hand-holding measure till AI algorithms also evolve in response to multi-faceted requirements. As can be seen, AI in recruitment is still a mixed bag with a lot of fine-tuning and streamlining needed. Going forward, one can remain hopeful about the responsible, ethical and efficient usage of AI to transform recruitment procedures but not in a chalk-and-cheese manner that leaves little scope for understanding, interpretation and opportunities in many cases.

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The Tech Opportunity In Indian Healthcare Services

April 4, 2022

The marriage of Indian healthcare with technology has been a productive one, with both parties anticipating a never-ending honeymoon ahead. If there were ever a metaphorical statement for the rapidly growing health-tech segment in the country, then this would be it. In fact, even NITI Aayog agrees, based on the clarion call given by its CEO, Amitabh Kant, highlighting the growing health-tech opportunities to the Indian healthcare system.   Governmental Innovation Is Propelling The Sector The Indian Government is laying a steady foundation for the growth of digital healthcare and newer platforms. The Ayushman Bharat Digital Health Mission has been a game-changer and Amitabh Kant, the NITI Aayog CEO, stated that it is now on the technology players, start-ups, and healthcare players along with other stakeholders to create new offerings in the field of digital health which meet growing demand and spur the same as well. Amitabh Kant’s statements came at the 8th Annual Summit of Nathealth and assume greater significance once you consider the backdrop. The country already has the infrastructure to create “compelling, accessible healthcare solutions that provide equitable access and can be rapidly deployed and scaled up” as per Kant. Take other factors into consideration like the increasing penetration of internet connectivity and smartphones throughout the country and the increasing trend towards e-pharmacy, telehealth, and digital healthcare solutions during the COVID-19 pandemic, and you get the picture. Digital healthcare or health-tech presents a massive opportunity for growth, particularly in still-nascent segments like technology-driven home healthcare, e-diagnosis, and e-pharmacy services. Conventional healthcare institutions, investors, and start-ups would find this the right time to enter the space and “build a position which would be hard to beat in subsequent years” according to Kant. Now take the National Health Policy of 2017 into context. It creates a roadmap for creating a digital health-tech-based ecosystem and integrates various aspects like health delivery, cloud, wearables, and IoT (Internet of Things). It also envisions a National Digital Health Authority for the regulation, development, and deployment of digital healthcare solutions throughout the entire care spectrum. The policy recommends deploying digital solutions for greater efficiency of the entire healthcare setup along with better outcomes, in addition to ensuring a healthcare information system that caters to all stakeholders. The aim here is to ensure superior outcomes in terms of quality, access, reduced disease burden, affordability, and better tracking of health-based citizen entitlements. Some other Government initiatives that have struck a chord include the following: The National Health Stack concept, which became the National Digital Health strategy and the final National Digital Health Mission, launched on 15th August. Integrated health data and information portal with the aim to integrate EHR within the purview of the medical setup. Pradhan Mantri Jan Arogya Yojana 2.0 IT portal which wishes to integrate insurance and provider platforms for various benefits. Every individual will have a health ID, offering access to integrated healthcare solutions, enabling Universal Healthcare coverage and delivery. How And Why India Is Bullish On The Health-Tech Opportunity? Consider a few facts in this regard: E-health services and similar platforms may completely revolutionise healthcare. 65% of current e-commerce users are projected to use digital healthcare offerings in the future. Nathealth created its vision paper which emphasised Rebuilding, re-structuring, and re-imagining resilient healthcare systems in India in a post-pandemic era. The clear takeaway is that the pandemic ushered digital healthcare into the mainstream and consumers now consider it a necessary service. KPMG reports indicate a valuation of INR 116.6 billion for the digital healthcare sector in 2018 while this is anticipated to touch INR 485.4 billion by the year 2024, indicating a 27.4% CAGR (compounded annual growth rate)  in this period. With face-to-face interaction going down, patients are increasingly opting for online services in healthcare, with a demand for solutions that enable more affordable healthcare consultations and accessible interfaces. The digitalisation of the healthcare space is helping in filling up availability gaps in Tier-II cities and rural zones. E-Pharmacies have also helped in transparent price listings and better consumer options along with better accessibility. KPMG estimates this opportunity at a whopping $30 billion in healthcare technology. It has also talked about how start-ups will play vital roles in enabling healthcare access throughout the country. Estimates of 70% of the population of India (roughly 892 million individuals) living in rural zones with limited/zero healthcare access and the fact that India spends just 4.7% of the GDP on healthcare, throw up the magnitude of the opportunity. KPMG encourages start-up hubs for encouraging more players to invest in the health-tech space and advocates national and local Governmental support for the same along with a health innovation fund. The biggest pharmaceutical players, hospital brands, and diagnostics brands should adopt a mentorship role and sync with these health-tech companies. The market size was estimated at $830 million for telemedicine in India (as of 2019). It is projected to shoot up to $5.5 billion by 2025 (indicating a 31% CAGR). The NITI Aayog and Ministry of Health and Family Welfare have already released their telemedicine guidelines, with more than 1 million consultations taking place by December 2020 via e-Sanjeevani in 550 Indian districts. Health-tech in India grew by 51% (annual) in 2021 as per Redseer, collectively encompassing consultation, pharma, and diagnosis. 47% is the growth in the NPS (Net Promoter Score), indicating how customers are more inclined towards using e-health platforms and are clearly recommending them to their loved ones. The Redseer report also highlighted how the average consumer acquisition cost had reduced for players, indicating scope for growth and profitability. E-Pharma still dominates this segment owing to rewards and discounted offerings. Redseer estimates acceleration in GMV to $9-12 billion by 2025 for the e-Health space and possibly $40 billion GMV by 2030. The Take-Aways (What Is Happening And What Can Happen?) Indian mainstream healthcare is at the tipping point of future-proofing itself through technology, while meeting rising demand via technology. These are the core takeaways that we need to keep in mind. Indian healthcare industries

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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.

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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.

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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.

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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.

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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.

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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!

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