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5 Innovative Applications of AI in Recruitment

Nidhi Agrawal
4 minutes, 4 seconds read

The growing gig economy has added a new challenge to the organizations’ recruitment settings. While 62% of millennials believe gig work is a viable alternative to mainstream jobs (Deloitte Global Millennial Survey 2019), only 8% of HR Organizations believe they’re ready to manage gig or contract workers; thus opening new avenues for the use of technology in recruitment processes. Let’s see how AI in recruitment can benefit organizations in upscaling candidate experience, diversity and inclusion, and onboarding irrespective of geographical location.

How Organizations Can Leverage AI in Recruitment?

According to Grand View Research, the global HR management market is projected to reach $30.01 billion by 2025, of which Talent Management software will cover $13.8 billion worth of the market share. Advanced analytics, apps, and team-focused management practices will fuel the growth of recruitment technologies. The following are 5 areas where AI can out rule existing technologies and HR software.

#1 Screening

Identifying the right candidate from a large applicant pool terrifies recruiters. Surprisingly, only 9% of organizations possess a strong screening technology, says Josh Bersin in HR Technology Market 2019. According to Ideal’s recruiting software ebook, almost 65% of resumes received for a high-volume role are ignored. Now that the inclination towards an alternative workforce is growing, HRs face additional pressure in shortlisting candidates for the organizations. 

In the age where candidates have equal rights to question employers, automated responses aren’t just enough. AI-powered chatbots can not only automate the resume screening processes but also understand the candidates’ queries better and respond in real-time. 

For example, Olivia developed by Paradox is a recruitment assistant chatbot. It helps companies in collecting resumes, screening them, and interacting with the candidates. Olivia bot can schedule interviews and delivers one-to-one candidate experience. 

#2 Identifying Passive Candidates and Rediscovery

According to Deloitte Global Human Capital Trends Survey 2019, 61% of organizations consider finding qualified experienced hires as the most difficult recruitment challenge. Also, 26% of leading recruiters believe- inefficient technology is the reason for hiring setbacks.

Organizations rely on the capabilities of their existing workforce more than a new-hire. However, uncovering the talent that’s a great fit for a new role and their willingness to take up a new responsibility is quite a challenge. AI can help in rediscovering hidden talent among the existing employees thus reducing candidate acquisition costs. 

Another aspect of recruitment, especially for sophisticated roles is passive candidate sourcing. However, identifying and engaging with people who are not currently looking for a job change can be daunting. AI can simplify this aspect as well. Instead of focusing only on a candidate’s resume, sourcing more information from his public profiles and making predictions about the success in acquisition can save a lot of human efforts. 

#3 Sentiment Analysis

AI can judge a candidate’s sentiments better than a human because there won’t be any conflict of emotions during an interview. AI can identify, extract, quantify, and study the candidate’s states using procedures like NLP (natural language processing), computational linguistics, facial recognition, and biometrics. 

Through AI, companies like Unilever, IBM, Dunkin Donuts, and many others are analyzing a candidate’s facial expressions during video job interviews. For instance, using the HireVue AI-driven recruitment platform, Unilever was able to hire for entry-level jobs from 1200 more colleges.

#4 Defining Jobs APIs

Deloitte Global Human Capital Trends Survey 2019 reports – 25% of organizations feel constructing an appealing job offer as challenging. Moreover, according to HRDrive 2016 survey, 72% of HR managers claim to provide clear job descriptions. But, only 36% of candidates say they understood it.

AI can bridge this gap by mapping industry jargon and search queries. AI can also present descriptive job descriptions or skills requirements in concise language that can save the candidate’s time and hence improve conversions.

On 15th November 2016, Google launched Cloud Jobs API- a machine learning service to improve the hiring process by providing a lingua franca between the job seeker and employer job postings. It comprises of two ontologies- occupation and skills and establishment of relational models between them. 

#5 Reducing Unconscious Bias

Organizations believe that a diverse workforce improves employee productivity, and retention and yields innovation and creativity. However, diversity hiring suffers a setback because of unintentional bias and recruitment preferences. 

AI can help in reducing unconscious biases during recruitment because it is completely programmable. The model can be trained to clear patterns of potential prejudices based on gender, ethnicity, geography, or even academic institutions. According to Modern Hire research, 49% of candidates believe AI can improve their chances of getting hired.

Will AI Replace Recruiters?

PayScale suggests that 66% of organizations agree that employee retention is a growing concern, making hiring an even more sophisticated process. Benefits of AI in recruitment encircles around sourcing, screening, assessment, and identifying hidden talents. Technocrats believe AI will not replace recruiters, it will simply augment the existing hiring processes. 

We are an AI-first products and solutions firm; feel free to reach us out at for your industry-specific requirements.


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Across the Insurance ecosystem, a special fraction within the industry is noteworthy for its adoption of new technologies ahead of others. However slow but sure, uberization of insurance has conventionally demonstrated a greater inclination towards digitization. Insurers now more than ever, need big data-driven insights to assess risk, reduce claims, and create value for their customers. 

92% of the C-Level Executives are increasing their pace of investment in big data and AI.

NewVantage Partners Executive Survey 2019 

Artificial Intelligence has brought about revolutionary benefits in the Insurance industry.

AI enriched solutions can remove the ceiling caps on collaboration, removes manual dependencies and report errors.

However, organizations today are facing a lot of challenges in reaping the actual benefits of AI.

5 Challenges for AI implementation for Insurers

5 AI Implementation Challenges in Insurance

Lack of Quality training data

AI can improve productivity and help in decision making through training datasets. According to the survey of the Dataconomy, nearly 81% of 225 data scientists found the process of AI training more difficult than expected even with the data they had. Around 76% were struggling to label and interpret the training data.

Clean vision, Process, and Support from Executive Leadership

AI is not a one time process. Maximum benefits can be reaped out of AI through clear vision, dedicated time, patience and guided leadership from industry experts and AI thought leaders.

Data in-silos

Organizational silos are ill-advised and are proven constrictive barriers to operational productivity & efficiency. Most businesses that have data kept in silos face challenges in collaboration, execution, and measurement of their bigger picture goals. 

Technology & Vendor selection

AI has grown sharp enough to penetrate through the organizations. As AI success stories are becoming numerous investment in AI is also getting higher. However big the hype is, does AI implementation suits your business process or not – is the biggest question. The insurtech industries have continued its growth trajectory in 2019; reaching a funding of $6B. With the help of these insurtech service firms, Insurance organizations have made progress, tackling the age-old insurance ills with AI-powered innovations.

People, Expertise and Technical competency

‘Skills and talent’ in the field of AI is the main barrier for AI transformation in their business.

Still playing catch-up to the US, China, and Japan — India has doubled its AI  workforce over the past few years to nearly 72,000 skilled professionals in 2019. 

Are you facing challenges with your Insurance process but have no idea where the disconnect is? Is your Insurance business process ripe for AI in the year 2020?

What is the right approach?

Join our Webinar — AI for Data-driven Insurers: Challenges, Opportunities & the Way Forward hosted by our CEO, Parag Sharma as he addresses Insurance business leaders on the 13th of February, 2020.

Register for the live webinar by Parag Sharma (AI Thought Leader & CEO Mantra Labs). 


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Ratemaking, or insurance pricing, is the process of fixing the rates or premiums that insurers charge for their policies. In insurance parlance, a unit of insurance represents a certain monetary value of coverage. Insurance companies usually base these on risk factors such as gender, age, etc. The Rate is simply the price per ‘unit of insurance’ for each unit exposed to liability. 

Typically, a unit of insurance (both in life and non-life) is equal to $1,000 worth of liability coverage. By that token, for 200 units of insurance purchased the liability coverage is $200,000. This value is the insurance ‘premium’. (This example is only to demonstrate the logic behind units of exposure, and is not an exact method for calculating premium value)

The cost of providing insurance coverage is actually unknown, which is why insurance rates are based on the predictions of future risk.  

Actuaries work wherever risk is present

Actuarial skills help measure the probability and risk of future events by understanding the past. They accomplish this by using probability theory, statistical analysis, and financial mathematics to predict future financial scenarios. 

Insurers rely on them, among other reasons, to determine the ‘gross premium’ value to collect from the customer that includes the premium amount (described earlier), a charge for covering losses and expenses (a fixture of any business) and a small margin of profit (to stay competitive). But insurers are also subject to regulations that limit how much they can actually charge customers. Being highly skilled in maths and statistics the actuary’s role is to determine the lowest possible premium that satisfies both the business and regulatory objectives.

Risk-Uncertainty Continuum

Source: Sam Gutterman, IAA Risk Book

Actuaries are essentially experts at managing risk, and owing to the fact that there are fewer actuaries in the World than most other professions — they are highly in demand. They lend their expertise to insurance, reinsurance, actuarial consultancies, investment, banking, regulatory bodies, rating agencies and government agencies. They are often attributed to the middle office, although it is not uncommon to find active roles in both the ‘front and middle’ office. 

Recently, they have also found greater roles in fast growing Internet startups and Big-Tech companies that are entering the insurance space. Take Gus Fuldner for instance, head of insurance at Uber and a highly sought after risk expert, who has a four-member actuarial team that is helping the company address new risks that are shaping their digital agenda. In fact, Uber believes in using actuaries with data science and predictive modelling skills to identify solutions for location tracking, driver monitoring, safety features, price determination, selfie-test for drivers to discourage account sharing, etc., among others.

Also read – Are Predictive Journeys moving beyond the hype?

Within the General Actuarial practice of Insurance there are 3 main disciplines — Pricing, Reserving and Capital. Pricing is prospective in nature, and it requires using statistical modelling to predict certain outcomes such as how much claims the insurer will have to pay. Reserving is perhaps more retrospective in nature, and involves applying statistical techniques for identifying how much money should be set aside for certain liabilities like claims. Capital actuaries, on the other hand, assess the valuation, solvency and future capital requirements of the insurance business.

New Product Development in Insurance

Insurance companies often respond to a growing market need or a potential technological disruptor when deciding new products/ tweaking old ones. They may be trying to address a certain business problem or planning new revenue streams for the organization. Typically, new products are built with the customer in mind. The more ‘benefit-rich’ it is, the easier it is to push on to the customer.

Normally, a group of business owners will first identify a broader business objective, let’s say — providing fire insurance protection for sub-urban, residential homeowners in North California. This may be a class of products that the insurer wants to open. In order to create this new product, they may want to study the market more carefully to understand what the risks involved are; if the product is beneficial to the target demographic, is profitable to the insurer, what is the expected value of claims, what insurance premium to collect, etc.

There are many forces external to the insurance company — economic trends, the agendas of independent agents, the activities of competitors, and the expectations and price sensitivity of the insurance market — which directly affect the premium volume and profitability of the product.

Dynamic Factors Influencing New Product Development in Insurance

Source: Deloitte Insights

To determine insurance rate levels and equitable rating plans, ratemaking becomes essential. Statistical & forecasting models are created to analyze historical premiums, claims, demographic changes, property valuations, zonal structuring, and regulatory forces. Generalized linear models, clustering, classification, and regression trees are some examples of modeling techniques used to study high volumes of past data. 

Based on these models, an actuary can predict loss ratios on a sample population that represents the insurer’s target audience. With this information, cash flows can be projected on the product. The insurance rate can also be calculated that will cover all future loss costs, contingency loads, and profits required to sustain an insurance product. Ultimately, the actuary will try to build a high level of confidence in the likelihood of a loss occurring. 

This blog is a two-part series on new product development in insurance. In the next part, we will take a more focused view of the product development actuary’s role in creating new insurance products.


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