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5 Insurance Front-Office Processes You Can Improve with AI

Nivin Simon
6 minutes, 5 seconds read

Amidst the growing footprint of Insurtech around the world, Insurance service models continue to evolve for both front and back-office processes. Currently, InsurTechs are using AI in three main areas: Customer Experience (58%), Product Innovation (43%), and Process Improvement (19%) — according to a McKinsey report. An organization’s ‘Front Office’ strategy will need to embody intelligent sales force automation, call-centre management, help-desk applications, product configuration and risk assessment tools. Insurance Carriers are restructuring these operations with an outward focus — aimed at improving interactions with their customers. 

While the Insurance back-office is focussed on streamlining in-house operations, the front office is responsible for driving customer experience, engagement and behaviour. However, most front-office operations deal with repetitive customer-facing jobs. Using Artificial Intelligence-based technologies such as RPA, tasks that require human mediation can now be handed over to automation technologies that imitate human interactions. Gartner estimates 20% of RPA will be cloud-based by 2022.

The real benefit of undergoing automation transformation is that both the front & back office can now be contextually linked in a smart manner — avoiding ‘working in isolation’ for extended periods. Customer-facing agents and reps can access information across the back-end more reliably and faster than before. Automating even routine tasks such as updating customer information, performing security checks, fetching product details or updating complaint forms — can reduce resolution times and the potential for manual errors.

This allows the front-office staff to focus on the most pressing matter — the relationship with the customer.

Customer servicing can now take place at incredible scale and complexity using chat, mobile and voice self-service tools. For example, speech recognition can capture what type of service to offer the customer (eg: update contact information, access policy details etc). These tools can also detect ‘anger’ or ‘frustration’ from the tone of voice and the information is passed to front-line reps who can quickly resolve an issue. As a result, remote diagnostics and self-service tools will see enhanced adoption over the coming years. The market for AI-enabled technologies in the claims process alone will be worth $72B by 2020.

5 key front-office operations that can be improved with AI

  1. Underwriting
    The most central function within the insurance value chain is to price risk. Using AI, the insurance underwriting process is now empowered with real-time insights derived from models analysis tons of customer-centric data.

    Using historical data, machine learning models can be trained to understand ‘known risks’ based on experience. For ‘unknown risks’, IoT sensors play a crucial role — by delivering a real-time picture of an ongoing operation. This allows for a second model to infer risk based on current data and the entire historical record of that specific process.

    Armed with in-depth knowledge about risk, insurers are moving from traditional risk pricing to a more proactive risk mitigation role. Through this new approach, carriers can set up real-time risk alerts, predict fraud and more accurately forecast ‘claims occurrence’ across the customer life cycle.

  2. Policy Administration
    A policy administration system is a backbone that manages all the policies within an insurance company. From the first point of interaction to fetching data from the back-office — most, if not all core operations run through this system. However, most insurance organizations still rely on legacy systems that require tremendous workaround using manual efforts.

    According to a study by Celent, nearly 45% of Insurance CIOs identified disconnected and duplicative legacy systems as a key inhibitor to digital transformation.

    Today’s challenging market dynamics and competitive pricing pressures are changing this approach. There are several areas worth investing in for carriers such as image & voice recognition to capture and authenticate customer information at the initial contact stage to intelligent entity extraction tools for understanding even handwritten text from a physical document.

    Automation enhancements help drive policyholder retention by improving connectivity to the back-end and delivering the most optimal outcomes for front-office workflows.

  3. Claims Management

    Claims are the most widely scrutinized function within the insurance value chain. Most claims servicing is performed by human agents over the phone. With speech recognition, these conversations can be automatically transcribed/ translated in real-time. This frees up more agent time to handle greater issues while leaving automation enabled self-service to handle the most basic customer queries.

    Claims assessment or loss estimation itself can be performed remotely using image recognition tools linked to algorithms that can calculate the payout for the policyholder.

    Without the need for human intervention, straight-through processing can be dramatically improved by reducing processing time — allowing human agents to react faster to policyholders demands.

    Also, read – How AI can settle claims in 5 minutes!

  4. Marketing & Sales Distribution
    According to Salesforce, only 36% of the average salespersons’ week is spent selling. Human sales reps typically spend a large portion of their time nurturing unqualified leads. With sales funnel maximizers, like LCA, reps can get quick access to leads that have been scored, prioritised and allocated for the right agent to optimize conversions.

    Distribution and sales chains are moving to a completely digital and affinity-based ecosystem. Chatbots and virtual agents can, therefore, play a critical role in increasing cross-sell and up-sell opportunities. These AI-enabled tools are fitted with Natural Language Processing (NLP) capabilities to contextually interpret the interaction with the customer.

    AI also leverages predictive analytics to produce behavioural insights when pitching the customer — allowing the agent to ask the right questions, address unmet needs and resolve anticipated near-term challenges.

  5. Product Personalization
    Using Machine Learning algorithms to precisely price risk, allows Carriers to understand the complexities involved in new product development — especially measuring the ‘unknown risks’ involved in creating new product lines.

    Data (both historical and IoT derived) coupled with predictive analytics can offer more personalised guidance to insurance buying. InsurTechs are poising themselves strategically in this area, ahead of the large carriers, to attract a new and younger customer base. Companies like MetroMile, Trov and Lemonade have been able to create unique offerings with AI-derived insights fine-tuned to the individual, while also charging much lower premiums than the market.

    New customers are able to buy convenient, sachet-type, even pay-as-you-use modelled insurance products for protecting their assets (mobile, laptop, home appliances, short travel, vacations etc). This has brought about an appetite for on-demand insurance where insurance can be bought, queries can be resolved and claims can be processed, all within a few minutes.

Other Customer-Facing Areas improved by AI

1. Proactive Front-Office Processes 
2. Precise Risk Mitigation/Active loss prevention
3. Chatbots and Robo-advisors 
4. Real-time Underwriting 
5. Accurate Claims Processing 
6. Direct Marketing & Cu0stomer Retention
 7. Bespoke Insurance Advice
 8. Understanding User’s Emotions 

Forrester predicts the impact of intelligent automation — through evidence in ‘the service desk’. They claim: automation will eliminate 20% of all service desk interactions, by the end of 2019. Enabling human workers with digital assistants in the insurance front-office has scope for very high disruption. Human agents are prone to making repeat errors that automation equipped with AI can fix easily — especially in routine and repetitive tasks.

Carriers, now have the opportunity to boost their market position by improving agent productivity, reducing operational inefficiencies like reprocessing, producing errorless transactions for customers and thereby creating an uninterrupted service chain.
Mantra Labs solves the most challenging front & back-office operations plaguing the Insurance value chain. To know more about our work in this space, reach out to us on hello@mantralabsglobal.com.

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The insurance industry is changing and experts predict — nearly one-third of existing insurance models will disappear within this decade. The fierce competition, new opportunities with technologies like AI, and on top of that millennials’ changing preferences sum up to the call for more flexible and consumer-facing business models. Here are four new business models to set the insurance archetype.

Source: The Deloitte Global Millennial Survey 2019 

Social Good & Transparency as a Business Model

Currently, AI is being used to strengthen the capabilities and knowledge of insurers and not consumers, creating information asymmetry. But, the question is — for how long will the consumers accept being a victim of ignorance. 

A possible solution to this situation is bringing information transparency. It’s not like traditional insurers don’t share policy information with their customers. They do. However, lengthy policy documents and customers’ reliance on agents for information shadows the actual coverage, terms, etc. In a way, the information that customers receive becomes dependent on the agents’ knowledge and intentions.

Translating policy, terms and conditions documents into consumable bits of information with a clear distinction between what’s covered and what’s not will help in achieving transparency between insurers and customers.

For instance, Lemonade — the American Insurtech for renters and home insurance, disrupted the industry lately with their instant and transparent end-to-end insurance process. Their consumers are better aware of coverage and claims thanks to simplicity in the user experience. 

Moreover, Lemonade donates the unclaimed premiums to social causes their consumers care about. From its inception in 2015 to date, Lemonade has sold over 1.2 million policies, in complete transparency and all through their AI bot — Maya!

Nearly 46% of millennials are willing to make a positive impact on the society/community. Lemonade has partnered with 92 charities and has donated $8,46,849 from unclaimed premiums. Hence, the answer.

Similarly, Swedish InsurTech Hedvig has successfully deployed it’s “nice insurance” services, giving back 80% of the unclaimed premiums to charities chosen by the customers.

More insights on — millennials and their expectations from insurance ‘beyond’ convenience.

B2B2C or API-based Model

When user acquisition is the top priority, B2B2C or API-based model comes into action. Also known as an open-source platform solution, this business model connects people and processes with technology infrastructure and assets to manage user interactions. 

In the API-based model, apart from traditional distribution channels, 3rd party apps also become a medium for customers to buy/access insurance policies. Automation plays a key role in this insurance model. Here, any other customer-centric digital application can install the API without manual/human intervention.

For example, in January 2018, Allianz announced that it will offer parts of its Allianz Business System (ABS) to other insurance companies for free. Interested organizations can simply install the API (Application Programming Interface, which is nothing but a chunk of software that connects two different apps) and start selling Allianz policies to their customers.

Lemonade — after disrupting the insurance space through transparency, has now stepped into this model. In October 2017, the company launched its public API, allowing anyone to distribute Lemonade’s policies through their websites or apps.

“It takes years to pull together the licenses, capital, and technology needed to offer insurance instantly through an app, which is why it’s almost nonexistent. Today’s API launch changes that. Anyone with a slight familiarity with coding can now include these capabilities in their app, in a matter of hours.”

  • Shai Wininger, Co-founder, President & COO, Lemonade

P2P Insurance

Unclaimed premiums also contribute to conflicts between insurers and policyholders. What if a customer is not interested in donating to charity, unlike mentioned in the above case? 

Peer-to-Peer (P2P) insurance is perhaps an answer to eliminate premium settlement conflicts. It is also an emerging business model to access insurance coverage at lower costs than most of the traditional insurances. 

This insurance model pools the individuals who share at least one relation — friends, family, or interest (community/clubs) and it serves two-fold benefits-

  1. Every member knows other members, funds available, and claims initiated/processed. Therefore, irrespective of the information shared by the insurer, there’s a transparent collaboration among peers.
  2. Since the members know each other socially, there’s a negligible chance of fraudulent claims. For instance, in the US alone, insurance frauds amount to nearly $80 billion/year.

Also read – how behavioral psychology is fixing modern insurance claims

The notion of financial protection for the community has been prevalent in our societies since the 1600s. In the middle ages, the tradesmen followed the guild system (an association of craftsmen and merchants), where participants paid fees as a kind of insurance safety net. Though, the successful conceptualization of P2P insurance in the modern business models dates back to 2010 with German InsurTech — Friensurance. However, the P2P insurance model has credited the success to many more InsurTechs like Guevara, Axieme, TongJuBao (P2Pprotect), and PeerCover

Microinsurance

The greatest limiting factor for the success of microinsurance is distribution. For example, in the US, 18% of the premium represents the distribution cost, set aside marketing and advertising costs. Availability isn’t the issue for microinsurance. 

The new business model for microinsurance focuses on outreaching and distributing policies at scale. Workflow automation solutions like document processing, automated customer query resolution, etc. make microinsurance models more effective. 

  1. Aggregator model: Instead of traditional agents, retailers, utility or mobile network operators, etc. can be intermediaries for the distribution of microinsurance policies. They provide access to a very large consumer base and even more with free and freemium coverages. For example, Check24, a European aggregator together with HDI insurance developed AurumPROTECT that is available exclusively through aggregators channels. 
  2. Harnessing proxy insurance sales force: Banks have been the ideal partners to distribute microinsurance policies at scale for ages. But, for short-term policies, this is a good time to utilize the agents of other products to offer insurance as an ancillary product. For example, Ola — an Indian cab aggregator provides a number of travel-related microinsurance underwritten by Acko General Insurance. 

The Bottom Line

The effectiveness of each of these models drills down to the smart use of technology in their implementations. Moreover, most of these business models are automated, thus, eliminating additional human resources for implementations. For instance, in India, an agent can charge up to 20% of the premium amount as fees, which can reduce significantly if the distribution is automated. Investment in technology for automating operations is also worth it because it makes customer outreach simpler and faster. 

Also, read – 5 Front-office operations in Insurance you can automate with AI.

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As Albert Einstein said — only a life lived for others is worth living. Following the Nobel trail, Mantra Labs conducted a blood donation camp in association with the Indian Red Cross Society on January 9, 2020. 

According to WHO (World Health Organization), blood donation by 1% of a nation’s population can meet the minimum basic requirement. As per this norm, India requires 13.1 million blood units annually (1% of 1.3 billion population). However, since 2013, only 7-9 million units of blood are collected annually, which is much lower than required.

Mantra Labs’ humble contribution to save the lives in danger and encourage its people towards humanity was seen in their active participation.

In this virtuous initiative, more than 38 tech enthusiasts from the company’s headquarter at Bangalore, donated blood and many more registered themselves for emergency requirements. 

Blood Donation Camp at Mantra Labs

Donating blood was an excellent feeling in itself. I was not only motivated by the fact that I’ll be saving lives but also it made me feel like a Marvel Avenger. However, it’s the only way at your workplace to get some “me” time: relaxing totally guilt-free, all while saving lives! 😛

Tuhina Chattopadhyay – Marketing Associate, Mantra Labs

Take a glimpse of the blood donation camp at Mantra Labs office

About Indian Red Cross Society: Founded in 1920, the Indian Red Cross Society (IRCS) is a humanitarian organization to protect human life and health. In India, the first blood bank was established by IRCS in 1942 in Kolkata, WestBengal. The organization aims to phase out replacement donors and achieve 100% voluntary blood donation in the future. The Indian Red Cross Society collects approximately 25000 units of blood annually.

Website: Indianredcross.org

About Mantra Labs: Mantra Labs is an AI-first products & solutions firm providing innovative applications in InsurTech and Consumer Internet domains. With over 150 technology tinkerers and experimentalists, the company has been leading technology initiatives for clients like Religare, Aditya Birla Capital, Myntra, Ola, and many more.

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