This disclosure relates generally to categorical time-series clustering. In an embodiment, the method for categorical time-series clustering for categorical time-series associated with distinct subjects obtained from sensors. Based on the categorical time-series, the subjects are clustered into clusters by using a Markov chain model. Clustering the subjects include assigning each subject to a cluster. The subjects are assigned to the clusters by determining cluster-specific transition matrices based on a transitional probability of the subject's transitioning between states. A semi-distance function is constructed for each cluster-specific transitional matrix between the states at multiple time instances, which us indicative of a conditional probability of movement of the subject between the states at different time instance. Using an expectation maximization (EM) model, one or more latent variables of each of the cluster-specific transitional matrices are obtained to determine a likelihood of association of the subject to the cluster.

Methods and systems for predictively generating an insurance claim in response to detecting an imminent insurance-related event are provided. According to certain aspects, a smart home controller or insurance provider remote processor may store a first set of data received from smart devices disposed on, or proximate to, a property. This data may be analyzed to detect that an insurance-related event is imminent and calculate a likelihood that a property owner will file an insurance claim in response to damage caused by the imminent insurance-related event. If there is a sufficient likelihood that an insurance claim will be filed, the smart home controller or remote processor may store a second set of data received from the smart devices. Subsequently, according to certain aspects, the second set of data may be analyzed to prepopulate an automatically generated insurance claim with information detailing damage to the property caused by the insurance-related event.

A system may include a non-transitory computer-readable storage medium storing instructions for execution and one or more processors that execute the instructions. The instructions may cause the one or more processors to configure at least one parameter of a crowdsourcing request related to obtaining information relating to a collateral for a loan, publish the crowdsourcing request related to obtaining the information relating to the collateral for the loan to a group of information suppliers, collect and process a response from an information supplier of the group of information suppliers, where the response includes information on a condition of the collateral for the loan, process the response provided by the information supplier to determine whether an information supply event relating to the response is successful, and respond to a determination of a successful information supply event.

A system may include a non-transitory computer-readable storage medium storing instructions for execution and one or more processors that execute the instructions. The instructions may cause the one or more processors to configure at least one parameter of a crowdsourcing request related to obtaining information relating to a collateral for a loan, publish the crowdsourcing request related to obtaining the information relating to the collateral for the loan to a group of information suppliers, collect and process a response from an information supplier of the group of information suppliers, where the response includes information on a condition of the collateral for the loan, process the response provided by the information supplier to determine whether an information supply event relating to the response is successful, and respond to a determination of a successful information supply event.

A system and method for automated blockchain custody service for managing a set of custodial assets includes a blockchain service circuit structured to interpret a plurality of access control features corresponding to parties associated with a secured loan; a data collection circuit structured to receive first collateral data from at least one sensor associated with the collateral used to secure the loan; receive second collateral data regarding an environment of the collateral from an IoT circuit; and associate the first collateral data and second collateral data with a unique identifier associated with the item of collateral, wherein the blockchain service circuit is structured to store the unique identifier and associated collateral data as blockchain data; a smart contract circuit structured to create a smart lending contract; and a secure access control circuit structured to receive access control instructions from a lender of the loan via an access control interface.

Systems and methods for automatic consideration of jurisdiction in loan related actions are disclosed. An example system may include a data collection circuit to determine location information corresponding to each entity involved in a loan; a jurisdiction definition circuit to determine a jurisdiction for at least one of the entities in response to the location information; and a smart contract circuit to automatically undertake a loan-related action for the loan based at least in part on the jurisdiction for at least one of the entities.

Systems and methods for automatic loan classification are disclosed. An example system may include a data collection circuit to collect a training set of interactions from at least one entity related to at least one loan transaction; an automated loan classification circuit trained on the training set of interactions to classify at least one loan negotiation action; and a robotic process automation circuit trained on a training set of a plurality of loan negotiation actions classified by the automated loan classification circuit and a plurality of loan transaction outcomes to negotiate terms and conditions of a new loan on behalf of a party to the new loan.

Systems and methods for automatic loan classification are disclosed. An example system may include a data collection circuit to collect a training set of interactions from at least one entity related to at least one loan transaction; an automated loan classification circuit trained on the training set of interactions to classify at least one loan negotiation action; and a robotic process automation circuit trained on a training set of a plurality of loan negotiation actions classified by the automated loan classification circuit and a plurality of loan transaction outcomes to negotiate terms and conditions of a new loan on behalf of a party to the new loan.

Systems and methods for crowdsourcing a condition of collateral are disclosed herein. An example system may include a set of crowdsourcing services by which a crowdsourcing request is communicated to a group of information suppliers and by which responses to the crowdsourcing request are collected and processed to provide a reward to at least one successful information supplier. The example system may further include an interface to the set of crowdsourcing services that enables configuration of parameters of the crowdsourcing request, wherein the crowdsourcing request and the parameters are configured to obtain information related to a condition of a set of collateral for the loan. The example system may further include a set of publishing services that publish the crowdsourcing request.

Systems and methods for crowdsourcing a condition of collateral are disclosed herein. An example system may include a set of crowdsourcing services by which a crowdsourcing request is communicated to a group of information suppliers and by which responses to the crowdsourcing request are collected and processed to provide a reward to at least one successful information supplier. The example system may further include an interface to the set of crowdsourcing services that enables configuration of parameters of the crowdsourcing request, wherein the crowdsourcing request and the parameters are configured to obtain information related to a condition of a set of collateral for the loan. The example system may further include a set of publishing services that publish the crowdsourcing request.