Systems and methods for shared utility accessibility
AssigneesSolstice Initiative, Inc., Massachusetts Institute of Technology
InventorsStephanie Koo Speirs, Sandhya Murali, Sruthi Sujani Davuluri, Chikara Onda, Christopher R. Knittel
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Abstract
The present invention provides predictive models for assessing an applicant's risk of defaulting on shared utility service bill payment, such as bill payment for community solar. Described are several alternatives to using FICO score to assess risk. Such alternatives include machine learning techniques (such as random forest classifier) as well as regression analysis.
Claims51
A method of providing, to a utility service entity, an applicant's probability of delinquency for utility bill payment, the method comprising:
- (a) training a predictive model to provide probabilities of delinquency on utility bill payment, wherein the training is performed using a dataset comprising data points representing information associated with a number of individual utility service account holders;
- (b) collecting demographic data and financial data about an applicant;
- (c) applying the demographic data and financial data to the predictive model to obtain a probability of delinquency on utility bill payment for the applicant, and
- (d) providing the probability of delinquency on utility bill payment for the individual applicant to the utility service entity.
A method of providing, to a utility service entity, a determination of qualification for an applicant's enrollment in a utility service, the method comprising:
- (a) training a predictive model to provide a probability of delinquency on utility bill payment, wherein the training is performed using a dataset comprising data points representing information associated with a number of individual utility service account holders;
- (b) collecting demographic data and financial data about an applicant;
- (c) applying the demographic data and financial data to the predictive model to obtain, for the applicant, a probability of delinquency on utility bill payment;
- (d) assigning to the applicant a determination of qualification for enrollment in the utility service that is based on the probability of delinquency on utility bill payment for the applicant;
- and
- (e) providing the determination of qualification for enrollment in the utility service to the utility service entity.
A method of providing, to a utility service entity, probabilities of utility payment delinquency for multiple applicants, the method comprising:
- (a) training a predictive model to provide probabilities of delinquency on utility bill payment, wherein the training is performed using a dataset comprising data points representing information associated with a number of individual utility service account holders;
- (b) collecting demographic data and financial data for each of said multiple applicants;
- (c) applying the demographic data and financial data to the predictive model to obtain, for each of said multiple applicants, a corresponding probability of delinquency on utility bill payment, and
- (d) providing said corresponding probability of delinquency for each of said multiple applicants to the utility service entity.
A method of providing, to a utility service entity, determinations of qualification for enrollment in a utility service as to multiple applicants, the method comprising:
- (a) training a predictive model to provide probability delinquency on utility bill payment, wherein the training is performed using a dataset comprising data points representing information associated with a number of individual utility service account holders;
- (b) collecting demographic data and financial data for each of said multiple applicants;
- (c) applying the demographic data and financial data to the predictive model to obtain, for each of said multiple applicants, a corresponding probability of delinquency on utility bill payment;
- (d) assigning to each of said multiple applicants a determination of qualification for enrollment in the utility service that is based on said corresponding probability of delinquency on utility bill payment for the applicant;
- and
- (e) providing said determinations to the utility service entity.
A method of enrolling one or more applicants in a utility service, the method comprising enrolling in the utility service at least one applicant based on the applicant's probability of delinquency on utility bill payment, wherein the applicant's probability of delinquency on utility bill payment is determined by applying demographic data and financial data for the applicant to a predictive model that has been trained, tested, and validated using a dataset comprising data points representing past information associated with a number of individual utility service account holders.
A method of facilitating enrollment of one or more applicants in a community shared utility service using a predictive model, the method comprising:
- (a) receiving demographic data and financial data for each of one or more applicants;
- (b) applying a predictive model to the demographic data and financial data to obtain a probability of delinquency on a utility bill payment for each of the one or more applicants, wherein the predictive model is a machine learning model that has been trained, tested, and validated using a dataset comprising data points representing past information associated with a number of individual utility service account holders;
- (c) assigning, to each of the one or more applicants, a determination of qualification for enrollment in the community shared utility service based on the probability of delinquency obtained for each of the one or more applicants;
- (d) providing said determinations to a utility service provider in order to facilitate enrollment in the community shared utility service.
A method of obtaining a probability of delinquency for an applicant's payment in a community shared utility, the method comprising:
- (a) training a random forest predictive model to provide a probability of delinquency on utility bill payment, wherein the training is performed using a dataset comprising data points representing information associated with a number of individual utility service account holders;
- (b) collecting an applicant's demographic data and financial data;
- (c) applying the demographic data and financial data to the random forest predictive model to obtain a probability of delinquency for the applicant's payment in a community shared utility.
A non-transitory computer-readable medium having stored thereon computer-readable instructions that when executed by a computing device cause the computing device to:
- (a) access an individual's demographic data and financial data from memory;
- (b) process the individual's demographic data and financial data through a random forest classifier to compute a probability of being delinquent on a utility bill payment.
A method, comprising:
- (a) storing, in a database, demographic data and financial data for each member of a group of individual utility account holders;
- (b) evaluating, by a computing apparatus, a plurality of pre-defined features from each member of the group of individual utility account holders based on the demographic data and the financial data stored in the database;
- (c) generating, by the computing apparatus, an aggregated dataset of the pre-defined features;
- (d) separating, by the computing apparatus, the aggregated dataset into a training dataset and a test dataset;
- (c) applying, by the computing apparatus, a machine learning technique to the training dataset to derive a predictive model that correlates the features for each of said individual utility account holders with a probability of delinquency on a utility bill payment;
- (d) applying, by the computing apparatus, the predictive model to the testing dataset to yield a determination of whether the accuracy of predictions from the predictive model is above a pre-defined threshold;
- and
- (e) following a determination that the accuracy of predictions from the predictive model for the testing dataset is above the pre-defined threshold, applying, by the computing apparatus, the predictive model to a utility service applicant's demographic data and financial data to generate a prediction as to whether the applicant will be delinquent on a utility bill payment.
A non-transitory computer-readable medium having stored thereon instructions configured to instruct a computing device to perform a method, the method comprising:
- (a) storing, in a database, demographic data and financial data for each member of a group of individual utility account holders;
- (b) evaluating, by a computing apparatus, a plurality of pre-defined features for each member of the group of individual utility account holders based on the demographic data and the financial data stored in the database;
- (c) generating, by the computing apparatus, an aggregated dataset of the features for the group of individual utility account holders;
- (d) separating, by the computing apparatus, the aggregated dataset into a training dataset and a test dataset;
- (c) applying, by the computing apparatus, a machine learning technique to the training dataset to derive a predictive model that correlates the features for each member of the group of individual utility account holders with a probability of delinquency on a utility bill payment;
- (d) applying, by the computing apparatus, the predictive model to the testing dataset to yield a determination of whether the accuracy of predictions from the predictive model is above a pre-defined threshold;
- and
- (e) in response to a determination that the accuracy of predictions from the predictive model for the testing dataset is above the pre-defined threshold, applying, by the computing apparatus, the predictive model to an applicant's demographic data and financial data to generate a prediction as to whether the applicant will be delinquent on a utility bill payment.
A method of using a predictive model for the enrollment of one or more applicants in a community shared utility service, the method comprising:
- (a) collecting demographic data and financial data for each of one or more applicants;
- (b) applying said demographic data and financial data to a predictive model to obtain a probability of delinquency on a utility bill payment for each of the one or more individuals, wherein the predictive model has been trained, tested, and validated using a dataset comprising data points representing past information associated with a number of individual utility service account holders;
- (c) assigning a determination of qualification for said community shared utility service to each of the one or more applicants based on said probability of delinquency, and
- (d) providing said determination of qualification for said community shared utility service to a utility company.
A method of obtaining a probability of delinquency for an applicant's payment in a community shared utility, said method comprising:
- (a) collecting the applicant's demographic data and financial data;
- (b) applying a random forest analysis to the demographic and financial data to obtain a probability of delinquency for the applicant's payment in a community shared utility.
Dependencies are inferred from the claim text.