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5 Deep Learning Use Cases for the Insurance Industry

4 minutes, 9 seconds read

In 2010, with the launch of the Image Net Competition, a vast dataset of about 14 million labeled images was made open-source to inspire the development of cutting-edge image classifiers. This was when Deep Learning technology got its a real breakthrough and since then there’s been no looking back for advancements in this field.

Different industries are actively using Deep Learning for object detection, features tagging, image analysis, sentiment analysis, and processing data at extremely high speeds. The bigger benefit that differentiates Deep Learning from other AI and ML technologies is the ability to train vast amounts of unstructured data in near real-time. Organizations with a strong focus on data are already about 1.5 times more likely to invest in Deep Learning for actionable insights — Forrester Predicts.

What makes Deep Learning Technology so sought after?

Let’s take a look at 5 Deep Learning use cases from an insurance perspective.

5 Noteworthy Deep Learning Use Cases in Insurance

Deep Learning (DL) is a branch of Machine Learning, which is based on artificial neural networks. DL techniques are specifically useful for determining patterns in large unstructured data. It is highly beneficial for assessing damages during an accident, identifying anomalies in billing, etc. that can eventually help in fraud detection and better customer experiences.

The insurance industry can leverage Deep Learning technology to improve service, automation, and scale of operations. 

1. Property analysis

Typically, insurers analyze a property only once before quoting an insurance premium. However, a customer may remodel the property, for instance, install a swimming pool. 

Under such instances, Insurers can proactively modify the insurance coverage with the help of deep learning technology. In fact, with DL technology, Insurers can help their customers with predictive maintenance, fault analysis, and real-time support. 

For example, Enodo provides underwriting for multifamily properties. It allows users to analyze historical rent, concession data, and market values. Such data-driven tools are also a great aid for insurers.

2. Personalized offers

Insurers are seeking different ways to enhance the customer experience. Deep Learning can vividly improve interaction experiences at different customer touch-points. Take for instance — marketing outreach. Through personalized recommendations and dynamic remarketing strategies, insurers can achieve better conversions. McKinsey states that personalization can reduce customer acquisition costs by up to 50%

At the core of these strategies lies Deep Learning technology. DL technology can make logical classifications of unstructured data through unsupervised learning. We’ve already seen product recommendations based on our own preferences, browsing/search patterns, and peers’ interests. The same applies to the insurance industry, especially when insurers endeavor profits through bite-size and on-demand insurance products.  

3. Pricing/Actuarial analysis

Actuarial analysis and evaluation are both time-consuming and error-prone processes. Insurers can considerably improve policy pricing through automated reasoning. Deep Learning techniques combine statistics, finance, business, and case-based reasoning and can assist actuaries in better risk assessments. Accenture reports — Insurers are leveraging machine learning for underwriting in P&C (56%) and life (39%) insurance sectors

  1. Explainable AI (XAI) is capable of adopting and implementing AI across all capacities of the actuarial profession. 
  2. Pattern recognition from historical data can help assess the risk and understand the market better.
  3. Deep Learning can help in pragmatic actuarial solutions to make effective decisions on large actuarial data sets.

4. Deep Learning Use Cases in Fraud Detection

In Norway alone in 2019, there were 827 proven fraud cases, which could have caused a loss of over €11 million to insurers.

Insurance fraud usually occurs in the form of claims. A claimant can fake the identity, duplicate claims, overstate repair costs, and submit false medical receipts and bills. Mostly because of disconnected information sources, Insurers fall victim to fraudulent activities from customers. Now, here’s the challenge. How to unify different data sources, which, to date, even include offline receipts and manually scanned documents. 

Deep Learning can help in fraud detection by-

  • Finding hidden/implicit correlations in data.
  • Facial recognition, sentiment analysis on submitted claims application.
  • Supervised learning to train the fraud detection models using labeled historical data.
  • Eliminating the time lag in the verification of documents, which raises the potential for data breaching.

5. Claims

Deep Learning incorporates two-fold benefits to insurers in terms of claims. One — with a connected information ecosystem, it helps insurers with faster claims settlement (thus, customer experience as well). Two, deep learning predictive models can equip insurers with a better understanding of claims cost. 

For example, Tokio Marine — the largest P&C insurance group in Japan uses a cloud-based document processing system to process handwritten claims from the time of the first intimation. Many insurers are looking forward to end-to-end claims processing systems with deep learning and other AI capabilities. 

webinar: AI for data-driven Insurers

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 and decision-makers on April 14, 2020.

The Crux

Today, Deep Learning technology is able to mimic an infant’s brain. The research is on for developing new neural network architectures (e.g. Siamese Network, OpenAI’s GPT-2 Model, etc.) that will be capable of performing complex functionalities of a mature human brain. Deep Learning technology, in the near future, will be leading the development of cognition-based insurance systems.

Also read — The Cognitive Cloud Insurer is Next!

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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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