New Data enabling New Insurance

Mobito facilitates the exchange of data between companies through an advanced data platform that allows companies to streamline their interaction with third party data and monetize their own data. A key industry we are focused on is the insurance market where incumbents and challengers are increasingly relying on external data to develop new products, improve their services and deliver better and more personalized user experiences. Below, we outline some of the developments that underpin the industry’s evolution and the key needs and challenges around successful data usage.

Insurance Fundamentals

Insurance is principally involved in the assessment and pricing of risk. It offers individuals, corporations and governments protection against future adverse events that may cause them financial loss. In its most typical model, the insured pays some recurring amount, i.e. the price of insurance products, to buy protection against specific damages that may affect an insured asset. Fundamentally, this price depends on the expected cost of the damage incurred and the likelihood of it happening.

Traditional Underwriting

Traditionally, insurers have relied on long historical records in order to understand the likelihood or frequency of an event happening. Analysing 50 years of flood records may allow an insurer to map probabilistically future likelihood of flood events and underpin their pricing and product features. In this case the model would need to also account for the fact that climate change has rendered historical data less representative as the location, frequency and severity of flood events is changing. In this sense, a lot of traditional insurance risk pricing relies on analyzing reliable historical data.

Claims processing

Importantly, when a claim is made for an insured product, say after hail damages an insured car, the underwriter needs to verify certain conditions are met and assess the extent of the damage, before releasing the claim- a process that can take months. In the 6 years between 2008 and 2014 the $ spent by US insurers for auto insurance claims against hail amounted to to $5.37 billion.

New Insurance

Two key drivers in improving insurers’ portfolio performance and the associated pricing of underlying risk are:

Data Uses

Third party captured data support insurers in the following 3 core business activities:

Automating part of the claims processing

A category of the insurance landscape that has been embracing a lot of these new capabilities is Parametrics Insurance. Companies in this space are proponents of a new model that disrupts the processing of claims of insurance products. A parametric insurance product usually includes a predefined threshold of some data measurement or parameter, which if reached can automatically or semi-automatically trigger the release of a claim. This way, parametric insurance pays a predefined amount after the occurrence of a triggering event. For example, companies might sell home sensors that measure flood intensity (e.g. in cm) so that when 4 cm of flood is reached locally, the insurer releases a predefined payment. Similar insurance products are focusing on risk of hurricanes tracked by wind-speed and barometric pressure, earthquakes tracked by the magnitude and even flight cancellations and pandemic/epidemic outbreaks.

New Data Needs

Automations of the claims’ cycle often relies on historical and (near) real-time data parameters:

  • that track metrics that correlate with the insured asset, such as footfall measurements for a mall operator or internet outage for logistic companies. In Parametrics, such complementary data can serve as checks to the triggering event. As Head of Product at parametric insurance provider FloodFlash Nyasha Kuwana puts it: this is a way of “looking out the window” to make sure context agrees with flood sensor measurement.

New Data Requirements

The opportunities unlocked by accessing real time data and these new sources of third party data places new challenges to an insurer:

  1. Data Integration and connection maintenance: 3rd party integrations and trouble-shooting for integration problems and downtime
  2. Data Preparation: cleaning, aggregating and normalizing data
  3. Data Intelligence: data analysis including AI/ ML processing

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