Dynamic Insurance Pricing – Telematics Analytics & Behavioural monitoring

In the auto insurance industry, the terms telematics and usage-based insurance (UBI) are often used interchangeably – but they are actually two different concepts. Usage-based insurance is a broader concept that can be broken down into two categories: self-reporting policies and telematics-based policies. There are two main types of telematics-based insurance products: Pay-as-you-drive (PAYD) and Pay-how-you-drive (PHYD). Vehicle telematics refers to automobile systems that combine Global Positioning System tracking and other wireless communications for automatic roadside assistance and remote diagnostics. Telematics refers to solutions that are based on information flowing to and/or from a vehicle. When implemented, telematics have the potential to increase operational efficiency and improve driver safety in a number of ways; for example:

  • GPS technology tracks a vehicle’s location, mileage, and speed. Fleet and Distribution companies can use this information to optimize routes and scheduling efficiency.
  • Communications technology promotes connectivity between drivers and dispatch.
  • Sensors monitor vehicle diagnostics which can then be used to streamline vehicle maintenance.
  • Accelerometers measure changes in speed and direction, cameras that monitor road condition, and drivers’ actions. This information can be used to improve driver performance through a one-on-one or in-vehicle coaching program

Insurers want access to a database of telematics data to help them set personalized premiums for individual drivers, but arrangements governing how that information is gathered, managed and accessed could be subject to scrutiny by competition regulators. Telematics data can constitute personal data, and therefore fall subject to data protection laws, on the basis that it records the activities of individual drivers, or a number of individuals. Insurers will need to be able to make sense of this data via a model with predictive capabilities based on frequency of driving, hard braking, sharp turns, time of the day, and a handful of other factors, to determine the personalized premium for each customer based on risk profile. UBI programs offer many advantages to insurers, consumers and society. Linking insurance premiums more closely to actual individual vehicle or fleet performance allows insurers to more accurately price premiums. This increases affordability for lower-risk drivers, many of whom are also lower-income drivers. It also gives consumers the ability to control their premium costs by incenting them to reduce miles driven and adopt safer driving habits. Fewer miles and safer driving also aid in reducing accidents, congestion, and vehicle emissions, which benefits society.

Auto insurance is going through transformational changes due to weather, telematics, social data and advancement in auto technology. The next generation of insurance pricing will be very similar to utility industry pricing model that will be driven by actual usage, rate tier based on timing of the day, location and the customer risk profile. As auto industry gets matured with driverless car technology, the insurance will be driven by risk profile of car and technology vs. risk profile of individual customer. All data from self driven/driverless car will be fed back to isnruance companies through telematics data. Three insurance suppliers and an auto parts maker have warned in their most recent annual reports that driverless cars and the technology behind them could one day disrupt the way they do business. The industry collected $107.4 billion in passenger car auto insurance premiums in 2013, the latest year for which figures were available, according to the Insurance Information Institute. Self-driving cars could have other effects as well. Insurers expect car- and software-makers to face litigation when crashes do happen, shifting at least some of the expense from consumer auto insurance to commercial liability policies. A 2013 analysis by PricewaterhouseCoopers suggested that the company thinks driverless cars won’t impact their bottom line anytime soon, but that it will, eventually. The use of telematics helps insurers more accurately estimate accident damages and reduce fraud by enabling them to analyze the driving data (such as hard breaking, speed, and time) during an accident. This additional data can also be used by insurers to refine or differentiate UBI products. Additionally, the ancillary safety benefits offered in conjunction with many telematics-based UBI programs also help to lower accident and vehicle theft related costs by improving accident response time, allowing for stolen vehicles to be tracked and recovered, and monitoring driver safety. Telematics also allow fleets to determine the most efficient routes, saving them costs related to personnel, gas, and maintenance.

According to SMA Research, approximately 36 percent of all auto insurance carriers are expected to use telematics UBI by 2020. Based on a May 2014  CIPR survey of 47 U.S. state and territory insurance departments, in all but five jurisdictions – California, New Mexico, Puerto Rico, Virgin Islands, and Guam – insurers currently offer telematics UBI policies. In twenty-three states, there are more than five insurance companies active in the telematics UBI market

As the move toward dynamic pricing becomes more prominent, some firms will likely go farther than others. A lot of analysis and research will need to be done to determine how much of a risk can effectively be determined by static versus dynamic factors.


Big Data Use-cases – Insurance, Energy and Utilities, Travel and Hi-Tech

Big Data is still in its infancy stage of it’s life cycle and alike traditional EDW/BI, it will take couple years to be part of nervous system of an organization. From what I have seen recently, most of the real big data initiatives that are currently in Production are by start-ups and upcoming companies that are working on new product line around real-time analytics and integration. Some of them are actually offering their products through cloud as SAAS and specifically extending it to AAAS (Analysis as a Service). The Cloud and Big Data computing has opened a new market for innovators.

Let’s look at some of the use cases for few traditional industries here:

Big Data use-cases in Insurance Industry

At the recent insurance industry trade shows, “big data” was the talk of the floor. The broad, vague term means different things to different people, though, and like many buzzwords suffers a bit from overexposure. But most people see the potential of advanced data and analytics to make insurance operations more efficient and effective. Here are few of the use cases that are very relevant to Insurance industry and has high ROI potential

  • Fraud Detection & Analysis
  • Personalized Pricing:
  • Customer Sentiment Analysis
  • Catastrophic Planning
  • Call Detail Record
  • Loyalty Management
  • Social Media Analytics
  • Advertising and Campaign Management
  • Agents Analysis
  • Customer Value Management
  • Underwriting and Loss modeling

Big Data use-cases in Utility Industry:

With the proliferation of smart meters, utilities are finding themselves inundated with data as they build out the Internet-enabled, interactive power system called the smart grid. But according to Oracle survey, many utility companies have yet to exploit that data as they modernize the grid. “The average utility with at least one smart meter program in place has increased the frequency of its data collection by 180x– collecting data once every four hours as opposed to just once a month,” states the survey of 151 utility executives in the U.S. and Canada conducted in April. “Despite improvements, 45%of utilities still struggle to report information to business managers as fast as they need it and 50% miss opportunities to deliver useful information to customers”. Here are few of the use cases that’s candidate for big data implementation and analytics

  • Smart Meters – Notifications and Alerts,
  • Smart Meters – Real-time Usage Pattern Analysis
  • Smart Meters – Predictive Analysis for Distribution of power
  • Smart Grid – Weather Pattern and Real Time Usage and Distribution
  • Manage Disasters and Outages
  • Compliance Checks and Audits
  • Customer Sentiment Analysis
  • Customer Feedback and Call Detail Record Analysis

Big Data use-cases in ECommerce & Hi-Tech Digital Industry

  • Association and Complementary Products – Big Data can be used as input to recommendation engines within websites like Amazon or Buy.com that can increase average order size by recommending complementary products on real time basis based on predictive analysis for cross-selling.
  • Cross-channel analytics — sales attribution, average order value, lifetime value (e.g., how many in-store purchases resulted from a particular recommendation, advertisement or promotion).
  • Event analytics — what series of steps (golden path) led to a desired outcome (e.g., purchase, registration).
  • Right Offer at the Right Time
  • Next Best Offer – deploying predictive models in combination with recommendation engines that drive automated next best offers and tailored interactions across multiple interaction channels.
  • Large-scale click-stream analytics
  • Ad targeting, analysis, forecasting and optimization
  • Abuse and click-fraud prevention
  • Social graph analysis and profile segmentation
  • Campaign management and loyalty programs

Big Data use-cases in Travel Industry

Just think about every data point produced in one single business or pleasure trip, from the chosen time, airline choice, hotel destination, meals, entertainment decisions, and how that data can then be used to provide a deeper and richer consumer experience in a quicker and easier sale of services. As one industry publication points out it is the ability to personalize that will spur Big Data analytics and foster development of new applications and new services in the travel realm.

  • Personalized Pricing for Travel – Aviation, Hotels, Vacation Packages and Cars….
  • Customer Sentiment and Behavior Analysis
  • Customer Loyalty Management
  • Call Detail Record Analysis for Customer Experience
  • Traffic Pattern and Congestion Management
  • GPS Coordinated data processing for Geo-Fencing
  • Social Media – Advertising and Campaign Management
  • Social Media – Consumer Feedback and Interaction analysis

These are just few of the possibilities of big data use cases in these industries. Though the possibilities are endless, key to success is to have a strong framework for big data implementation and having the first step right in the direction.

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