AI data protection and BFSI compliance strategies are going to be commonplace in the future, according to a wide spectrum of industry experts.
Learn how to leverage machine learning to automate customer sentiment and emotion analysis using the latest techniques and algorithms. Discover the benefits of using customer sentiment analysis to improve customer experience and drive business growth.
Read the blog to discover key data analytics trends that one should watch out for in 2023.
Global healthcare supply chains are often besieged by a raft of problems. The healthcare supply chain issue is often visible, when the supply of urgent vaccines or medicines is disrupted due to various reasons.
Read the blog on major limitation of machine learning. ML can be immensely practical for several projects, although it is not always proving to be a good solution for some projects too.
Data enrichment enables businesses to maintain relevance to their users, with advertisements that are tailored to their requirements.
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.
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.
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.
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
