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Date: 06/04/2019

Title: Catastrophe Claims Management Using Advanced Emerging Technologies

Teaser: Disruptive use cases in property and casualty insurance and a cutting-edge technology solution

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Catastrophe Claims Management Using Advanced Emerging Technologies

Digitizing post hurricane claims adjustment process using aerial imagery and deep learning could disrupt the P&C insurance sector in the wake of catastrophe. This article describes challenges in claims management, disruptive use cases in P&C and some cutting-edge technology solutions to mitigate these challenges.

Authors: Udit Anand | Anmol Gandhi

Managing claims after a catastrophe is a lot like parachuting into a war zone. Properties are destroyed, areas are inaccessible, and policyholders are at their most vulnerable. They may have lost their homes, their valuables and perhaps their cars. They could be injured, and they may not have eaten or slept for a long time. To achieve success in a war zone, insurers need an army of people who are very well trained, supremely organized, meticulously disciplined, and thoroughly committed to the mission. This applies not only to a carrier’s frontline troops (their adjusters and agents) but also to their commanding officers (claims managers) and logistical support (investigators and attorneys) back at HQ. If your catastrophe claims unit lacks any of these prerequisites, the mission to quickly take care of policyholders and settle legitimate claims, while remaining vigilant for fraud by opportunists will almost certainly fail.

The claims management process experience has five major issues that insurers face in the wake of a catastrophe:

  • Inefficient claims management: Due to high number of claims that are coming through at the time of catastrophe, it becomes difficult to manage claims.
  • Restricted access to affected areas: Well-connected areas can become inaccessible making it difficult for adjusters to reach the affected areas. Hence, causing a delay in the claims-adjustment process.
  • Lack of resources: The adjusters to claims ratio increases exponentially during catastrophes further delaying the adjustment settlement.
  • Data quality: Underwriters rely on data to make their decisions. The quality of data can be severely affected in the wake of a catastrophe. Incorrect data due to a catastrophe may lead to errors and delay in the settlement process.
  • Dip in customer satisfaction: The claims adjustment process can become lengthy and cumbersome which might eventually delay the settlement for the affected customer.

There’s no shortage of technology for the insurance sector from post-storm claims processing to everyday policy underwriting. Data-driven technologies are helping property & casualty (P&C) carriers to not only minimize the effects of potential catastrophe but also prepare for them.

Future of P&C insurance – Data is the key

Data is the key, technologies like image recognition, aerial imagery and artificial intelligence can harness data to solve the data quality problem in the P&C sector especially in times of a catastrophe. Following are some examples of the technology disruption in the P&C catastrophe sector.

1)     Artificial Intelligence

  • Artificial intelligence (AI) isn’t exactly emerging – it’s been around for some years. AI aggregates and analyzes data faster than any human can.
  • For example, «Liberty Mutual» is reportedly working on a mobile app that would allow policy holders involved in a crash assess the damage to their vehicle by taking a picture with their phone. The app would utilize AI trained by thousands of images of car crashes to accurately assess the damage, potentially eliminating a lot of time-consuming steps in the claims process.

2)     Detect fraudulent claims

  • Fraud affects every industry, and insurance is no exception. False declarations to the subscription, false claims, and amplification of the disaster cause insurers to lose several hundreds of millions of dollars every year.
  • «Shift Technology», a French start-up working towards automating claims, can identify as much as 75% of fraudulent claims by aggregating and cross-checking the data of several undisclosed partners, such as insurers, which helps insurers speed up processes for bona fide customers.
  • The Shift Technology AI model compares multiple seemingly unrelated claims to detect unusual similarities in circumstances or invoices and detect statistically unlikely trends to raise a red flag for human investigation.

3)     Predictive disaster modeling

  • The sooner an insurance carrier knows how an event will impact an area, the faster they can begin processing claims and calculating losses.
  • Aon has formed a strategic alliance with «», a San Francisco-based InsurTech startup, which uses artificial intelligence (AI).’s new wildfire model (Z-FIRE) addresses this challenge by utilizing machine learning to combine vital property details – including vegetation, building materials, topography, weather patterns among others – with actual loss data.
  • These factors at the individual property level create a predictive risk score, which provides a critical tool to evaluate risk.

4)     Regulatory changes and more sophisticated catastrophe response

  • After the catastrophe caused by Hurricanes Irma and Maria, there was a need for an efficient recovery process.
  • The government has worked on methods to improve the way insurance claims are handled. More than 287’000 claims were filed, and of those, nearly 11’000 claims representing a total of nearly USD 2 billion have not been resolved, Rivera (an insurance carrier) said. Insurance companies struggled after the storm, including Real Legacy, which folded.
  • There was a need for law amendments, there are several changes one of which was «law 246» mandates that claims made to micro insurances shall be made within 30 days. Inference: speed will be an important factor.
  • Imagery from satellites, fixed-wing aircraft, unmanned aerial systems, and mobile devices can help adjusters understand what’s on the ground. In some cases, this can eliminate unnecessary site inspections and become agile.

As tempting it might sound, the above described technology is not a panacea. As straightforward as these solutions seem they can be intricate and onerous to implement. Challenges like setting up data pipelines, maintaining data quality, calibrating models to achieve accuracy, all start to play a major role in achieving the final outcome. Insurance companies are taking considerable steps in this regard. They are developing novel solutions using advanced digital solution using aerial imagery and deep learning to solve the incumbent problems.

Some of the companies using advanced imaging technologies are:

1)     Cape Analytics

  • Cape Analytics is all about saving insurance companies from having to send someone out to physically inspect a property, which is resource-intensive and may be hazardous if it involves climbing on roofs or other structures.
  • Instead, Cape Analytics can detect, for example, if changes have been made to a property since a previous valuation, with visual updates pushed out to the platform at regular intervals through the year.
  • The company partners with third parties such as «Nearmap», which provides high-resolution aerial imagery — and then extracts structured data from the visuals.

2)     Munich Re

  • New methods currently being explored by the global reinsurance giant. Munich Re could see hurricane victims receive claim payments on their damaged properties in the time it takes them to return to their homes.
  • Artificial Intelligence (AI) will play a key role in enabling this, in the immediate aftermath of a hurricane, aircraft fly over the affected areas, taking high-resolution images. Then, data is analyzed by AI-driven software to assess the damage to insured houses.

3)     Geomni-Verisk

  • Geomni has built a massive library of high-resolution imagery and 3D data by using multiple remote sensing platforms with a focus on fixed-wing aircraft but also including satellites, unmanned aerial vehicles (UAVs), and even mobile devices used to collect ground-level data through the Geomni Mobile app.
  • After a major catastrophe, Geomni aircraft, located throughout the United States, are deployed to collect post-catastrophe imagery and data.

All these companies with their solution address the issues that hit insurers the hardest during catastrophic events. Allowing insurers to focus on getting clients their money quickly and efficiently with the following benefits.

  • Agile and efficient: Since the estimated damage is known along with the locations, these solutions provide efficient and agile response
  • Precision: These solutions provide precise results for catastrophe claims assessment.
  • Economical: These solutions can help in understanding the resources required at the time of claims adjustment, hence economical.
  • Customer satisfaction: Faster and efficient claims management process leader to higher customer satisfaction.

In summary, it is an exciting time to be in P&C insurance sector where Advanced solutions developed by insurance and InsurTech companies are leveraging the use of aerial imagery, deep learning and image recognition disrupting the P&C insurance sector in the wake of a catastrophe.



Marcel Loetscher

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