Home > COVID-19’s Impact on Industries : Series A – Auto Insurance Companies

Sectors that gained revenue during pandemic and forecasted to be stable post pandemic

Series 1 – Auto Insurance Companies

– Advanced Analytics Practice

Observations:

  1. During lockdown, auto insurance companies benefited from the low number of insurance claims and expenses. Post lockdown, there is no expectation of increase in claims compared to normal periods.
  2. However, insurance companies that charge insurance based on miles driven (premium for pay as you drive) have faced significant reduction in revenue during the lockdown period.

Industry Insights:

  1. GEICO (Government Employees Insurance Company) [Reference] generated a pre-tax underwriting gain of $984 million i.e. an increase of 27.8% from the year-ago period of $770 million. Claims frequency at GEICO fell during the first quarter in multiple domains, namely, property damage and collision by 12% to 14%; personal injury by 9% to 11%; and bodily injury by 6% to 8%.
  2. The lower miles driven in March by all types of vehicles, and in turn lower claims frequency, were particularly apparent in combined ratios in the financials of Progressive Corporation [Reference]. Combined ratios in the agency and direct auto businesses of 74.2% and 75.2% marked declines of 11.5 and 13.3 percentage points, respectively, from what had been in March 2019.
  3. Metromile [Reference] (pay as you drive insurer) has furloughed and parted ways with about one-third of its staff or about 100 people.

Challenges:

  1. Decreased opportunity to sell one-size-fits-all policies to customers.
  2. Claim resolution under social distancing.

Artificial Intelligence Powered Solutions:

  1. Driving behavior insights – Insurers now need to move towards more personalization in its offerings to customers. Insurance premium needs to be decided based on “how much customer drive his car” and “how he drives his car”. AI is helping insurers to understand driving behavior and assign a driving score based on how insured accelerate, break, turn etc.
  2. Product Creation Assistance – Insurers can create products based on individual needs and lifestyle so that customers only pay for the coverage they need. This increases the appeal of insurance to a wider range of customers. Machine learning algorithms can combine public data with proprietary insurance data to enable the creation of better insurance products for customers. By streamlining and speeding up the collection and analysis of massive data from owned channels, third-party sources and agents, insurers can use machine learning to discover customer trends and interests in real time. These insights can then be used to develop and improve product and policy design.
  3. Cognitive Claim Investigation – Because of social distancing, claim resolutions will rely more and more on digital technologies such as computer vision and telematics data. Post-crash photographs are compared with image database using computer vision to determine the severity of damage, estimate repair costs and analyze the impact on future insurance premiums. Deep learning algorithms are leveraging telematics data to not only provide real-time crash detections, but also provide a detailed report by analyzing the motion sensor data and associated contexts. By determining driving events just before a crash (such as a speeding event, an evasive maneuver or distracted driving), insurance companies are finding clues to the cause of the accident and resolving claims faster.

You can read the next series here: Retail & Consumer Packaged Goods (CPG).

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