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You call artificial intelligence and machine learning magic. Your friend, on the contrary, deems it as just another revolution – devouring some jobs, flooding with a double of new jobs. While this debate continues in the chorus, PwC’s global AI study says that the global economy will see a boost of 14% in GDP by 2030, amounting to a potential increase of $15.7 trillion.

And why not? This technology has reshaped the market, introduced Alexa, got Netflix to give you binge-worthy recommendations, eased the effort you put into communicating with a customer service agent – and recently, once again – made headlines with ChatGPT.

There are a number of emerging trends in AI and machine learning which potentially have or will impact the way industries thrive and sustain.

If you are an aspiring tech professional, it’s both important and exciting for you to tap the possibilities these star technology terms have for you. That said, read about the hottest trends in AI and ML in 2023 and how they are fueling business growth.

But before that, we’ve got something you won’t want to miss: an amazing opportunity to learn more. Join us and embark on an incredible journey of knowledge and growth. We’re calling all data science and AI enthusiasts to join us at the DataHack Summit 2023, happening from 2nd to 5th August at the NIMHANS Convention Centre in Bangalore. Experience a thrilling event with hands-on learning, industry insights, and networking opportunities. Don’t miss out on this incredible gathering of data-driven minds!

Table of Contents What are AI and Machine Learning?

Artificial intelligence refers to computer systems or algorithms that can simulate human intelligence and mimic cognitive functions, including problem-solving. As the term suggests, “artificial intelligence” is a human-like cognitive ability. This implies that specific algorithms and systems can “learn or comprehend beyond what has been told” independently if provided with data and a set of instructions. Recommendation engines like Spotify and virtual assistants like Apple’s Siri are popular examples of this technology.

Machine learning is an area of artificial intelligence that allows a computer system to predict and decide by extracting information from structured and semi-structured data. It uses data to create models that can be used to perform certain tasks like predicting sales. Image recognition, Google translation, and auto-friend tagging suggestions on Facebook are everyday examples of machine learning.

Differences Between AI and Machine Learning

Before we explore emerging trends in AI and machine learning in 2023, let’s be clear on one fact: AI and ML are not the same— they share eminent differences.

It is simple. AI is present in a variety of applications that mimic humans, and ML enhances the reasoning power of such applications. Simply, AI is a broader concept.

Nevertheless, you will often find these two terms being used together. More often than not, you will find them working together. Take search engines as a testament to this. When you type something in the search bar, it’ll use machine learning algorithms to predict what you may want to search.

Check out this table to read the straightforward differences between AI and machine learning:

Artificial Intelligence Machine Learning

Artificial intelligence (AI) refers to the ability of computer systems to perform tasks that require human intelligence. Machine Learning (ML) refers to the use of data and algorithms to learn and adapt.

It is focused on decision-making. ML is focused on learning using machine learning algorithms.

It aims to develop computer systems that can plan, interpret, learn, and decide like humans. This aims to learn by creating its own algorithms.

It uses structured, semi-structured, and unstructured data. This uses structured and semi-structured data.

AI requires minimum human intervention. In ML, human expertise is required to train algorithms.

Siri, translation software like Google Translate, Google Assistant, and chatbots are common examples of artificial intelligence. Recommendation engines, Facebook friend suggestions, traffic alerts, etc. are everyday examples of machine learning.

Emerging Trends in AI and Machine Learning Natural Language Processing (NLP)

Natural Language Processing is one of the popular trends in AI and machine learning in 2023. It is an AI technology that makes monotonous language-based processes smooth sailing. The technology eradicates the necessity of manually typing content by capturing human language using algorithms that interpret, manipulate, and output automatically.

Today, businesses take a hand from NLP applications, such as language translation, text extraction, and sentiment analysis. AI and ML experts are working on various interaction approaches that are no different from that of a human, as it may help them explore the potential of NLP. Businesses in different sectors are tapping their AI-driven prowess to enhance a number of functions.

Banking and Finance

Banking and financial institutions use NLP applications for customer management and document search. For example, HDFC and ICICI bank utilizes NLP for robust customer engagement via chatbots. This helps the banking professionals to understand the client without them being physically present.


The healthcare sector can save time spent on clinical documentation, speech recognition, and interpreting clinical data with the help of NLP solutions. Computer-aided coding (CAC) is another area where NLP is significantly used in the healthcare industry. It comes in handy when certain patients need personalized health solutions. IBM Watson’s NLP capabilities, IBM’s AI engine, were used for healthcare management at the Memorial Sloan-Kettering Cancer Center.


The manufacturing industry is embracing this technology by providing solutions like task automation, quality control (by scanning data to identify patterns), maintenance & repair (by analyzing sensor and equipment data), and predictive maintenance. For example, the European Union (EU) plans to incorporate NLP in studying building information to enhance the efficiency and productivity of the construction industry.

Other real-world examples of NLP in action include Chatbots. The chatbot market is estimated to reach from $40.9 million in 2023 to $454.8 million by 2027. Apart from chatbots, Alexa, Google Assistant, and Siri are the iconic names in the world of NLP.

Computer Vision

Computer vision is a branch of AI that allows computer systems to derive insights using visual data and images and act accordingly based on the information. In simple words, just as AI enables computers to mimic the human brain, computer vision helps them to “see.” As a result, computer vision works quite similarly to the way the human eye does. Human vision uses information based on visually perceived data. The machine uses visual data through algorithms, videos, and images. The data is then parsed and segregated into different categories.

Source: Appen

The global computer vision market is estimated to amount from $9.45 billion in 2023 to $41.11 billion by 2030, with a CAGR of 16.0% during the forecast period. Some use cases:



Computer vision has changed the way doctors analyze cancer detection, X-Ray analysis, and CT scans. While doctors still manually check diagnostic results and read reports, computer vision does its fair share of jobs by automating various tasks like analyzing images. For example, the UK NHS specialists use the NVIDIA DGX-2 system in their radiology operations.


Construction business is one of the fastest ones to adopt computer vision – and do it fondly. Many of the crucial tasks like workplace hazard detection, asset inspection, and monitoring machines and equipment for maintenance requirements are the ways in which the industry has leveraged computer vision.

Apart from these use cases of computer vision, retail is the one to watch out for. Computer vision simplifies tasks in the retail industry by performing inventory scans, notifying stock-outs, and helping people self-checkout, which is ultimately improving customer experience.

Edge Computing

This is a concept of distributed computing frameworks bringing computing and the source of data closer to each other. Edge, here, means processing data at or near its source – which enables faster speed and results. With edge computing, data is processed in real-time, locally, and closer to where it is generated. This approach reduces the latency and bandwidth required for transmitting data to a centralized location for processing.

Source: Wikipedia

It has become a huge market now, and its global revenue is expected to reach $59,633 million by 2030, at a CAGR of 21.2%. Automation in retail and autonomous robots are the common use cases of edge computing.


For manufacturers, edge computing is leveraged to analyze and filter data, sending only the relevant information to the server in a cloud or on-site. This enables manufacturers to monitor all the information and assets. Microsoft Azure IoT Edge is a widely used platform that helps manufacturers run AI and machine learning algorithms on IoT devices using edge computing.

Remote Workspaces

Edge computing is widely used for remote working arrangements to increase efficiency and bandwidth. Especially after the COVID-19 pandemic, many companies are using platforms like the Google Cloud Platform, ADLINKS, etc., to leverage edge computing functionalities.

Oil and Gas

With time, machines have evolved, and the volume of data and information has increased significantly. It all boils down to one major demand: keeping up with the pace and efficiency. Edge computing is helping industries achieve the same.

Deep Learning

Deep learning, a subsection of machine learning, refers to a machine learning technique that helps machines perform tasks like humans. The technology is based on artificial neural networks (networks with multiple layers of processing) that extract more accurate features from complex data.

Deep learning is garnering popularity lately for many reasons, primarily because of its multiple (even hundreds) processing layers. These models bring about accuracy that can even surpass that of humans at times.

Source: Built-in

Deep learning has changed the way humans think, decide, and act, given the privileges it provides. And that’s why businesses are enjoying a good time unleashing their offerings. Here are some of the most prevalent ones:

Autonomous Driving

Self-driving vehicles largely employ machine learning models based on CNNs (convolutional neural networks). These models identify and classify objects, like zebra crossing, road signs, etc., and learn from them. Using this learning, they develop programs for autonomous driving vehicles.


E-commerce platforms provide tailored experiences to customers based on their past purchases and browsing history. Alibaba, the largest e-commerce marketplace, uses deep learning to recommend products to customers as per their browsing history.


OTT platforms are thriving, and easy accessibility is the major factor contributing to their success. To boost user experience, streaming apps are implementing deep learning. Netflix, one of the leading streaming platforms in the world, uses deep learning algorithms to analyze the tastes and preferences of viewers.

Explainable AI

While AI gives you the output, Explainable AI gives reasoning behind it. Defined as a set of methods/ processes. Explainable AI makes the results created by machine learning algorithms of AI understandable and reliable to users. It is interpretability that allows humans to understand the information a model offers, what it is learning, and why it is generating certain results.

Source: Birlasoft

Explainable AI has a stronghold in today’s market space as businesses are indulging in AI and ML and want these models to be transparent and trustworthy.

Explainable AI enhances transparency and fairness and also improves the accountability of AI systems. It helps the user understand the explanation for a particular prediction or reasoning behind the decision made by ML models. Here are some of the common use cases of explainable AI that exemplify its usage in different sectors:


In healthcare, explainable AI can help medical professionals explain the diagnosis to the patient and help them understand how a treatment plan will work. It can also be helpful for medical imaging data for diagnosis.

Autonomous Vehicles

Autonomous vehicles are trained with the help of explainability techniques, which incorporate human-readable descriptions in order to explain the reasoning behind a prediction.


Another prevalent example of this is in the Human Resource domain; explainable AI can be helpful in explaining the reason behind a particular status of the job application.

Moreover, Explainable AI systems in the banking sector help with explanations for the approval or rejection of loan applications. These systems are useful in every AI-driven business that involves factors like accountability and reliability.

How Emerging Trends are Impacting Businesses and Industries

From self-driving cars to virtual makeup try-on, the most exciting technological events are happening in this century! These emerging trends in AI and machine learning in 2023 are “revolutionary” by all standards – no matter the industry. They are helping businesses scale and are opening the door to more opportunities. Moreover, they are eliminating the distance between the workforce and efficiency.

54% of executives claim that AI has brought increased productivity to their desks. Because why not?

AI and ML in Healthcare

Source: Bernard Marr

Healthcare systems have the potential to make a significant change for people, save lives, and save money. That said, it is one of the major hubs where AI and ML trends are to thrive. Several business giants, including Microsoft, and startups, have already commenced the development of healthcare tools and processes using deep learning, natural language processing, and explainable AI to aid the system. Research predicts that the global AI market in healthcare will flourish at a CAGR of 37.5% between 2023 and 2030.

In healthcare, diagnosis is the most notable use case of AI and ML in 2023. Technology is helping doctors identify diseases and interpret diagnoses. Machines can now read reports and diagnostic tests to identify the issue. Healthcare professionals also take a hand from wearable technology to gather real-time data. Another prevalent use case of AI and ML in healthcare is personalized treatment. By interpreting large sets of data, the technology helps professionals get precise prescriptions for the patient.

Real-World Examples of AI and ML in Healthcare

Here are a few examples of AI and ML in action in the healthcare industry.

The world-famous Mayo Clinic undertakes robotic surgeries in its urology and gynecology departments. They use the da Vinci System with robot-assisted devices.

The Hospital for Sick Children, Toronto, uses an AI-based MendelScan tool to analyze historical patient data.

AI and Machine Learning in Finance

Source: Neal Analytics

Banks and financial institutions have a lot to gain from the current AI and ML trends. The technology will not only help boost customer experience but will also allow the industry to reduce costs. According to research by Autonomous Next, banks will be able to minimize costs by 22% by 2030 with the help of artificial intelligence technologies, which will help them save up to $1 trillion.

The credit score report is a common use case of AI and ML in the finance sector. The technology has simplified the entire journey of a user checking their credit score online. Every day, millions of individuals want to know the whereabouts of their credit health, and with a mathematical model, it is no longer a challenge. Another predominant use case is a personalized experience. Natural language processing is helping banks and financial institutions to improve customer experience by providing them with tailored services, such as personalized offers, chatbot services, etc.

Real-world Examples of AI and ML in Finance

Here are some examples of AI and ML in the banking and financial sector.

Wells Fargo, a famous commercial bank, uses AI-powered chatbots to provide account information.

AI and Machine Learning in Retail

Source: E2E Network

In retail, success is mostly a matter of pace. The industry is employing techniques and implementing AI and ML solutions to boost productivity and stay ahead of the competition. AI and ML solutions are helping this sector with operations and costs by optimizing business processes. The stronghold of technology is such that AI services in retail are forecast to amount from $5 billion to over $31 billion by 2028.

Real-world Example of AI and ML in Retail

Taco Bell introduced a seamless way to order food through Tacobot. This AI-driven solution allows customers to order in larger quantities through a simple step – texting. The bot is integrated with Slack, which makes it super easy for customers to type and order!

AI and Machine Learning in Manufacturing

The manufacturing industry is yet another arena where the rising trends in AI and ML bring significant contributions. In fact, 43% of manufacturers have employed data scientists in their workforces, and 35% are planning to do it within the next five years. Moreover, a study by McKinsey reveals that manufacturing companies implementing AI have welcomed revenue and cost savings. While 16% of the companies surveyed witnessed 10 to 19% drop in costs, and 18% noticed up to 10% boost in their revenue.

The trends in AI and machine learning in 2023 are also redefining the management standards for the manufacturing industry. First and foremost, manufacturers can now monitor the areas of their operation in real-time – it solves many challenge spots, including resource allocation.

Real-world Example of AI and ML in Manufacturing

The BMW Group mobilized image recognition to perform inspections and run quality tests. At the crux, the emerging trends in AI and machine learning in 2023 are paving the way to effectiveness, traceability, and monetary relief for manufacturers.

Source: Built-in

How to Stay Ahead of the Curve with AI and Machine Learning

It can be tempting to take the plunge for a full-fledged AI and ML implementation. But more often than not, businesses find themselves encountering ambiguity in planning and road mapping. The most important parameters that can make or break a plan are: onboarding the right people, identifying and addressing the challenges, and keeping operations in alignment with ethics and responsibilities.

Hiring and Training for AI and Machine Learning

Before you make those AI and machine learning trends in 2023 work for you, find the right people who know how to make them work.

The most popular and in-demand job roles in AI and ML include data scientists, machine learning engineers, and big data engineers. The expertise and the number of people a business needs to hire depend on the project and what it is that it seeks to achieve or solve.

Businesses must also emphasize training new hires for AI and machine learning. It is crucial to ensure that the team is both innovative and analytical. Apart from that, it is imperative to have a dynamic AI and ML culture within the business environment. It means being open to creating a diverse team and getting familiar with the data-driven culture and a flock of tools.

Challenges and Solutions in Implementing AI and Machine Learning

Companies planning to introduce AI and ML to their functions are faced with unexpected challenges and encounters. These challenges include the identification of the right data, budget requirements, data, and privacy. Moreover, hiring the right people, integration with existing systems, and complex AI/ML algorithms also pose a roadblock for companies.

In order to overcome these challenges, businesses need to define their goals and priorities. It is critical to be familiar with different technologies that fall under the umbrella of AI and machine learning and how to use them. Here’s how businesses are using these technologies:


The social media giant uses DeepText to understand and interpret the sentiments of posts. It also uses DeepFace technology that helps the platform automatically identify your face in a photo.


IBM has always been bold with the implementation of new technologies in AI. The introduced Project Debater. It is the first AI system that is capable of debating complex subjects and can help people make arguments.


The company means it when it states, “AI in All.” Tencent is all into incorporating AI in its operations to develop products catering to a variety of customer segments, including gaming, live streaming, and payments.

Ethics and Responsibility in AI and Machine Learning

Ever since its emergence, the technology has intrigued the world in some way or another. At the same time, there have been some landmark cases where AI went wrong and sparked a big question about its future.

Microsoft Uber

Another real-world example of AI gone wrong comes from Uber, which became newsworthy when its self-driving car hit a pedestrian in Arizona. A lawsuit was filed against the autonomous car, which was no less than a beacon warning the world about the mindful use of technologies.


AI and ML may be more efficient, but they are not humans. Businesses across all industries much consider ethical concerns and abide by the safeguards to minimize any collateral damage.


Today, artificial intelligence is nearly a $100 billion market, which will be twenty times bigger by 2030. These emerging trends in AI and machine learning in 2023 are setting the trail of automation, accuracy, and experience that businesses can thrive on. If we talk about mainstream technologies, then deep learning and NLP have already established a stronghold, decking up customer experience and allowing businesses to scale more. These fiercely burgeoning trends in AI and ML in 2023 are not far from cracking into more businesses in the coming years.

It’s only a matter of the right knowledge and the right implementation at the right time.

If you’re ready to equip yourself with profound learning on AI and ML, then perhaps the AI & ML introductory course by Analytics Vidhya is your guide. Curated by industry experts with decades of experience in the field, this course discusses various questions and topics for which you may be scouring an answer.

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Frequently Asked Questions Q1. What’s new in AI and ML?

A. Multimodal learning is a nascent area of research in AI and ML. Businesses are investing in multimodal learning, a type of learning that allows algorithms to process, interpret, and support multimodal data. Unlike traditional AI systems that only focus on a particular task (for example, speech recognition), multimodal learning enables algorithms that can perform multiple tasks (for example, textual, visual, and speech recognition) simultaneously.

Q2. Why AI and ML are booming right now?

A. Constant developments in neural network systems, the availability of data, and the emergence of multimodal algorithms have contributed to the rapid boom in artificial intelligence and machine learning. Moreover, as businesses expand, they generate and necessitate more robust data mechanisms with higher computing power. These technologies offer more material efficiencies in computing.

Q3. What is the trend in AI and machine learning in 2023?

A. Terrific growth in automation across different business sectors, implementation of edge computing to improve efficiency, and computer vision are some of the topmost trends in AI and machine learning in 2023 that the market will be watching out for.


You're reading Emerging Trends In Ai And Ml In 2023 & Beyond

How To Use Ai And Ml Tools For Hr Management In 2023?


In the words of Jeanne Meister, a renowned writer and co-author of The Future Workplace Experience, “AI will augment HR and give HR time to work on more strategic business issues. The opportunity is to use AI to streamline HR manual processes and provide a more consumer-grade service to employees.”

In late 2023-early 2023, HR technology experienced a surge as companies realized the importance of effective HR operations and overall employee well-being for success. Consequently, employee mental health, engagement, and retention are crucial performance indicators. In 2023, as the world adapts to post-COVID remote working culture, some trends that emerged during the Covid epidemic will continue. For instance, hybrid working is here to stay, and HR Tech start-ups that can assist businesses in better managing their workflows using ML and AI will continue to succeed.

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Table of Contents

Over the last few years, AI and ML tools have been used in the human resource industry to facilitate decision-making, daily operations, smoother onboarding, and a more efficient hiring process. Below is a list of ways in which AI and ML tools for HR have revolutionized how HR professionals work.

Better Job-Candidate Matching: By analyzing resumes, job descriptions, and candidate data, AI and ML can assist HR departments in more effectively matching potential candidates to open positions.

Automation of Daily Tasks: Automating repetitive operations like resume screening, candidate sourcing, and interview scheduling is something that HR departments can do with the help of AI and ML. This free time allows HR specialists to concentrate on more strategic tasks.

Predictive Analysis: HR data analysis using AI and ML can spot trends and patterns like turnover rates, employee engagement, and performance. Making data-driven decisions that enhance HR procedures and results can be done with this information.

Better Employee Engagement: Employee sentiment and engagement data can be analyzed using AI tools for human resource management to pinpoint areas where staff members are unhappy or disengaged.

AI and People Analytics

AI and people analytics are two closely related fields that organizations can benefit from. But before understanding how AI can aid people’s analytics, it is crucial to understand the latter.

People analytics is the conjugation and utilization of talent data to enhance essential business and talent outcomes. The history of this domain may date back to the 1910s when a book called “The Principles of Scientific Management” sought to normalize increasing productivity and optimizing tasks by measuring how employees worked. Fast forward to the next century, companies are still doing so, just a tad bit differently.

AI and ML tools for HR can be used to automate this task by analyzing massive amounts of candidate data and shortlisting the most relevant candidates for a particular job. For example, AI can analyze employee data to identify patterns and trends related to employee turnover, performance, and engagement. This information can be used to create predictive models to help HR departments make more informed decisions about hiring, training, and development.

People analytics leaders allow HR executives to create data-driven insights to guide hiring decisions, enhance workforce operations, and foster a pleasant work environment.

Tool Key Features Price

Pymetrics – Candidate-job matching. Publicly undisclosed pricing policy

AmazingHiring – Outreaching features like email and messaging templates Approximately $4800 annually, based on recent price indications

ICMS Talent Cloud – Integrations with other HR platforms Approximately $1,700/month

Oracle Recruiting – Cloud implementation Publicly undisclosed pricing policy

Skillate – Insights and analytics Publicly undisclosed and non-restrictive pricing policy

Textio – Job posting optimization Free of charge to begin with, the charge catches on with usage

Eightfold – Analytics and insights Publicly undisclosed pricing policy

HireVue – Video interviews $35000 via a flat-rate model

Entelo – Candidate relationship management (CRM) Starts with $150 per month; no other pricing details disclosed

Talenture – Talent management Different plans, the basic one starts at %19/per month per employee

iMocha – Coding interviews Could range anywhere between $5000-$25000, depending on the project

Harver – Candidate experience management Publicly undisclosed pricing policy

OneModel – Customization Publicly undisclosed pricing policy

Whatfix – Interactive guides Project and usage-specific pricing policy

intelliHR – Employee management Four kinds of plans with varying per employee/per month pricing

Below is a detailed list of potent AI and ML tools for HR professionals:

1. Pymetrics


Pymetrics is an AI and ML tool for HR that harnesses the power of neuroscience-based games to assess and evaluate candidates and match them with relevant job opportunities. In their words, “our mission is to make hiring as equal as we can, which includes providing accessibility accommodations for those who may need it.” Since its inception, numerous companies have utilized its algorithms to streamline talent acquisition, training, and development.

With Pymetrics, companies get the following:

Gamefied Assessments: These games’ exciting and engaging nature helps candidates enjoy the evaluation process more.

Job Matching: Pymetrics matches job openings in the company’s database with candidates based on their profiles.

Job Recommendations: Pymetrics offers analysis and suggestions to assist businesses in enhancing their hiring and training procedures.

The company has not openly disclosed its pricing scheme publicly. You can visit their website and add an inquiry to get more information.

2. AmazingHiring

Source: Finwizard

AmazingHiring is another prominent AI and ML tool for HR. It utilizes AI-powered search and sourcing to assist HR teams/recruiters locate and connect with the best candidates. Since its establishment in 2023, the tool has gained popularity among several businesses for use in their hiring procedures. Unique AI-powered candidate-matching algorithms are the USP of this HR technology.

AmazingHiring offers other things:

Matching Jobs with Job Seekers: AmazingHiring’s AI-powered matching algorithm examines the recruiter’s job description and candidate profile to find the most significant matches for the employment opportunity.

Collaborative Recruiting Platform: For recruiters and hiring managers to collaborate on the hiring process, AmazingHiring offers a platform.

Outreaching Features: Offers a variety of outreach options for recruiters, including email and messaging templates, candidate monitoring, and analytics to monitor the success of their outreach.

The HR tool charges around $4800 annually based on their recent pricing indications.

3. ICIMS Talent Cloud

Source: ICIMS

An HR solution called ICIMS Talent Cloud was developed to leverage AI systems to assist businesses in managing all aspects of hiring and managing people on a single platform. It offers features and tools to assist hiring managers in streamlining their hiring procedures and enhancing the overall candidate experience. ICIMS Talent Cloud has several essential characteristics, including:

Recruitment Marketing: ICIMS Talent Cloud offers resources for developing and monitoring job board and social media listings, email marketing campaigns, and referral schemes.

Integrations: ICIMS Talent Cloud integrates with various other HR and recruitment tools, including payroll systems, HRIS platforms, and background check providers.

As a holistic cloud solution, it costs around $1,700/month.

4. Oracle Recruiting

Source: SSB Technologies

The cloud-based HR platform Oracle Recruitment offers several tools and services to assist businesses in managing their hiring process. It is a component of Oracle’s HCM Cloud toolkit, which offers a variety of HR solutions to assist businesses in managing the whole employee lifecycle.

The HR platform offers:

Employee Onboarding: It facilitates the entire onboarding process for companies, making the workflows more streamlined in handling new hires, electronic forms, etc.

Reporting and Analytics: Oracle Recruiting provides data and insights into recruitment performance, including metrics like time to fill, source of hire, and cost per hire.

Implementation: The platform offers different cloud structures: public, private, on-premise, and hybrid.

Oracle no longer publishes its pricing details. You can contact the vendor for more information.

5. Skillate

Source: Mettl

An AI-powered HR technology platform called Skillate offers many tools and services to assist businesses in streamlining their hiring procedures. Natural language processing (NLP) and machine learning algorithms are used in Skillate’s AI technology to evaluate resumes and job descriptions and then match the best candidates with open positions.

Some key features of Skillate are:

Candidate Screening and Matching: Skillate’s AI technology automatically screens candidates’ resumes and narrows the talent pool to only job-relevant candidates.

Insights and Analytics: Like other HR tools, Skillate provides data and insights catering to performance, hiring costs, and predictions about employee retention and attrition.

Integrations: Skillate is highly integrable with other HR technologies like ATS and job boards like LinkedIn, Indeed, etc.

Skillate has a non-restrictive pricing policy that is not publicly disclosed. You can get in touch with the vendor to have a clearer idea.

6. Textio

Source: Textio

Textio is a unique cloud-based AI and ML tool for HR that utilizes natural language processing (NLP) to assist businesses in improving their writing use cases. The CMS software system provides creative insights and recommendations for crisp job descriptions, emails, chatbots, etc.

Textio offers:

Real-time Language Analysis: Textio’s AI analyzes language and facilitates inclusive writing by identifying cliches, biases, and language patterns.

Real-time Feedback: It provides real-time feedback on the effectiveness of writing as it is being created, allowing writers to make adjustments and improvements in real-time.

The pricing starts from $0, free of charge to begin with. The cost, however, goes up depending on your usage. Visit the website to request more pricing information.

7. Eightfold


Eightfold is another tool that utilizes artificial intelligence for HR professionals to streamline talent acquisition in the recruitment process. It is designed to help businesses hire and retain top-tier talent. With Eightfold, companies get:

Candidate Experience Management: The HR platform from Eightfold offers candidates a tailored experience that makes looking for and applying for jobs simple.

Career Mobility: Eightfold’s platform helps employees explore career paths within the company and find new opportunities for growth.

Analytics and Insights: The platform’s AI algorithms provide real-time analytics and insights into the hiring process, allowing companies to track their progress and make data-driven decisions.

Head to the website and contact the vendor to get project-based pricing details.

8. HireVue

Source: G2

A talent experience platform called HireVue is intended to streamline hiring processes and automate activities. With text recruiting, tests, and video interviewing tools, businesses can enhance how they interact with, evaluate, and hire talent. It offers:

Pre-Employment Assessments: The platform provides pre-employment tests that gauge applicants’ behavioral tendencies and job-specific competencies, assisting recruiters in spotting top performers.

Interviewer Training: The platform offers interviewer training resources to assist recruiters in conducting more efficient and objective interviews, including best practices and coaching tools.

Video Interviews: With HireVue, recruiters may conduct one-way or live video interviews to evaluate applicants’ body language, communication abilities, and general suitability for the position.

HireVue is one of the most expensive platforms, starting at $35000 per year via a flat rate model.

9. Entelo

Source: Entelo

Entelo is an automation platform that provides recruitment software based on predictive analytics and natural language processing (NLP) that enables recruiters to skim through data sets of resumes and categorize them based on gender, race, and veteran status.

The AI and ML tool for HR automation facilitates the following:

Candidate Sourcing and Engagement: Using social media, professional networks, public databases, and other sources, Entelo employs AI to go through skill sets and source prospects.

Diversity and Inclusion: The tool offers features to reduce bias in the hiring process, such as blind reviews and diverse candidate sourcing.

Candidate Relationship Management (CRM): The application provides a central platform to handle candidate data, communication, and scheduling, improving the procedure’s efficiency and structure.

The platform does not disclose the exact price structure; however, companies start with the automation platform for about $150/month. Contact the vendor for more details.

10. Talenture

Source: Talenture

Talenture is a high-end applicant tracking system and recruiting tool for HR professionals that provides recruiting teams with candidate sourcing, job processing, communication, analytics, job promotion, and career site capabilities. The AI and ML tool for HR streamlines the process by using:

Talent Management: To organize and track candidate interaction, interviews, feedback, and hiring choices.

Collaborative Hiring: Talenture’s AI software allows recruiters to collaborate with hiring managers, other recruiters, and team members, facilitating feedback and improving decision-making.

Talenture’s standard pricing plan starts at $19/per month, while the professional one starts at $29/per month. They also offer an enterprise plan for $39/per month.

11. iMocha

Source: iMocha

HR teams can examine candidates using the AI-powered skills assessment and recruitment platform iMocha, a collection of assessment libraries. This collection allows recruiting teams to quickly and efficiently evaluate quality candidates without technical expertise. It offers over 2500 skill tests, making it the world’s most extensive skill assessment collection.

Other benefits that come with iMocha:

Candidate Experience: Candidates can complete tests and interviews at any time, from any location, with iMocha’s personalized and mobile-friendly candidate experience.

AI-powered Analytics: The platform uses AI to analyze candidates’ assessment results, providing recruiters with insights into their performance, strengths, and areas for improvement.

Coding Interviews: iMocha’s HR solution allows recruits to conduct proctored live coding interviews and collaborate with the hiring team in real-time.

iMocha offers different subscription options that may cost anywhere from $5,000 to $25,000 annually.

12. Harver

Source: Harver

Harver is one of those AI and ML tools for HR that focuses on helping companies with volume hiring. As the hiring scale increases, many companies come across common challenges like shortlisting, onboarding, etc., and this is where Harver’s algorithm steps in. It analyzes resumes and measures a candidate’s aptitude, cultural acceptability, and skill sets.

With Harver, companies get assistance with the following:

Pre-Employment Assessments: Harver provides a variety of pre-employment tests to help employers find top performers by evaluating candidates’ cognitive talents and job-specific skills.

Scheduling Video Interviews: Just like a few other HR technologies, Harver also allows companies to conduct structured video interviews.

Candidate Experience: The platform delivers a tailored and mobile-friendly candidate experience, enabling applicants to finish screenings and interviews whenever and wherever they want, without any staffing requirements.

Head to the vendor website and request a demo to get more information about the pricing structure.

13. OneModel

Source: OneModel

OneModel is a proficient data workbench widely used by HR professionals to enable data compilation and examination. It gives HR teams access to a single source of truth for HR data, allowing them to learn more about their workforce and make informed decisions. Some of the critical features of OneModel include the following:

Data Visualization: With OneModel, users can create custom dashboards and reports using a variety of charts, graphs, and other visualizations.

Security: To guarantee that data is secure, OneModel gives data security first priority and provides robust security features, including multi-factor authentication, encryption, and role-based access controls.

Customization: OneModel is adaptable, enabling customers to customize the platform to meet their unique demands and specifications. Users can make custom data models, fields, and workflows to match their business processes.

OneModel offers three main categories of services:

People Analytics Data Mesh

People Analytics Essentials

People Analytics Enterprise

Visit the website and contact the vendor to have more detailed information.

14. Whatfix

Source: Ambition Box

Whatfix is a digital adoption platform that offers interactive modules and walkthroughs to help new employees become familiar with new programs and tools, easing the process of employee onboarding. Via real-time delivery of context assistance, the Whatfix platform is intended to boost productivity, streamline employee onboarding and training, and decrease support inquiries.

Other features that Whatfix offers are:

Contextual Help: Whatfix’s contextual help feature gives consumers the appropriate knowledge at the appropriate time, minimizing the need to look up solutions or seek assistance.

Performance Support: Whatfix’s performance support function gives staff members rapid access to information whenever needed, speeding up task completion and boosting output.

Interactive Guides: Whatfix offers interactive instructions that take users step-by-step through a task or process inside an application. These manuals are simple to incorporate within an application and may be altered to match a company’s logo.

Whatfix works on a flat per-application fee structure along with user license fees. The product and plan you require will determine the flat application price. The cost of user licenses is determined by the kind of user who will use the application where Whatfix will be installed. Contact the vendor to know more.

15. intelliHR

Source: People Managing People

Suppose your business wants utilizable reports on employee performance and satisfaction indicators. In that case, you can switch to intelliHR, an interface for HR automation software. It provides analytics tools so HR professionals can coordinate their work with the company’s strategic business goals.

intelliHR also offers:

Performance Management: IntelliHR offers an AI solution for managing performance that enables managers to create objectives, monitor development, and give feedback to staff members.

Employee Engagement: IntelliHR offers tools, like as surveys, feedback channels, and recognition and reward programs, for assessing and enhancing employee engagement.

Compliance Management: IntelliHR assists businesses in adhering to local and federal HR regulations, such as those governing data privacy and employment laws.

intelliHR offers numerous plans:

Engagement: at $5.5 per employee, billed monthly.

Performance: at $9.1 per employee, billed monthly.

Strategic: at $15.2 per employee, billed monthly.

Enterprise: contact the vendor to get more information.

How Can HR professionals level up their careers using ML and AI? Get Better at Predictive Analytics

HR professionals can analyze tonnes of workforce data using ML and AI to predict patterns and generate data-driven predictions regarding employee behavior, retention, and performance. Moreover, AI can address future workforce issues by developing proactive plans.

However, one must have the necessary knowledge and background to analyze and infer from prediction data.

Better Employee Engagement

To understand employee mood and engagement, practitioners can use AI and ML tools for HR to analyze employee feedback, social media, and other data sources. This can assist HR professionals in discovering potential sources of employee dissatisfaction and developing strategies to increase employee engagement and retention.

This will only be possible if HR professionals know how to use these tools. Otherwise, a higher engagement will remain a long shot

Automate HR Functions

HR professionals can use ML and AI to automate time-consuming and repetitive HR tasks like interview scheduling, resume screening, and onboarding. This enables professionals to concentrate on higher-value tasks like creating HR strategies and interacting with employees.

Being familiar with AI and ML tools for HR can significantly reduce the time it takes to accomplish the same task manually. Moreover, automation drastically reduces the scope of human biases and errors, increasing the overall efficiency of recruitment.


The following resources are available at Analytics Vidhya:

Video Tutorials: Many video tutorials on machine learning, artificial intelligence, and other related topics are available on the website. The application of machine learning algorithms for predictive analytics, employee sentiment analysis, etc., is covered in detail in these lessons.

Blogs: Every blog features several thoroughly researched pieces on artificial intelligence, data sciences, machine learning, and ML in human resource management.

Community of Contributors: AV has a strong community of data scientists and machine learning practitioners who can help with instruction and real-world problem-solving.

Hey there! Before you wrap things up, check out the series of workshops at the highly-anticipated DataHack Summit 2023, that will blow your mind. These include ‘Applied Machine Learning with Generative AI‘, ‘Exploring Generative AI with Diffusion Models‘, and ‘Mastering LLMs: Training, Fine-tuning, and Best Practices’, among many others. Get ready to unleash your creativity and expertise. These workshops are meticulously designed to equip you with practical skills and real-world knowledge that will take your abilities to new heights. With immersive hands-on experience, you’ll gain the confidence to tackle any data challenge with ease. This is an opportunity you don’t want to miss, as it will not only enhance your expertise but also connect you with industry leaders and open doors to exciting career prospects. So, don’t wait any longer – register now for the DataHack Summit 2023 and secure your spot!

Frequently Asked Questions Q1. What are HR technology trends in 2023?

A. Based on current business patterns, several new HR technology trends are anticipated to persist in 2023. Some of them are

Automation using AI and ML

Remote Working

HR Data Analytics

Predictive Analytics

Employee Experience and Management

Attrition Detection

Workforce Mobility

Q2. What is the future of AI in HR?

A. The trends in HR technology will continue to be driven by AI and automation. As chatbots, virtual assistants, and AI-powered recruitment tools proliferate, HR departments can automate a number of their procedures to increase productivity and cut costs.

Q3. What is the future of HRM in 2025?

A. Based on the current trends, HRM will have a bright future in 2023. The development and continued use of technology, especially artificial intelligence and machine learning, to enhance HR procedures and decision-making will continue.

Predictive analytics will be used more extensively to detect problems and possibilities within the workforce. Moreover, the future will witness a growing emphasis on the employee experience, with HR departments taking a more proactive role in fostering a supportive work environment and offering specialized support to specific individuals.

But as the scale increases, challenges may also exhibit similar patterns. Only time will tell.

A. AI and ML tools for HR are used for several purposes. These include

Talent Acquisition and Recruitment

Employee Onboarding

Employee Engagement

Performance Management

Training, and Development

Internal Mobility.


Top 10 Venture Capitalists For Emerging Ai Companies To Follow In 2023

The venture capitalists for emerging AI companies offer massive amounts of facilities to AI pioneers

Due to the growing need for artificial intelligence, tech pioneers and innovators are moving on to this burgeoning domain to help flourish this sector. Avant-garde AI companies are rapidly analyzing the needs of the industry and are helping leaders fight against major turmoil. To aid this need, venture capitalists are increasingly becoming more involved in investing in these AI companies. Here are have listed the top venture capitalists for emerging AI companies that leaders should follow.


Accel is an early and growth-stage venture capital firm that powers a global community of entrepreneurs. Accel backs entrepreneurs who have what it takes to build a world-class, category-defining business. The company brings more than three decades of experience in building and supporting companies. Accel’s vision for entrepreneurship and business enables it to identify and invest in the companies that will be responsible for the growth of next-generation industries.


Accubits is an AI and Blockchain focused development and solutions company based in Washington DC with its development offices in India and Dubai. Pioneering as a custom Blockchain solutions provider delivering solutions ever since 2012. It is a big family of engineers, innovators, inventors and many others continually working to help its customers in leveraging their business and adding value to their lives.


AEye is the creator of iDAR™ (Intelligent Detection and Ranging), the premier artificial perception platform for vehicle autonomy, ADAS, and robotic vision applications where safety is the priority. iDAR fuses solid-state agile LiDAR, an optional camera, and integrated AI to create a smart, software definable sensor that extracts only the data that matters – enabling fast, accurate perception.

Alibaba Cloud

Alibaba Cloud develops highly scalable cloud computing and data management services. As the cloud computing arm and business unit of Alibaba Group, It provides a comprehensive suite of global cloud computing services to power both our international customers’ online businesses and Alibaba Group’s own e-commerce ecosystem. Alibaba Cloud’sinternational operations are registered and headquartered in Singapore, and the company has international teams stationed in Dubai, Frankfurt, Hong Kong, London, New York, Paris, San Mateo, Seoul, Singapore, Sydney, and Tokyo.


Since 2011, AlphaSense’s AI-based technology has helped professionals make smarter business decisions by delivering insights from an extensive universe of public and private content—including company filings, event transcripts, news, trade journals, and equity research. Company’s mission is to organize the world’s business information and leverage technology to enable companies to make smarter, faster, and more confident decisions.


Amazon is an international e-commerce website for consumers, sellers, and content creators. It offers users merchandise and content purchased for resale from vendors and those offered by third-party sellers. Operating in North American and International markets, Amazon provides its services through websites such as chúng tôi and chúng tôi It Also enables authors, musicians, filmmakers, app developers, and others to publish and sell content via its branded websites.

Analytics Ventures

Analytics Ventures (AV) is a venture studio fund dedicated to the inception of new ventures harnessing innovations in Artificial Intelligence. The company believes that Artificial Intelligence is still in the early stages of its maturity cycle, and as such, the biggest value in the field continues to come from entrepreneurs, researchers, and early-stage investors. AV has partnered with corporations, researchers, and entrepreneurs to provide a proven execution model and best in class AI expertise.

Andreessen Horowitz

Andreessen Horowitz is a venture capital firm specializing in investing in seed, start-ups, early, mid-stage, growth, and late-stage. It prefers to invest in the social media business and technology sector with a focus on software, back-end infrastructure, the infrastructure of the Internet, cloud computing, enterprise software and services, consumer, business Internet, mobile-Internet, consumer Internet, cloud computing, data storage, social network browsers data-storage, consumer electronics, networking functions, software related biology, biotech, and medicine companies at the intersection of computer science and life sciences with a focus on digital therapeutics, cloud technology in biology, and computational medicine.

Anduril Industries

Anduril Industries is a defense product company that builds technology for military agencies and border surveillance. Anduril commits top technical talent to solve the most complex national security challenges. The company is building the next generation of technology that will aid and protect those who serve on the front lines defending the nation and its interests. Its mission is to develop cutting-edge technology that enables America and its allies to maintain global leadership now and into the future.

Aurea Software

Top 10 Trends Of Blockchain In 2023

Top 10 Trends of Blockchain in 2023 1. Blockchain as a service (Bass) by big tech companies

One of the promising blockchain trends in 2023 is BaaS, short for Blockchain As A Service. It is another blockchain trend that is as of now coordinated with various new businesses just as endeavors.

These computerized items might be shrewd agreements, decentralized applications (Dapps), or considerably different administrations that can work with no arrangement prerequisites of the total blockchain-based foundation.

A portion of the organizations building up a blockchain that give BaaS administration are Microsoft and Amazon, subsequently forming the fate of blockchain applications.

2. Combined blockchain moves to the center stage

Blockchain systems can be named: Private, Public, Federated, or Hybrid. The term Federated Blockchain can be alluded to as outstanding amongst other blockchain’s most recent trends in the business.

It is simply an updated type of the essential blockchain model, which makes it progressively perfect for some, particular use cases. In this sort of blockchain, rather than one association, different specialists can control the pre-chosen hubs of blockchain.

Presently, this choice gathering of different hubs will approve the square with the goal that the exchanges can be handled further. In 2023, there will be an ascent in the utilization of combined blockchain as it gives private blockchain systems, a progressively adaptable standpoint.

3. Stable coins will be more visible

Utilizing Bitcoin for instance of digital forms of money its exceptionally unstable in nature.

To maintain a strategic distance from that unpredictability stable coin went to the image unequivocally with a stable worth partner with each coin.

Starting at now, stable coins are in their underlying stage and it is anticipated that 2023 will be the year when blockchain will accomplish their unequaled high.

One main impetus for utilizing stable coin is the presentation of Facebook’s digital money “Libra” in 2023 even with all the difficulties confronting this new cryptographic money proposed by Facebook and the contracting circle of accomplices in

4. Long-range informal communication problems meet blockchain solution

There are around 2.77 Billion online life clients around the world in 2023.

The presentation of blockchain in online life will have the option to tackle the issues identified with infamous embarrassments, security infringement, information control, and substance significance.

Consequently, the blockchain mix in the internet based life area is another rising innovation trend in 2023.

With the execution of blockchain, it very well may be guaranteed that all the internet based life distributed information stay untraceable and can’t be copied, much after its erasure.

Also, clients will get the chance to store information all the more safely and keep up their possession.

Blockchain likewise guarantees that the intensity of substance pertinence lies in the possession of the individuals who made it, rather than the stage proprietors.

This causes the client to feel increasingly make sure about as they can control what they need to see. One overwhelming undertaking is to persuade online life stages to actualized it, this can be on a willful base or as a consequence of security laws like GDPR.

5. Interoperability and blockchain networks

Blockchain interoperability is the capacity to share information and other data over numerous blockchain frameworks just as systems. This capacity makes it basic for people, in general, to see and access the information across various blockchain systems.

6. Economy and finance will lead blockchain application

In contrast to other customary organizations, the banking and fund businesses don’t have to acquaint radical change with their procedures for embracing blockchain innovation.

After it was effectively applied for the cryptographic money, budgetary establishments start genuinely considering blockchain appropriation for customary financial tasks.

PWC report, 77 percent of monetary organizations are relied upon to receive blockchain innovation as a feature of an underway framework or procedure by 2023.

Blockchain innovation will permit banks to lessen inordinate organization, lead quicker exchanges at lower costs, and improve its mystery.

One of the blockchain forecasts made by Gartner is that the financial business will infer 1 billion dollars of business esteem from the utilization of blockchain-based cryptographic forms of money by 2023.

In addition, blockchain can be utilized for propelling new cryptographic forms of money that will be managed or impacted by financial strategy.

Along these lines, banks need to decrease the upper hand of independent cryptographic forms of money and accomplish more noteworthy power over their fiscal arrangement.

7. Blockchain integration into government agencies

The possibility of the circulated record is additionally exceptionally appealing to government specialists that need to administrate extremely huge amounts of information.

As indicated by Gartner, by 2023, in excess of a billion people will have a little information about them put away on a blockchain, yet they may not know about it.

8. Blockchain combines with IOT

The IoT tech market will consider a to be center around security as intricate wellbeing challenges crop up. These complexities originate from the different and conveyed nature of the innovation.

The quantity of Internet-associated gadgets has penetrated the 26 billion imprint. Gadget and IoT arrange hacking will get ordinary in 2023. It is up to arrange administrators to prevent interlopers from doing their business.

The current brought together the design of IoT is one of the fundamental purposes behind the defenselessness of IoT systems.

With billions of gadgets associated and more to be included, IoT is a major objective for digital assaults, which makes security-critical. Blockchain offers new trust in IoT security for a few reasons.

To begin with, blockchain is open, everybody taking an interest in the system of hubs of the blockchain system can see the squares and the exchanges put away and endorses them, in spite of the fact that clients can, in any case, have private keys to control exchanges.

Second, blockchain is decentralized, so there is no single position that can support the exchanges dispensing with Single Point of Failure (SPOF) shortcoming. Third and above all, it’s safe—the database must be broadened and past records can’t be changed.

Numerous IoT based organizations receive blockchain innovation for their business arrangements. The International Data Corporation (IDC) is expecting that 20 percent of IoT arrangements will empower blockchain administrations by 2023.

9. Blockchain with AI

With the joining of AI (Artificial Intelligence) with blockchain innovation will make for a superior turn of events. This combination will show a degree of progress in blockchain innovation with a sufficient measure of utilizations.

The International Data Corporation (IDC) proposes that worldwide spending on AI will reach $57.6 billion by 2023 and 51% of organizations will make the change to AI with blockchain combination.

Moreover, blockchain can likewise make AI progressively rational and justifiable, and we can follow and decide why choices are made in AI. Blockchain and its record can record all information and factors that experience a choice made under AI.

In addition, AI can help blockchain productivity much better than people, or even standard processing can. A gander at the manner by which blockchains are at present sudden spike in demand for standard PCs demonstrates this with a great deal of preparing power expected to perform even fundamental errands.

10. Interest for blockchain experts

Blockchain is another innovation and there is just not many percents of people who are talented in this innovation.

As blockchain innovation turning into a quickly expanding and wide-spreading innovation, that makes a circumstance for some to create aptitudes and experience about blockchain innovation.

Despite the fact that the quantity of specialists in blockchain fields is expanding, then again the execution of this innovation has a fast development which will make a circumstance for the interest of Blockchain trend in 2023.


It merits saying that there are certified endeavors by colleges and universities to find this need, however, the pace of graduating understudies with enough abilities to manage blockchain innovation isn’t sufficient to fill the hole.

Additionally, companies are finding a way to expand on their current abilities by including preparing programs for creating and overseeing blockchain systems.

Claims Processing Transformation: Trends & Strategy In 2023

Claims processing is a procedure whereby an insurer receives, verifies and processes a claim/theft report submitted by a policyholder. It accounts for 70% of property insurers’ expenses. Furthermore, claims processing impacts customer satisfaction; More than 85% of customers who were dissatisfied with their last claims processing considered to switche providers.

Despite the importance of claims processing, 80% of claims executives accept the fact that they miss important opportunities. Not to miss future opportunities, we want to highlight customer expectations and technological improvements that are changing claims processing, and present a method to develop an overall strategy considering customer expectations.

Figure 1. Challenges of claims executives:

Customer Expectations

According to McKinsey, insurance is the sector that customers need human contact the most to solve their problems. It is specifically correct for claims processing where policyholders experience a tragic event. According to EY, 76% of customers want to submit their claims preferably by telephone. Another research indicates that 22% of customers demand direct communication with experts. Thus, a large proportion of customers are not willing to submit their claims via digital media and they require human contact.

On the other hand, 22% business insurance policyholders prioritize fully digital claims processing when selecting a provider. Also, millennials and urbanites generally demand less physical interaction and prefer to consume digital services when possible in any industry. It is therefore plausible that, over time, there will be a demand for further digitization of claims processing. For example, millennials and post-millennials (those born after 1997) will make up almost half of the adult population by 2030. In this context, McKinsey predicts that more than 50% of claims processing will be automated by 2030.

Another issue is that there is a positive relationship between the amount of the loss and the need for human contact. In the case of minor accidents/thefts, policyholders tend to prefer digital automated claims processing. However, if the damage is severe, they need more human contact. 

Technological Developments

Technological developments increase operational efficiency of insurance companies by automating claims processing and enhancing fraud detection. There are four major technological improvements that make this contribution:

 AI/ML models: These tools are transforming almost all industries and the insurance industry is not exempt. The subdivisions of AI/ML models facilitate claims processing as follows:

NLP: Claims processing requires effective use of language. NLP-driven chatbots can automate the FNOL process by guiding policyholders to submit the required documents, including pictures of the damage. OCR is also good at deriving data from handwritten documents. Due to regulations, insurers still have to work with such documents when processing claims.

Computer vision: models can estimate the cost of damage by evaluating videos and photos taken to submit a FNOL.

Advanced analytics: are useful for detecting and preventing fraud via calculating coefficients which are associated with insurance fraud.

Blockchain: Automates claims processing thanks to smart contracts, which are agreements stored on a blockchain that can be enforced by code. Also, thanks to authentication capabilities, blockchain technology helps fight against double dipping fraud.

IoT/Telematics: The cloud of smart devices and telematics assists insurers detect fraud. IoT constantly provides data about the environment so that insurance companies can check whether the claims of policyholders are true or not.

Custom Mobile Apps: Provides customers with a convenient way to submit their claims and track their status. With the increasing use of smartphones, custom mobile apps could be a promising tool for claims processing in the near future.

For more information regarding how AI improves claims processing you can read our AI claims processing article.

For more information how technology (AI based and others) improves claims processing you can read our article Top 7 Technologies that Improve Claims Processing in 2023.

For more information about how technological enhancements boosts all insurance practices you can read our Top 5 Insurance Technologies & Their Use Cases article.

How to set an optimal claims handling strategy based on customer type?

Technology obviously reduces the cost of the insurers. On the other hand, claims processing still requires human assistance in order to satisfy non-tech savvy customers or to resolve complex or highly valuable claims. Consequently, there is a tradeoff between:

Reducing cost

Ensuring customer satisfaction

Ensuring high accuracy

for the case of the claims processing. In addition, this optimization process is a dynamic one thanks  to improving technology and customer preferences changing in favor of more automated channels.

To identify the right approach for the right situation, insurers need to create a matrix with:

The claims on the one side including their type (e.g. retail auto damage claim with minor damage), the user (e.g. user not preferring automated channels). Insurers can use behavioral analytics to understand the level of digital literacy and stress management of their customers and classify them.

The claims processing approaches (e.g. fully automated chatbot with OCR support for end-to-end claims processing).

Such an exercise can help insurers identify the right approach for the right claim and increase the level of automation while delighting customers.

A riskier alternative to this approach would be to over-invest into building a delightful digital and automated claims processing flow and guide all customers into using it, rather than offering manual claims processes (e.g. digital platforms, the call center) for some users.

Depending on the insurer’s customer profile, one of these approaches can be chosen.

If you need more information regarding the latest developments in insurtech and identify top vendors, we can help, contact us.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.





7 Smartphone Trends That Really Should Stop In 2023

Eric Zeman / Android Authority

2023 was an important year for the smartphone industry in many ways. 5G became available to more than just flagship devices, we got foldables with improved durability, and mid-range phones took a major step up in features.

It wasn’t all great though. For every welcome industry move in 2023, there was a trend we wouldn’t like to see continued in 2023. Here’s our full list.

Pointless 2MP cameras

Ryan-Thomas Shaw / Android Authority

One of the most annoying camera trends in the last two years has been the use of low-quality 2MP sensors. It’s a transparent attempt to bump up camera numbers. We’ve seen everyone from Xiaomi and realme to Samsung and OPPO adopt this strategy, often using two 2MP cameras so they can brag about offering quad rear cameras.

See also: 2023 smartphone mega shootout — the best camera phones tested

We’d definitely like to see more brands decide on a quality over quantity approach for cameras in 2023. In other words, we’d like brands to improve their main, ultra-wide, or even macro cameras instead of simply adding more lenses. On the latter, if brands still insist on offering a macro lens, then hopefully we see higher resolution sensors with autofocus instead of token 2MP cameras.

Slow wired charging from lagging brands

Robert Triggs / Android Authority

It’s hard to believe that you can actually buy phones with 65W or even 100W+ charging speeds in 2023, such as the Xiaomi Mi 10 Ultra and OnePlus 8T. What’s even harder to believe is that there are still flagship phones out there that don’t offer fast charging.

Poor update commitments

Eric Zeman / Android Authority

Google already commits to offering three years of system updates to its Pixel phones. Samsung also joined the club this year by offering a three-year commitment to Android version updates for some devices. That was one of the few bright spots in this regard in 2023.

The year saw OnePlus confirm just one update for its Nord N10 and N100 phones, while Motorola thought it could get away with pledging one version update for its $1,000 Edge Plus phone. Moto eventually changed tack and switched back to two version updates, but why did we need to go through this in the first place?

Between consumers holding on to their phones for a longer period of time and the economic uncertainty surrounding COVID-19, it only makes sense for more brands to stay committed to software updates.

Major price hikes for flagships

Ryan-Thomas Shaw / Android Authority

OnePlus 8T

Xiaomi, realme, and OnePlus all offered 2023 flagships at a higher price than predecessors. Part of this is apparently due to higher flagship silicon prices this year. However, outside of some welcome surprises, it’s still rather disappointing to see a dearth of affordable flagship phones in 2023.

Read: The best 2023 flagship phones still worth buying in 2023

We’ve also seen mmWave versions of phones coming in at ~$100 more expensive than the standard 5G versions. Some examples of this include the Verizon versions of the OnePlus 8 and Pixel 4a 5G. Hopefully, we’ll see more sensibly priced flagship phones in 2023, but we’re not holding our breath for mmWave phones to come down in price.

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