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AI Use Cases for Data-driven Reinsurers

Across the Insurance expansile, a special fraction within the industry is notable for its embrace of new technologies ahead of others. For an industry that notoriously keeps a straggling pace behind its banking and financial peers, Reinsurance has conventionally demonstrated a greater proclivity for future-proofing itself. In fact, they were one of the first to adopt cat-modelling techniques in the early ’90s to predict and assess risk.  This makes perfect sense too — ‘Insurance for insurers’ or reinsurance is the business of risk evaluation of the highest grade — which means there are hundreds of billions of dollars more at stake. 

Front-line insurers typically practice transferring some amount of their risk portfolio to reduce the likelihood of paying enormous claims in the event of unforeseen catastrophe losses. For most regions of the World — wind and water damage through thunderstorms, torrential rains, and snowmelt caused the highest losses in 2019.

In the first half of 2019 itself, global economic losses from natural catastrophes and man-made disasters totalled $44 billion, according to Swiss Re Institute’s sigma estimates. $25 billion of that total was covered by reinsurers. Without the aid of reinsurance absorbing most of that risk and spreading it out, insurance companies would have had to fold. This is how reinsurance protects front-line insurers from unforeseen events in the first place.

Yet, protection gaps, especially in emerging economies still trails behind. Only about 42 per cent of the global economic losses were insured as several large-scale disaster events, such as Cyclone Idai in southern Africa and Cyclone Fani in India, occurred in areas with low insurance penetration.

Reinsurance can be an arduous and unpredictable business. To cope with a prolonged soft market, declining market capital and shaky investor confidence — reinsurers have to come up with new models to boost profitability and add value to their clients.

For them, this is where Artificial Intelligence and the sisterhood of data-driven technologies is bringing back their edge.


Source: PwC – AI in Insurance Report

AI Use Cases for Reinsurers 

Advanced Catastrophe Risk Modelling

Catastrophic models built on machine learning models trained on real claims data, and ethno- and techno-graphic parameters can decisively improve the authenticity of risk assessments. The models are useful tools for forecasting losses and can predict accurate exposure for clients facing a wide range of natural and man-made risks.

Mining Data for behavioural risks can also inform reinsurers about adjusting and arranging their reinsurance contracts. For example, Tianjin Port explosions of 2015 resulted in losses largely due to risk accumulation — more specifically accumulation of cargo at the port. Static risks like these can be avoided by using sensors to tag and monitor assets in real-time.

RPA-based outcomes for reducing operational risks

RPA coupled with smart data extraction tools can handle a high volume of repetitive human tasks that requires problem-solving aptitude. This is especially useful when manually dealing with data stored in disparate formats. Large reinsurers can streamline critical operations and free employee capacity. Automation can reduce turn-around-times for price/quote setting in reinsurance contracts. Other extended benefits of process automation include: creating single view documentation and tracking, faster reconciliation and account settlement time, simplifying the bordereau and recovery management process, and the technical accounting of premium and claims.

Take customised reinsurance contracts for instance that are typically put together manually. Although these contracts provide better financial risk control, yet due to manual administration and the complex nature of such contracts — the process is prone to errors. By creating a system that can connect to all data sources via a single repository (data lake), the entire process can be automated and streamlined to reduce human-related errors.

Risk identification & Evaluation of emerging risks

Adapting to the risk landscape and identifying new potential risks is central to the functioning of reinsurance firms. For example, if reinsurance companies are not interested in covering Disaster-related insurance risks, then the insurance companies will no longer offer this product to the customer because they don’t have sufficient protection to sell the product. 

According to a recent research paper, the reinsurance contract is more valuable when the catastrophe is more severe and the reinsurer’s default risk is lower. Predictive modelling with more granular data can help actuaries build products for dynamic business needs, market risks and concentrations. By projecting potential future costs, losses, profits and claims — reinsurers can dynamically adjust their quoted premiums. 

Portfolio Optimization


During each renewal cycle, underwriters and top executives have to figure out: how to improve the performance of their portfolios? To carry this out, they need to quickly assess in near real-time the impact of making changes to these portfolios. Due to the large number of new portfolio combinations that can be created (that run in the hundreds of millions), this task is beyond the reach of pure manual effort. 


To effectively run a model like this, machine learning can shorten the decision making time by sampling selective combinations and by running multi-objective, multi-restraint optimization models as opposed to the less popular linear optimization method.  Portfolio optimization fueled by advanced data-driven models can reveal hidden value to an underwriting team. Such models can also predict with great accuracy how portfolios will perform in the face of micro or macro changes.

Repetitive and iterative sampling of the possible combinations can be carried out to create a narrowed down set of best solutions from an extremely large pool of portfolio options. This is how the most optimal portfolio that maximizes profits and reduces risk liability, is chosen. 

Reinsurance Outlook in India 

The size of the Indian non-life market, which is more reinsurance intensive than life, is around $17.7B, of which nearly $4B is given out as reinsurance premium. Insurance products in India are mainly modeled around earthquakes and terrorism, with very few products covering floods. Mass retail sectors such as auto, health and small/medium property businesses are the least reinsurance dependant. As the industry continues to expand in the subcontinent, an AI-backed data-driven approach will prove to be the decisive leverage for reinsurers in the hunt for new opportunities beyond 2020. 

Also read – Why InsurTech beyond 2020 will be different

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MantraTalks Podcast with Parag Sharma: Delivering Digital-first Health Experiences for Patient Care in the New Normal

6 minutes read

The healthcare industry took the brunt of the Covid-19 pandemic from the very beginning. It was, and still is, a humongous task for hospitals to deal with the rising number of COVID patients as well as handling the regular consults. 

To delve deeper into the state of healthcare in the COVID times, we interviewed Parag Sharma, CEO, Mantra Labs Pvt Ltd. Parag shares his insights on how technology can help in delivering digital-first health experiences for patient care in the New Normal.

Parag is a product enthusiast and tinkerer at heart and has been at the forefront of developing innovative products especially in the field of AI. He also holds over ten years of experience working in the services line and has been instrumental in launching several startups in the Internet & Mobile space. His rich domain expertise and innovative leadership have helped Mantra climb to the top 100 innovative InsurTechs in the World – selected by FinTech Global. 

Catch the interview:  

Connect with Parag- LinkedIn

COVID-19 and Its impact on Healthcare Organizations

Considering the COVID situation, according to you how has COVID-19 impacted the IT & service operations among healthcare organizations?

Parag:  Since the onset of COVID-19, the healthcare sector has been deeply impacted. Institutions are facing a serious crunch in manpower. IT support systems which were usually manned and managed by a large team of IT professionals are not available in the same strength. Resource allocation’ is one of the biggest concerns due to physical and mental exhaustion of the healthcare workforce. 

Hospitals are facing issues such as operational disruption due to staff quarantine, supply-chain delays and sudden decline in patient footfalls, difficulty in sustaining fixed costs, etc. People are not comfortable getting out of the safety confinements of their homes due to the rising risk of getting infected with the virus. Hospitals will have to reassess their future strategy and budgets in light of the uncertain economic situation.

Preparing for the Future

What can hospitals do to ensure the continuity of their customer-facing operations in the wake of a second Pandemic wave?

Parag: There are many things that hospitals can do to manage themselves in this hour of crisis. Being more digital than what they are would be one step forward for all of them. They can bring their IT systems to the cloud so that the person can access data and manage their work remotely. They can enable their patients to book appointments and enquire about services through apps and chatbots which won’t require them to call the reception or come to the hospital. These are some of the services which hospitals can provide to their customers with minimum physical contact. 

Related: Manipal Hospital’s move to a self-service healthcare mobile application

Hospitals can extend Telehealth services to their patients. Recently, telehealth has proved to be useful especially when there is asymmetry between the number of patients and healthcare providers. I think it will be very useful for healthcare institutions to deploy telehealth solutions to provide medical facilities to people who have so far been outside the benefits of healthcare.

New Expectations in Health Experiences

Is consumer behavior defined by the ‘new normal’ going to change the way we access healthcare from this point on?

Parag: Yes, people will expect a completely different way to access healthcare services from now on. Hospitals should gear-up and rise to this occasion. The pandemic has also provided a new opportunity to adopt a completely different approach in the way healthcare is delivered. They always felt that medical care cannot be provided remotely but now this is happening and people are appreciating remote healthcare services. Hospitals and healthcare institutions are convinced that telehealth and remote care will be more successful soon.

Technology in Healthcare can Bridge Operational Gaps

What are the operational challenges, as far as digital capabilities go, that hospitals are facing currently? And, what steps must they take to bridge these gaps?

Parag: Operational challenges are not just digital challenges. But a lot of these challenges can be addressed with technology. For example, Electronic Health Records which hospitals manage within the premises can be moved to the cloud so that the person can access these records on the cloud itself and need not come to the hospital. 

Related: Medical Image Management: DICOM Images Sharing Process

Secondly, if you deploy telehealth and telemedicine solutions, irrespective of where your patients are or doctors are, hospitals can deliver the required care to its patients. You can even extend your diagnostics services to your patients by giving them an application through which they can seamlessly book appointments for consults, diagnostics, or pathological services and resolve their queries, etc. Simply by giving a seamless interface either through bots or applications can go a long way in providing better health experiences to the customers.

Role of Chatbots in Superior Customer Experiences

According to you, what role does chatbots powered by Artificial Intelligence have in the Healthcare CX landscape?

Parag: Chatbots are the simplest example of the implementation of AI-based technology in healthcare. There are a lot of things which bots can do simplistically. For example, if a patient wants to book an appointment with the doctors, instead of going through a complex web applications and interfaces, what if I can simply write “I want to book an appointment with the doctor Dr. XYZ at 4 pm” and the bot can figure out in case the time slot is available with that particular doctor, it will confirm the appointment followed by a payment process if the payment has to be made upfront. 

Apart from this, you can extend your bots to provide e-consultations where doctors can do remote consultations via audio and video features of a chatbot. So there is a huge scope for bots beyond answering routine queries by customers or booking appointments. It does not stop just there. You can extend chatbot functionalities to support functions such as admin, HR, finance, and business process efficiency so that they can provide better services to their customers.

Related: Healthcare Chatbots: Innovative, Efficient, and Low-cost Care

Chatbot Use Cases in Healthcare

Could you tell us some possible bot use cases for delivering better customer experiences to digital health users?

Parag: Apart from booking appointments and resolving customer queries, these bots can conduct remote consultations, internal processes, health symptom checker, out-patient video consultation, second opinion consultation, ordering medicines, psychological counseling & mental wellness, scenario-based risk advice, Heroism Recognition for employees, etc. Also, it can be further extended to help patients enquire about health insurance related queries, and all the interactions between insurance companies and hospitals can be provided to the patient. 

Related: Healthcare & Hospitals Use Cases | Digital Health

The Road Ahead

COVID-19 has forced hospitals to revise patient support strategy with limited operational staff that is bringing every day a new challenge. A way out is to heavily rely on digital innovation.

In India we have a disparity between the no. of healthcare providers and care seekers. Without technology, I don’t think there is any way healthcare institutions will be able to scale to a level where they can provide meaningful services to such a large number of people. Hospitals can invest in setting up an information exchange; making the process as seamless as possible; and removing all possible inefficiencies from the supply chain through technology.

Future growth for hospitals will come from digital technology because patients will opt more for digital platforms. And it is up to hospitals to catch up with the pace at which modern technology is developing. We, at Mantra Labs, have achieved several use cases including hospitals/diagnostic centers that are able to deliver superior health experiences.

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