Notable data science skills for 2022

Published by Analyttica

Standing in 2022, we witnessed a worldwide epidemic along with numerous other natural calamities mere months ago. Not just that, records show that since 2017, there has been a steady increase in expenses incurred for natural disasters almost across the globe. During these phases, the insurance industry offered its services to millions of people each day. In fact, it is safe to assume that they witnessed sky-high premium volumes with no signs of slowing down. But the question here is how insurance companies manage the vast amount of data involved with these massive premiums.

The answer to this question is pretty straightforward – Machine Learning. Without AI technologies like Machine Learning, it would be next to impossible for the industry to process all that complex data without encountering insurance fraud.

Thus, making ML practices a must for attaining business goals like operational efficiency, improved customer service, processing claims, and managing risks. Here’s how ML fits into the insurance industry and makes all the difference.

What do we mean by Machine Learning?

To begin, Machine Learning is a technology that falls under Artificial Intelligence. It aims to analyze vast ranges of complex data to allow prediction and decision-making based on pattern identification and prior outcomes.

At present, Data Scientists are directing their efforts toward creating ML models that can help automate claims, underwriting, cross-selling, and risk management. This can help automate repetitive tasks, cutting down on expenses and saving time.

Benefits of ML and its associated models in processing claims during natural calamities

Talking about how ML modeling techniques leave a mark in the insurance industry, it is hardly possible not to mention predictive modeling. Currently, numerous avenues in the insurance industry are benefiting from its introduction and ability to comprehend large data sets. Below are some of the top segments related to natural calamities where ML predictive analytics is making all the difference:

  • Processing claims with Deep Learning

The insurance industry’s most extensive and time-consuming factor in the aftermath of a big disaster is processing claims. This includes reporting and filing claims, conducting physical investigations, evaluating claims, approving them, and paying accordingly. All this helps navigate the primary purpose of the insurance industry, which is making accidental losses manageable.

Sadly, there are innumerable cases of fraud claims that tend to loom over the insurance industry. These include duplicate claims, fake identities, false reports, and fabricated receipts for repair costs. With deep learning techniques, these challenges can be mitigated by:

  • Spotting correlations in data
  • Conducting facial recognitions on claim applications
  • Labeling historical data for supervised learning
  • Accelerating the verification process to reduce data breaching

In recent times, tapping into weather data via drone-based aerial surveys has proven to be a useful method of gathering image data subsequent to any natural calamity. It helps determine the level of damage before and after the calamity and its resultant payout. But analyzing these data sets is no easy feat and requires the involvement of Deep Learning algorithms like Convolutional Neural Networks (CNN). It can analyze and classify any image inputs based on various aspects and objects present. Thus, allowing the opportunity to automate similar claims and curb the processing costs and time to a bare minimum.

  • Tracking storms with Roof-top Sensors

In most parts of the world, abrupt climatic changes such as hailstorms and pouring thunderstorms are the cause of numerous damage claims. However, time and again, the absence of accurate hail data after any storm has resulted in numerous false claims.

However, now those days are long gone with the introduction of ML-enabled rooftop sensors that can track every house affected. It can help insurance companies improve claim accuracy regarding who gets paid and how much. Not just that, but by leveraging big data, this innovative ML technique can forecast even meteorological events, such as floods, landslides, and droughts. To top it off, it has also been proven equally valuable for detecting the vulnerability of rooftop materials to storms via drone imagery. Thus, making it possible to send alerts to residents beforehand, allowing time for protective measures.

  • Assessing future claims via Drone Imagery

With the ever-increasing climatic change, the severity of natural calamities like floods, droughts, and hurricanes is also on the rise. Naturally, it has led to the rise of recorded disasters and resultant insurance claims.

This is where technologies like satellite imagery powered by Machine Learning (ML) can help insurers during the underwriting and claim-handling process. It can spot weather patterns, including the intensity of heat, rain, and waves, via flight altitudes and high-resolution cameras. These 2D images and 3D maps can help forecast future natural disasters and high-risk zones likely to be affected.

Lastly, sending professionals into the field can be expensive, time-consuming, and risky after any natural calamity. With this technology, insurers can verify claimants and ensure quick payouts to those in need.

To sum it all up

The concept of weather has been and is always likely to be a challenge for the insurance industry. Its unpredictability and irregularity are likely to grow in the coming years. This makes investing in powerful technological allies such as Machine Learning, Artificial Intelligence, and Data Analytics via Ed-Tech platforms like LEAPS by Analyttica the need of the hour for insurance companies. Its ability to leverage unstructured data from ariel images and videos to understand the extent of damage without physical inspection is undoubtedly the next big thing in the realm of insurance.