Implementing Machine Learning For Life And Health Insurance Loss Mitigation And Claims Handling
Techniques for implementing machine learning for insurance loss mitigation or prevention, and claims handling are disclosed. In some scenarios, the insurance loss mitigation and claims handling may be associated with a disability, worker's compensation, life or health insurance policy, and the machine-learning analytics model may be trained in accordance with data that is relevant to identifying appropriate predictions in accordance with these particular types of insurance products. For instance, the machine-learning analytics model may utilize information within a dynamic data set as training data, which may include electronically accessible information. The machine-learning analytics model may additionally be implemented to identify various predictions that are indicative of a risk of insuring an individual as well as one or more actions that, when performed, may reduce the initial calculation of risk.
1 . A computer-implemented method, comprising:
accessing, via one or more processors, a dynamic data set associated with one or more users including electronic medical records, demographic information, insurance records, and lifestyle information;
training, via one or more processors, a machine-learning analytics model using the dynamic data set as training data to generate a trained machine-learning analytics model;
receiving, via the one or more processors, user data associated with a user;
applying, via the one or more processors, the trained machine-learning analytics model to the user data to predict a set of one or more medical-related conditions associated with the user;
determining, via the one or more processors in accordance with the trained machine-learning analytics model, a first level of risk associated with insuring the user based upon the one or more predicted medical-related conditions;
identifying, via the one or more processors in accordance with the trained machine-learning analytics model, one or more intervening actions that, when executed by the user within a future time period, reduce the first level of risk associated with insuring the user to a second level of risk;
transmitting, via the one or more processors, the one or more intervening actions to a computing device to be presented to the user;
monitoring user activity associated with the one or more identified intervening actions to collect user activity monitoring data;
re-training, via the one or more processors, the trained machine-learning analytics model using the user activity monitoring data; and
applying, via the one or more processors, the trained machine-learning analytics model to the user activity monitoring data to determine a likelihood of whether the user will continue to execute the one or more intervening actions during the future time period.
2 . The computer-implemented method of claim 1 , wherein the first and second levels of risk associated with insuring the user represent insuring the user for a health insurance, worker's compensation, disability or life insurance policy.
3 . The computer-implemented method of claim 1 , further comprising:
calculating, via the one or more processors, a first insurance premium associated with insuring the user in accordance with the first level of risk;
calculating, via the one or more processors, a second insurance premium associated with insuring the user in accordance with the second level of risk; and
transmitting, via the one of more processors, the first and the second insurance premium to the computing device for presentation to the user.
4 . The computer-implemented method of claim 1 , further comprising:
calculating, via the one or more processors, a health or life insurance premium associated with insuring the user in accordance with the second level of risk; and
upon insuring the user for the health or the life insurance policy in accordance with the calculated health or life insurance premium, accessing, via one or more processors, the dynamic data set to collect user activity monitoring data.
5 . The computer-implemented method of claim 1 , wherein the act of training the machine-learning analytics model includes training a neural net.
6 . The computer-implemented method of claim 1 , wherein the one or more intervening actions include suggestions regarding (i) a type and frequency of exercise, (ii) daily nutrition, and (iii) lifestyle habits.
7 . The computer-implemented method of claim 1 , wherein the future time period corresponds to a period of insurance coverage for a health, worker's compensation, disability or life insurance policy.
8 . A computing device, comprising:
a communication unit configured to access a dynamic data set associated with one or more users including electronic medical records, demographic information, insurance records, and lifestyle information, and to receive user data associated with a user; and
a processing unit configured to:
train a machine-learning analytics model using the dynamic data set as training data to generate a trained machine-learning analytics model;
apply the trained machine-learning analytics model to the user data to predict a set of one or more medical-related conditions associated with the user;
determine a first level of risk associated with insuring the user based upon the one or more predicted medical-related conditions in accordance with the trained machine-learning analytics model;
identify one or more intervening actions in accordance with the trained machine-learning analytics model that, when executed by the user within a future time period, reduce the first level of risk associated with insuring the user to a second level of risk;
transmit, via the communication unit, the one or more intervening actions to a computing device to be presented to the user;
monitor user activity associated with the one or more identified intervening actions to collect user activity monitoring data;
re-train the trained machine-learning analytics model using the user activity monitoring data; and
apply the trained machine-learning analytics model to the user activity monitoring data to determine a likelihood of whether the user will continue to execute the one or more intervening actions during the future time period.
9 . The computing device of claim 8 , wherein the first and second levels of risk associated with insuring the user represent insuring the user for a health, worker's compensation, disability or life insurance policy.
10 . The computing device of claim 8 , wherein the processing unit is further configured to:
calculate a first insurance premium associated with insuring the user in accordance with the first level of risk;
calculate a second insurance premium associated with insuring the user in accordance with the second level of risk; and
transmit the first and the second insurance premium to the computing device for presentation to the user.
11 . The computing device of claim 8 , wherein the processing unit is further configured to:
calculate a health or life insurance premium associated with insuring the user in accordance with the second level of risk; and
upon insuring the user for the health or the life insurance policy in accordance with the calculated health or life insurance premium, access the dynamic data set to collect user activity monitoring data.
12 . The computing device of claim 8 , wherein the processing unit is further configured to train the machine-learning analytics model by training a neural net.
13 . The computing device of claim 8 , wherein the one or more intervening actions include suggestions regarding (i) a type and frequency of exercise, (ii) daily nutrition, and (iii) lifestyle habits.
14 . The computing device of claim 8 , wherein the future time period corresponds to a period of insurance coverage for a health, worker's compensation, disability or life insurance policy.
15 . A non-transitory computer readable media having instructions stored thereon that, when executed by one or more processors, cause the one or more processors to:
access a dynamic data set associated with one or more users including electronic medical records, demographic information, insurance records, and lifestyle information;
train a machine-learning analytics model using the dynamic data set as training data to generate a trained machine-learning analytics model;
receive user data associated with a user;
apply the trained machine-learning analytics model to the user data to predict a set of one or more medical-related conditions associated with the user;
determine, in accordance with the trained machine-learning analytics model, a first level of risk associated with insuring the user based upon the one or more predicted medical-related conditions;
identify, in accordance with the trained machine-learning analytics model, one or more intervening actions that, when executed by the user within a future time period, reduce the first level of risk associated with insuring the user to a second level of risk;
transmit the one or more intervening actions to a computing device to be presented to the user;
monitor user activity associated with the one or more identified intervening actions to collect user activity monitoring data;
re-train the trained machine-learning analytics model using the user activity monitoring data; and
apply the trained machine-learning analytics model to the user activity monitoring data to determine a likelihood of whether the user will continue to execute the one or more intervening actions during the future time period.
16 . The non-transitory computer readable media of claim 15 , wherein the first and second levels of risk associated with insuring the user represent insuring the user for a health insurance or a life insurance policy, and
wherein the future time period corresponds to a period of insurance coverage for the health or the life insurance policy.
17 . The non-transitory computer readable media of claim 15 , further including instructions that, when executed by one or more processors, cause the one or more processors to (i) calculate a first insurance premium associated with insuring the user in accordance with the first level of risk, (ii) calculate a health or life insurance premium associated with insuring the user in accordance with associated with insuring the user in accordance with the second level of risk, and (iii) transmit the first and the second insurance premium to the computing device for presentation to the user.
18 . The non-transitory computer readable media of claim 15 , further including instructions that, when executed by one or more processors, cause the one or more processors to (i) calculate a health or life insurance premium associated with insuring the user in accordance with the second level of risk, and (ii) upon insuring the user for the health or the life insurance policy in accordance with the calculated premium, access the dynamic data set to collect user activity monitoring data.
19 . The non-transitory computer readable media of claim 15 , wherein the instructions to train the machine-learning analytics model further include instructions that, when executed by one or more processors, cause the one or more processors to train the machine-learning analytics model by training a neural net.
20 . The non-transitory computer readable media of claim 15 , wherein the one or more intervening actions include suggestions regarding (i) a type and frequency of exercise, (ii) daily nutrition, and (iii) lifestyle habits.