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The ‘Digital’ Insurance Broker

Nivin Simon
3 minutes, 24 seconds read

The technological advancements brought forth by insurtech will soon become routine for brokerage offices within the next few years. Digital-first approaches have finally trickled down, turning ripe for adoption for this major distribution channel. However, broker adoption has still not caught pace with their agency counterparts.

According to a 2019 report surveying independent insurance brokers across the US, Canada & the UK, the average for digital technology adoption at an independent brokerage is only around 43%, even though nearly 96% of them (almost universally) use a broker management system for indispensable day-to-day operations. Interestingly, over 80% don’t offer any form of ‘mobile apps’ or ‘self-service portals’ for customers or staff. 

Today’s insurance customers are younger and prefer digital over traditional channels — leaving a lot of unmet gaps in the value chain. The report also identified key areas where adoption is growing — such as capabilities in workflow process management, document management, sales opportunities & prospect tracking, one system-one view visibility into all departments among others. For example, the downside to not outfitting your broker operation with employee mobility tools alone translates to over 30% reduction in staff productivity. 

Today’s insurance customers are younger and prefer digital over traditional channels

Meanwhile, brokers are facing a whole new set of challenges — Insurance is being built for digital and the audience is changing. Gen Z and Millennials will form the core of their target demographic. A fully online brokerage can benefit these potential customers through simple end-to-end policy administration and by fine-tuning the customer journey.

While brokers are not involved in the manufacture of insurance products or the evaluation of risk, several other value chain functions are being performed through brokers now — of which managing the customer relationship is pivotal. 

There is a lot of data across the lifecycle to look at, which necessitates the need for advanced analytics in order maximize the opportunities to up/cross-sell. At present, data analytics is widely under-utilized among most insurance brokers leaving them blindsided to customer needs.

The Case for a ‘Digital’ Brokerage

A digital broker business is built on these foundational blocks — robust broker management system, seamless mobility tools for employees, insurer connectedness, self-service portals, smart customer apps, advanced data analytics and the cloud. 

The case for digital brokerage

Taking the entire business model online requires the right business advisory and technical roadmap, without which the transformation can leave you with unwarranted gaps in the operating structure. This is where Artificial Intelligence can play a critical role in securing brokerages to be future-ready. The digital broker has to be outfitted with a staunch selection of AI-enabled tools that provide better business visibility, more unified workflows and eliminates time spent managing and updating divergent systems.  

Analysing big data (predictive analytics) and social media using AI can offer real-time insights for measuring risk, immediate demands and possible life changes for customers. For brokers, this translates to an enhanced ability to justify value to clients and ultimately retain those customers.


EY ‘The broker of the future report’

According to a recent EY report on the state of digital brokerages, ‘digital onboarding tools’ and ‘sales leads & application tools’ were identified as attributes with the lowest satisfaction among brokerages. There is a growing sense that these tools need to be a cut above the industry benchmarks — in order to improve the digital relationship with a customer or prospect.

The Digital Broker can also leverage automation to improve efficiency in agent productivity and document handling processes. For instance, enabling employees with remote digital tools empowers them to quickly take action – from quoting prospects to providing policy details and managing claims for existing customers — especially when they need it most. 

Brokers, just like insurers and agencies, need next-gen customer engagement solutions in order to maximize real customer lifetime value. Technologies like Artificial Intelligence have the potential to enhance several facets of the business from reducing back-office processing times and intelligent lead allocation to designing better customer facing products. Improvements achieved through the deployment of AI can create significant gains in operational efficiency and RPE (revenue per employee).

To learn how MantaLabs can help your brokerage begin its digital transformation journey, reach out to us on hello@mantralabsglobal.com

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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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