Page 40 - Insurance Times August 2019

or worn on the body as implants or accessories. process where all the insurers are at once notified about
Wearables allow the companies to capture data related an approaching calamity through data transmitted by
to the subject on a close monitoring system. Wearables sensors across regions. Thus, Blockchain will minimize
can provide for some serious data like the cholesterol human interaction giving rise to smart contracts and
and glucose levels in a human body that cannot be found eventually leading to reduction in process time.
otherwise without a formal medical checkup. This would Blockchain will also eliminate risks in the system by
also reduce healthcare costs for humans. Some of the identifying customer profiles, validating claims and
famous examples of wearables are Oura rings, which is avoiding duplication of transactions. This would boost
a ring to be worn on the finger of the subject. These rings the efficiency in KYC management and fraud detection.
studies the sleep patterns as well as heart beat rates of 2. Machine Learning: Machine Learning helps in finding
the subject which can prove to be a substantial piece of patterns in data in an automated manner using complex
data for the insurers. Another famous wearable was a
algorithms. All the multitude of available data can be
smart T-shirt worn by Ralph Lauren at US Open in 2014.
captured from new data sources using Internet of Things,
This t-shirt studies the heart beat rates, breathing
telematics and external data sources. This empowers the
patterns, the amount of calories lost while playing or
machine to think and tries to capture a specific trend in
while working out. Some serious concerns with wearable the data and predict the future outcomes of similar
technologies are: 1. It is an unchartered territory where occurrence of the event. Machine learning analyses the
we do not know how a human body would react to such unstructured data and starts making sense of dissimilar
wearables over our body all the time, 2. We might not datasets. Although machine learning has its own
be very comfortable on providing personal data of such disadvantages like initial cost of IT infrastructure, non-
magnitude to our insurers, 3. The security threat in terms readiness of adapting to new systems by the employees,
of technology would always persist where a hacking of
regulatory issues and the fraud and security.
such a wearable can be disastrous.
3. Robotics: Robotic Process Automation (RPA) is a
4. Bancassurance: Bancassurance is an arrangement in collection of tools like machine learning, virtual agents,
which an insurance company and a bank form a natural language classification and computer vision.
partnership so that the insurance company can sell its RPA can deal with the real time data, automation of
products to the banks client base. Banks gain from this claims in a structured manner without human
partnership since they earn extra income from the intervention, flexibility in claim settlement channel,
commission from sale of an insurance policy. They also integration of data and precision. The biggest concern
benefit since they get to add one more product to their with robotics is the encroachment of human jobs by
portfolio of products while selling services to the robots. Although the unskilled jobs will take an
customers. Insurance being a long term product can immediate hit, new jobs like coding, monitoring, risk
also help in customer retention for the banks. On the analytics and pattern recognition will be discovered.
other hand, insurance companies can get high market 4. Artificial Intelligence: Artificial Intelligence (AI) is
penetration rate by getting access to the bank's client changing the operational patterns of insurance
base. Also the employee cost reduces since bank tellers companies drastically. It plays a huge role in the
become a point of contact for sale of insurance policies.
underwriting process by using deep question answering
Hence this becomes a win win situation for the banks techniques so that the underwriters can attribute the
as well as the insurance companies.
risks associated with a certain policy through an
enhanced mechanism. It can also use predictive models
Technologies used in Insurtech of risk assessment with the aid of simulation modeling
The technology is being driven by the following fundamental for commercial and life products. AI also changes the
pillars that would give an additional thrust to the insurance claims management experience with the help of robotics
sector: to identify the bottlenecks in the system and make the
1. Blockchain: A blockchain is a digitized, decentralized, processes faster. It can also tap social media to keep a
public ledger of all cryptocurrency transactions. check on patterns of frauds in claims.
Blockchain is a decentralized system wherein multiple
parties share and update information thereby increasing It is estimated that only 10% of all insurance players will
operational efficiency and reducing time. Blockchain can have an algorithmic business strategy by 2019 thereby
convert the insurance ecosystem into a multi-dimension giving an early bird advantage to those insurance players

40 The Insurance Times, August 2019