The following documents are incorporated herein by reference as if fully set forth: U.S. application Ser. No. 14/145,142, titled SYSTEM AND METHOD FOR DETERMINING DRIVER SIGNATURES filed Dec. 31, 2013; U.S. application Ser. No. 14/145,181, titled SYSTEM AND METHOD FOR DESTINATION BASED UNDERWRITING filed Dec. 31, 2013; and U.S. application Ser. No. 14/145,205, titled SYSTEM AND METHOD FOR TELEMATICS BASED UNDERWRITING filed Dec. 31, 2013. Each of the applications shares common inventorship with the present application and are being filed concurrently.
Vehicle insurance is typically comprised of five types of coverage, including liability, collision, personal injury protection, comprehensive and uninsured motorist protection. Each of the coverages may have an agreed upon policy limit or liability to the insurance company. Insurance companies gather use correlative data, or proxies, to determine the risk associated with providing coverage. There are certain driving behaviors that have been statistically linked to higher risk of losses. For example, speeding, excessive braking, or driving while sleepy or intoxicated may be factors that lead to an increased chance of an accident. While in some cases prior incidents are available to an insurance company to make a determination, in many cases, this information is either not available or limited for a new customer. Accordingly, insurance companies use certain driver biographical information as a proxy for these behaviors. Based on historical data, the biographical information might include factors such as age, gender, marital status etc. that are correlated with the loss experience. Examples of general proxies may include the following:
Based on the above, as well as many other factors, insurance companies calculate a risk factor, which essentially translates to the likelihood and severity of a claim. This risk factor is then used to help determine either to provide or deny coverage, and assist in determining the pricing for any coverage that is provided.
However, these proxies are based on the fact that they are correlated to the actual driving behavior and may or may not be a causal factor. For example, persons with a bad credit score may pay a higher premium.
Accordingly, a system and method is proposed to introduce an improved expectation based rating, using telematics data to more accurately assess driver risk.
This application relates to an insurance rating methodology that assesses a driver's risk based on telematics data collected on actual driving behavior relative to the expected driving behavior. Drivers are assessed for eligibility for discounts based on the ratio between an expected or known or historical rating versus their actual demonstrated rating. For example, if a driver with a poor driving history who is currently rated as a high risk driver, exhibits average driving behaviors, this driver may actually be eligible for a discount as the demonstrated driving behaviors were on par or not as bad as their expected driving behavior. Conversely, if a driver with a prior excellent driving history exhibits average driving behaviors, then this driver may receive no discount or may even get a surcharge as the driver is exhibiting driving that is worse than expected.
Essentially, this allows an insurer to swap out proxies that are correlated with driving behavior with actual measured driving behavior. The methods described herein allow the insurance company to perform real-time pricing based on the determined telematics data collection period. The methods described herein may allow the insurance company to provide a quote to add an additional car, to replace a car, or to remove a previously covered driver.
A system for determining insurance pricing information based on telematics data, the system comprising, a processor configured to generate a driver proxy score (DPS) based on a combination of rating variables in a conventional class plan; the processor, configured to receive information associated with telematics data received from a telematics device, wherein the telematics data includes a plurality of risk factors; the processor further configured to determine a driver telematics score (DTS) based on the information associated with the telematics data; the processor further configured to generate an expectation based rating, based on the DPS and the DTS, which measures a variance of the telematics data from the expected value; the processor further configured to update pricing information based on the determined expectation based rating; and a transmitter configured to transmit the updated pricing information to a user device.
A more detailed understanding may be had from the following description, given by way of example in conjunction with the accompanying drawings wherein:
Disclosed herein are processor-executable methods, computing systems, and related technologies for determining expectation based processing and to determine risk and pricing information based on those ratings. The system may be configured to supplement or replace correlative values (i.e. proxies) with causation variables which may be directly based on telematics in combination with loss data. The resultant pricing information may be presented to the user.
During a registration phase for auto insurance, an insurance company or an insurance agent may request biographical data. This biographical data is entered in a database and compared with actuarial data. The system collects, from the user during the registration phase biographical data, such as: Name, Age, Gender, Occupation, Vehicle, Driving History, Geographical Location, Grades (if the driver is a student), and frequency of use of the vehicle. Using software based algorithms, using actuarial data, the biographical information is analyzed to generate the initial risk profile. Table 1, below, shows an example initial risk profile for a driver. In the example shown in Table 1, the initial risk profile is compared to a computationally predetermined hypothetical driver. The hypothetical driver, for example, may be a statistically average risk driver for which no penalties or credits would be awarded to, during the registration process.
As shown above, the risk profile contains specific risk behaviors that have been determined to affect the frequency and severity of potential accidents. Based on the received biographical information, the system 100 calculated risk factors for each of these behaviors. The variances from the hypothetical may be based on absolute or relative factors. For example speeding may be determined based on absolute speed, relative to posted speed limits, or speed relative to other nearby drivers. Similarly the variance for the other factors may be analyzed on an absolute or relative basis.
To replace or enhance one or more proxies for driving behavior, a vehicle 140 is equipped with a TrueLane® device that is configured to receive telematics data from one or more telematics devices regarding the actual driving behavior. In one embodiment, the vehicle 140 or the system 100 is configured to determine the identity of the driver.
The system receives the telematics data from the TrueLane® device and compares the measured value with the initial driver expectations. The system may then generate an updated risk profile, based on measured values. An updated risk profile, incorporating telematics data is shown in Table 2.
As a simple, single variable example. For the example above shown in Table 2, the 16 year old driver is expected to drive 2000 miles a year. However, the measured mileage is much less (432). The system 100 may be configured to determine, based on one or more variables, whether to adjust the rate or credit or penalize the driver in the pricing. In the simple case above, if total miles driven were the only important metric, the driver may be in a position to receive a significant discount as soon as the system 100 determines that he will not be driving the expected 2000 miles. In a more complex case, a multivariate analysis may be used incorporating multiple factors, wherein the initial premium may assume this driver would be worse based on the initial driving profile. But measured results allow the insurance company to replace these proxies with actual measured data.
The one or more telematics devices associated with the vehicle 140 may communicate with a satellite, Wi-Fi hotspot and even other vehicles. The telematics devices associated with the vehicle 140 report this information to the DCU 110. As will be described in greater detail hereafter, the DCU 110 may transmit this telematics data to the DPU 170 which may be configured to consolidate biographic and telematics data to generate driver expectation information.
The web site system 120 provides a web site that may be accessed by a user device 130. The web site system 120 includes a Hypertext Transfer Protocol (HTTP) server module 124 and a database 122. The HTTP server module 124 may implement the HTTP protocol, and may communicate Hypertext Markup Language (HTML) pages and related data from the web site to/from the user device 130 using HTTP. The web site system 120 may be connected to one or more private or public networks (such as the Internet), via which the web site system 120 communicates with devices such as the user device 130. The web site system 120 may generate one or more web pages that provide communication setting information, may communicate the web pages to the user device 130, and may receive responsive information from the user device 130.
The HTTP server module 124 in the web site system 120 may be, for example, an APACHE HTTP server, a SUN-ONE Web Server, a MICROSOFT Internet Information Services (IIS) server, and/or may be based on any other appropriate HTTP server technology. The web site system 120 may also include one or more additional components or modules (not depicted), such as one or more load balancers, firewall devices, routers, switches, and devices that handle power backup and data redundancy.
The user device 130 may be, for example, a cellular phone, a desktop computer, a laptop computer, a tablet computer, or any other appropriate computing device. The user device 130 includes a web browser module 132, which may communicate data related to the web site to/from the HTTP server module 124 in the web site system 120. The web browser module 132 may include and/or communicate with one or more sub-modules that perform functionality such as rendering HTML (including but not limited to HTML5), rendering raster and/or vector graphics, executing JavaScript, and/or rendering multimedia content. Alternatively or additionally, the web browser module 132 may implement Rich Internet Application (RIA) and/or multimedia technologies such as ADOBE FLASH, MICROSOFT SILVERLIGHT, and/or other technologies. The web browser module 132 may implement RIA and/or multimedia technologies using one or web browser plug-in modules (such as, for example, an ADOBE FLASH or MICROSOFT SILVERLIGHT plug-in), and/or using one or more sub-modules within the web browser module 132 itself. The web browser module 132 may display data on one or more display devices (not depicted) that are included in or connected to the user device 130, such as a liquid crystal display (LCD) display or monitor. The user device 130 may receive input from the user of the user device 130 from input devices (not depicted) that are included in or connected to the user device 130, such as a keyboard, a mouse, or a touch screen, and provide data that indicates the input to the web browser module 132.
The example system architecture 100 of
Each or any combination of the modules shown in
The web browser window 200 may include a control area 265 that includes a back button 260, forward button 262, address field 264, home button 266, and refresh button 268. The control area 265 may also include one or more additional control elements (not depicted). The user of the user device 130 may select the control elements 260, 262, 264, 266, 268 in the control area 265. The selection may be performed, for example, by the user clicking a mouse or providing input via keyboard, touch screen, and/or other type of input device. When one of the control elements 260, 262, 264, 266, 268 is selected, the web browser module 132 may perform an action that corresponds to the selected element. For example, when the refresh button 268 is selected, the web browser module 132 may refresh the page currently viewed in the web browser window 200.
While the below examples describe a scenario of a new customer registering for insurance and then having the pricing adjusted based on telematics data, the systems and methods described herein may be applied to current and former customers who are looking to renew their coverage. In this scenario, the biographical information may already be stored on the insurance server 180, and the DPU 170 may access this information directly.
In addition to the example information above, additional information may be determined during or after the registration phase. For example, Table 3 shows biographical information that may be used in generating driver risk profiles.
The system 100 may be configured to weight each factor based on actuarial date. For example, in the example above, there are two categories, primary and secondary wherein each factor within a particular category may be weighted equally. However, each factor may be assigned a unique weight.
The registration phase is used to generate an initial risk profile, as shown in Table 1, above. During the registration phase, the system 100 received biographical information about each of the drivers that may be associated with the user's account as well information about the vehicles for which coverage is requested. With millions of accidents each year, a large amount of data is available on factors that may affect the likelihood of an accident as well as the severity of the accident. The database 176 associated with the DPU 170 contains information regarding accident information. The DPU 170, using a multivariate analysis, generates the initial driver profile based on the provided biographic information verses the factors stored in the database 176.
The inside of the vehicle 140 may include a plurality of electronic devices that may communicate information to the telematics device. Many vehicles include at least one microprocessor and memory that connects to each individual electronic device. For example, there may be electronic devices associated with the seats, A/C units, global positioning satellite (GPS)/stereo system, DVD unit, and BLUETOOTH equipment. The microprocessor may also be in communication with the headlights, engine, traffic signals, rear view mirror, rearview cameras, cruise control, braking system and inner workings of a vehicle 140. There may also be additional devices such as multiple mobile phones brought by passengers into the vehicle 140. The telematics device is configured to receive information from the electronics in the vehicle 140. For example, the telematics device is configured to receive data concerning, speed, acceleration, braking, location, seat settings, lane changes, radio volume, window controls, vehicle servicing, number of cellular devices in the vehicle 140, proximity to other vehicle's, etc. The telematics device may be configured to transmit this information directly to the DCU 110. The DCU 110 may be configured to format the received information and transmit it to the DPU 170.
The DPU 170 may be configured to use this information to determine the driving behavior associated with an individual driver, or the vehicle. In one embodiment, the DPU 170 may be configured to identify the driver using a driver signature, for example, as disclosed in co-pending U.S. application Ser. No. 14/145,142, titled SYSTEM AND METHOD FOR DETERMINING DRIVER SIGNATURES filed Dec. 31, 2013.
The telematics device may be configured to include an event/status monitor of the vehicle's 140 activities. An example of the event/status log, which may be stored in a database operatively coupled to the telematics device, is shown in Table 4.
The telematics device may be configured to take periodic measurements regarding the vehicle, as well as event triggered measurements. For example, the telematics device may be configured to take readings every 5 minutes. However, events such as a detected turn, brake, and phone activation may trigger additional readings. The example above is not exhaustive; the metrics are shown as example only.
The telematics device may transmit the recorded information to the DCU 110. The DCU 110 formats this information and transmits it to the DPU 170, which may then compare this data with the expected driving behaviors.
The RPU 160 may access the database 176 associated with the DPU 170 to determine adjusted pricing information based on the variance from the expected driving behavior.
The system 100 uses the biographical information provided in web pages 302-1302 as a baseline for generating the initial premium. However, the telematics data, provided by the telematics device may be used to refine this information. The RPU 160 may access the information stored in the DPU 170, and use a software based algorithm to determine a discount or penalty.
In a first example, the system 100 may offer the user a predetermined discount to sign up for the telematics device. The system 100 may be configured to generate a discount factor, for example according to the following equation:
Discount relativity=starting discount*β1ρ1*β2ρ2*β3ρ3* . . . βnρn,
For example, the starting discount may be 10%, and if the product of the risk and weighting factors >1, the system 100 may determine the driver is not eligible for a discount.
In one scenario, the system 100 may only receive telematics data for a fixed time period. In this scenario, the RPU 160 may be configured to compensate for the limited duration of the telematics data using a seasonality factor. For example, if the telematics data is received from September-December, and the biographical information indicates one of the insured drivers attends college away from home, RPU 160 may be configured to use the seasonality factor to adjust the pricing information to account for the lack of information transmitted regarding that driver. Conversely, under the same scenario, if the readings were taken during the summer, when the student was home, the telematics data may be skewed the other way. Accordingly, the RPU 160 may use the seasonality factor to account for that.
Additionally, the system 100 may identify unknown or undisclosed drivers based on the received telematics data.
In another example of expectation based rating, a driver proxy score (DPS) may be derived from a combination of rating variables in a conventional class plan. Table 5, below, shows an example of a driver proxy score.
The DPU 170 may receive the telematics data and generate a driver telematics score (DTS). Table 6, below shows an example of a driver telematics score.
The DPU 170 may standardize the risk scores in Tables 5 and 6 using multivariate statistical techniques, to make them comparable on the same risk scale. An expectation based rating (EBR— may be calculated as follows:
Expectation Based Rating (EBR) for Driver 1=actual/expected=12/25=0.48
Expectation Based Rating (EBR) for Driver 2=actual/expected=12/10=1.2
As shown, by the scores above, two drivers with the same DTS may receive different EBRs based on their expected behavior from a conventional class plan. Driver 1 may receive a discount as the actual driving behavior is better than expected whereas Driver 2 may receive a surcharge as the actual driving behavior is worse than expectation.
The memory device 1520 may be or include a device such as a Dynamic Random Access Memory (D-RAM), Static RAM (S-RAM), or other RAM or a flash memory. The storage device 1516 may be or include a hard disk, a magneto-optical medium, an optical medium such as a CD-ROM, a digital versatile disk (DVD), or BLU-RAY disc (BD), or other type of device for electronic data storage.
The communication interface 1522 may be, for example, a communications port, a wired transceiver, a wireless transceiver, and/or a network card. The communication interface 1522 may be capable of communicating using technologies such as Ethernet, fiber optics, microwave, xDSL (Digital Subscriber Line), Wireless Local Area Network (WLAN) technology, wireless cellular technology, BLUETOOTH technology and/or any other appropriate technology.
The peripheral device interface 1512 may be an interface configured to communicate with one or more peripheral devices. As an example, the peripheral device may communicate with an onboard diagnostics (OBD) unit that is associated with a vehicle. The peripheral device interface 1512 may operate using a technology such as Universal Serial Bus (USB), PS/2, BLUETOOTH, infrared, serial port, parallel port, and/or other appropriate technology. The peripheral device interface 1512 may, for example, receive input data from an input device such as a keyboard, a mouse, a trackball, a touch screen, a touch pad, a stylus pad, and/or other device. Alternatively or additionally, the peripheral device interface 1512 may communicate output data to a printer that is attached to the computing device 1510 via the peripheral device interface 1512.
The display device interface 1514 may be an interface configured to communicate data to display device 1524. The display device 1524 may be, for example, an in-dash display, a monitor or television display, a plasma display, a liquid crystal display (LCD), and/or a display based on a technology such as front or rear projection, light emitting diodes (LEDs), organic light-emitting diodes (OLEDs), or Digital Light Processing (DLP). The display device interface 1514 may operate using technology such as Video Graphics Array (VGA), Super VGA (S-VGA), Digital Visual Interface (DVI), High-Definition Multimedia Interface (HDMI), or other appropriate technology. The display device interface 1514 may communicate display data from the processor 1518 to the display device 1524 for display by the display device 1524. As shown in
An instance of the computing device 1510 of
Although
The system 100 may provide this information at a predetermined renewal period or based on a triggering event. A triggering event may occur, for example based on the variance of the telematics data to an expected value or any event or observed data that may adjust expected losses.
The system 100 may further include a user transmission device (not pictured) wherein the user transmission device may communicate insurance information, including pricing information, contractual information, information related to the telematics program, and other notifications. A user transmission device may include one or more modes of communication to reach a potential customer, current customer, or past customer or other similar user. For example, the user transmission device may be coupled with a printing device that is automatically mailed to the user. In another embodiment, the user transmission device may be coupled to a device to generate automatic telephone calls, or “robo-calls,” or other similar communication mediums to communicate with the user. The user transmission device may further be configured to send e-mails to a user. The user device may further be configured to communicate via social media. This information may include, in addition to pricing information, an itemized list indicating a variance from the expected value for each risk factor, suggested driving behaviors etc.
The multivariate predictive model(s) used herein may include one or more of neural networks, Bayesian networks (such as Hidden Markov models), expert systems, decision trees, collections of decision trees, support vector machines, or other systems known in the art for addressing problems with large numbers of variables. In embodiments, the predictive models are trained on prior data and outcomes using an historical database of insurance related data and resulting correlations relating to a same user, different users, or a combination of a same and different users. In embodiments of the present invention, the predictive model may be implemented as part of the DPU 170 or RPU 160 described with respect to
As used herein, the term “processor” broadly refers to and is not limited to a single- or multi-core processor, a special purpose processor, a conventional processor, a Graphics Processing Unit (GPU), a digital signal processor (DSP), a plurality of microprocessors, one or more microprocessors in association with a DSP core, a controller, a microcontroller, one or more Application Specific Integrated Circuits (ASICs), one or more Field Programmable Gate Array (FPGA) circuits, any other type of integrated circuit (IC), a system-on-a-chip (SOC), and/or a state machine.
As used herein, the term “computer-readable medium” broadly refers to and is not limited to a register, a cache memory, a ROM, a semiconductor memory device (such as a D-RAM, S-RAM, or other RAM), a magnetic medium such as a flash memory, a hard disk, a magneto-optical medium, an optical medium such as a CD-ROM, a DVD, or BLU-RAY Disc, or other type of device for electronic data storage.
Although the methods and features described above with reference to