Case study Big Data Insurance

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CASE STUDY

Insurance based on

User Driving Behaviour

Author – Rajnish Goswami - CEO

Ingenious Qube Pvt. Ltd.

[email protected]

[email protected]

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© Copyright. www.ingeniousqube.com

CASE STUDY

Insurance based on User Driving Behaviour

The Situation

Insurance companies around the world strive to provide lower insurance rates and auto

insurance is no exception to this phenomenon. The automobile insurance companies are

devising ways to derive innovative pricing model that would help the customers reduce their

insurance premiums, however it requires understanding of how one drives the vehicle.

Insurance companies determined the premiums not just by the driver’s/vehicle’s history, but

also by statistical probabilities including age and gender.

Usage-based insurance (UBI) also known as pay as you drive (PAYD) and pay how you drive

(PHYD) and mile-based auto insurance is a type of vehicle insurance whereby the costs are

dependent upon type of vehicle used, measured against time, distance, behaviour and place.

This differs from traditional insurance, which attempts to differentiate and reward "safe"

drivers by giving lower premiums and/or a no-claims bonus.

“Pay How you Drive” (PHYD) car insurance, enables the insurance companies to charge the

client based on how they actually drive their vehicle. This radically changes the insurance

model where distance driven and insurance premiums are not aligned (except sometimes

based on declaration).

This requires integration of several technologies to enable an end-to-end system including an

in-vehicle device, communication network and an application platform hosting a number of

capabilities including a Geographical Information System (GIS) and Customer Relationship

Management (CRM) system.

The European and North American motor insurance industries have been operating at a loss

due to price pressure and the customer acquisitions costs related to the need to hold a

statistically viable data set that actuaries can analyse to define risk against.

PHYD based insurance is able to offer an alternative to this method by charging an insurance

premium based on the actual risk of the driver and reduce the size and volume of claims by

between 17% and 50%.

Implementation

PHYD requires an insurance company to source the driving data and is usually achieved by

offering a traditional policy at a discount and by installing a telematics device. The driver’s

data can be analysed against existing risk measurement tools and insurance segmentation.

Products can then be structured around charging for high risk behaviours or reducing the

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premium for low risk. The complexities of the charging regime are only limited by the ability

to communicate the proposition to the end user.

PHYD has traditionally been targeted at the extremes of insurance customer segments that

are either associated as high risk or safer drivers, some examples are:

• Young Drivers (under 23 years old) – size of premium

• Family Drivers (females between 30 – 45 years old) – safe defensive drivers

• Retired Drivers (over 55 years old) – law abiding & low speed drivers

• Green Drivers (male / females 30 – 70 years old) – reduced mileage &

environmentally aware driving

Ingenious Qube did an analysis on the data provided by its client which was procured from

the sensors installed on the vehicles. The sensors installed in the vehicles send the data every

5 seconds to the backend from where the client sends us the csv files in zip format for further

processing and data analysis. The analysis which we have done was based on user behaviour

on below aspects –

• Over speed

• Hard Breaking

• Hard Acceleration

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The data has provided the ability to our client to better its services and to provide best

premium rates to its customers based on the above data.

This helps in defining actual risk rather than projected risk at a specific underwritten group of

users based on their driving behaviour. The transition from projected to actual risk is based

on the insurers’ ability to identify new risk factors from the data and apply these within a

product.

Driver activity data can be mapped and used to identify generic high risk behaviours. This

method of detecting risk by applying algorithms to automatically analyse the data is

sometimes referred to as the driver fingerprinting or DNA.

Mapping driver behaviour. Source: Norwich Union PAYD driver behaviour fingerprint

Technology

Ingenious Qube helped the client in identifying the technology to be used for the POC. We

have used Hortonworks, HBase, Pig, Hive , Apache Solr, SQOOP. The proposed solution is not

only highly scalable, but it also securely connects disparate databases / data-sources with

replication of the data which provides availability of data under all circumstances. Also, the

proposed system is capable of processing huge chunks of data (Terabytes/Petabytes) in much

faster manner as compared to the traditional databases.

Potential Benefits

• Social and environmental benefits from more responsible driving.

• Commercial benefits to the insurance company from better alignment of insurance

with actual risk. Improved customer segmentation.

• Potential cost-savings for responsible customers.

• Technology that powers UBI/PHYD enables other vehicle-to-infrastructure solutions

including drive-through payments, emergency road assistance, etc.

• More choice for consumers on type of car insurance available to buy.

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• Higher-risk drivers pay most per use, thus have highest incentive to change driving

patterns or get off the roads, leaving roads safer.

• For usage-based insurance: Continuous tracking of vehicle location enhances both

personal security and vehicle security. The GPS technology could be used to trace

the vehicle whereabouts following an accident, breakdown or theft.

Case study Big Data Insurance

Data & Analytics

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