Abstract: A call management system of a call center identifies an inbound caller based upon computer analysis of customer identifiers, which may include at least two of customer name, street address, and zip code. Approximate string matching analysis matches n-grams generated from strings within customer identifiers, with n-grams generated from customer identification fields while searching one or more databases. Approximate string matching can incorporate a closeness metric based on Jaccard distance, and a Gaussian mixture model of best matches. In one embodiment, a Polymr search engine analyzes customer identifiers of inbound callers to retrieve customer data, such as customer demographic data, matched to the customer identifiers. In another embodiment, the Polymr search engine analyzes customer identifiers of inbound callers to identify repeat callers and retrieve previously collected customer data.

Abstract: Methods and systems for developing profitable convertible term products are disclosed. The methods include the disaggregation of the pricing of existing term products and the use of big data analytics to identify opportunities to improve the acceptance of the product within a particular share of the market. Then, a pricing model and a selling model are built to test the product.

Abstract: The embodiments recite systems and methods that improve the traditional underwriting process within a financial institution. These embodiments produce an underwriting model that emulates the resolution patterns of top performing underwriters. The underwriting model once is built and tested is incorporated into decision tools that provide underwriters with insightful advices when underwriting a client. The embodiments use statistical learning techniques such as support vector machine and logistic regression. These techniques can assume a linear or nonlinear relationship between factors and risk classes. Furthermore, the underwriting model also uses artificial intelligence tools such as expert systems and fuzzy logic. A company's underwriting standards and best underwriting practices may be updated periodically so that underwriting model based on decision heuristic keep improving the quality of its output over time.

Abstract: A routing system of a call center determines a plurality of advisor clusters to be assigned to each of a plurality of lead records stored in a lead model database. The predictive machine learning model inputs lead model data and advisor model data into a clustering analysis. Various modeling data are extracted from source lead data, sales data, and advisor data, in which the advisor data has been flattened for modeling. The predictive machine learning model applies a combination of a clustering analysis, a cluster model, and an aggregate conversion model to lead model data and user model data. The clustering analysis utilizes unsupervised clustering and supervised clustering, and outputs a plurality of advisor clusters and sales conversion scores. The clustering analysis clusters each of the advisors into one of the plurality of advisor clusters based on degree of similarity of a clustering vector.

Abstract: System and method for providing personalized, time-varying layered bundles of computer records to users. The system includes personalized servers, a communications network, user interfaces, and client devices employed by users. The personalized server includes a needs analysis module, a bundle building module, and an bundle generating module. A method of providing personalized bundle of computer records includes receiving a request for a personalized bundle of computer records, and requesting user needs data associated with the client. The method further includes converting the user data into determined needs data, and building a bundle of computer records personalized to the user using the determined needs data, which may include a determined needs timeline. The personalized, time varying bundle of computer records includes a plurality of computer records and plurality of types of bundles of computer records represented in the determined needs data.

Abstract: Disclosed is a method and a system for efficiently and accurately processing air particulate datasets when facing a high number of air particulate datasets from multiple locations to generate an artificial intelligence model having one or more computer-based rules that determines eligibility of a user to avail a health-related service based on air particulate records associated with current and past locations of the user.

Abstract: Systems and methods for assessing the needs of customers using predictive modeling techniques are disclosed. The method receives customer data from a first database and provided by the user. The method generates an instruction to a second database and receives additional customer information received from external databases to generate a basic profile for data based on the customer. The system further analyzes data provided by the user. The method generates a customer profile based on the basic customer data and additional data. The method determines missing data from the customer profile associated and a set of attributes of the user. The method identifies a profile with the customer similar set of attributes and estimates the missing data using predictive modeling techniques to generate estimated customer information. The system further pre-populates one or more missing fields of the full profile associated with the customer based on said estimated customer information. The system.

Abstract: A system and a method for identifying and ranking agents are disclosed herein. The system includes an analytics engine which retrieves information from external and internal databases. The analytics engine uses the information retrieved from these databases, in addition to one or more success factors or key attributes, to identify and rank prospective agents. The analytics engine can also match one or more prospective agents with a general agent and provide ranking and performance assessment reports for evaluating and following up on the agent’s career development.

Abstract: A call management system of a call center identifies an inbound caller based upon computer analysis of customer identifiers, which may include at least two of customer name, street address, and zip code. Approximate string matching analysis matches n-grams generated from strings within customer identifiers, with n-grams generated from customer identification fields while searching one or more databases. Approximate string matching can incorporate a closeness metric based on Jaccard distance, and a Gaussian mixture model of best matches. In one embodiment, a polymr search engine analyzes customer identifiers of inbound callers to retrieve customer data, such as customer demographic data, matched to the customer identifiers. In another embodiment, the polymr search engine analyzes customer identifiers of inbound callers to identify repeat callers and retrieve previously collected customer data.

Abstract: Systems and methods for validating outputs from a machine learning model is disclosed. The machine learning model and a statistical model are executed to generate electronic documents in response to customer requests. A random sample of electronic documents generated from the machine learning model and the statistical model are then selected. A comparison is performed between the random sample of electronic documents generated from the machine learning model and the statistical model. The performance of the machine learning model is validated based on results of the comparison.

Abstract: A call management system of a call center identifies an inbound caller based upon computer analysis of customer identifiers, which may include at least two of customer name, street address, and zip code. Approximate string matching analysis matches n-grams generated from strings within customer identifiers, with n-grams generated from customer identification fields while searching one or more databases. Approximate string matching can incorporate a closeness metric based on Jaccard distance, and a Gaussian mixture model of best matches. In one embodiment, a polymr search engine analyzes customer identifiers of inbound callers to retrieve customer data, such as customer demographic data, matched to the customer identifiers. In another embodiment, the polymr search engine analyzes customer identifiers of inbound callers to identify repeat callers and retrieve previously collected customer data.

Abstract: Disclosed is a method and a system for receiving a request to generate an anonymized pool dataset, wherein the request comprises overall number of people in the dataset, one or more genetic condition categories, a genetic attribute associated with each category, and a percentage of the number of users associated with each category; querying and receiving from a second server a set of datasets associated with people, wherein each dataset comprises health data associated with a user and a corresponding genetic data; generating a pool dataset from the anonymized set of datasets, wherein the pool dataset corresponds to the received overall number of users in the dataset, genetic condition categories, the genetic attribute associated with each category, and a percentage of the number of people in each category.

Abstract: Systems and methods for validating outputs from a machine learning model is disclosed. The machine learning model and a statistical model are executed to generate electronic documents in response to customer requests. A random sample of electronic documents generated from the machine learning model and the statistical model are then selected. A comparison is performed between the random sample of electronic documents generated from the machine learning model and the statistical model. The performance of the machine learning model is validated based on results of the comparison.

Abstract: System and methods for an agent tool that identifies high value targets within a particular area, based on an individual's social media activity are disclosed. A computing device displays a user interface generated by an analysis module, and receives an agent's search criteria. Then, the analysis module scans a plurality of social media sites and selects the profile of individuals according to the search criteria. Next, the analysis module identifies the profile of members who are associated to the profile of each previously selected individual, and compares them with the profile of pleased customers. If both profiles match, then the analysis module has identified a high value target. The system also provides the full profile of each high value target found, when needed. The agent tool identifies high value targets in a simpler, less time-consuming and inexpensive way, while increasing the company's revenue.

Abstract: A system and method for automated body mass index is disclosed. The disclosed method operates within a system architecture including one or more computing devices, one or more servers, and one or more databases. A processor operating within the one or more servers executes one or more algorithms for detecting relevant features associated with a potential client's multimedia information. The method may include calculating feature values, such as abdomen circumference, face width, face height, cheekbone width, jaw width, and neck width, and the like as well as calculating the body mass index of the potential client using one or more regression algorithms. A baseline and updated BMI may be determined, and used for determining a baseline and updated value.

Abstract: Systems and methods for assessing the needs of customers using predictive modeling techniques are disclosed in which a server receives, from a computing device, a request to generate a recommendation for a first user, the server further receiving a set of attributes of the first user; identifies at least one missing attribute for the first user and an existing user profile corresponding to a second user having the set of attributes; executes an artificial intelligence model trained based on personas corresponding to a set of existing users, wherein the artificial intelligence model estimates the at least one missing attribute of the first user; updates a user profile of the first user using the estimated at least one missing attribute generated by the artificial intelligence model; generates the recommendation based on the updated user profile; and transmits the recommendation to be displayed on the graphical user interface.

Abstract: The embodiments recite systems and methods that improve the traditional underwriting process within a financial institution. These embodiments produce an underwriting model that emulates the resolution patterns of top performing underwriters. The underwriting model once is built and tested is incorporated into decision tools that provide underwriters with insightful advices when underwriting a client. The embodiments use statistical learning techniques such as support vector machine and logistic regression. These techniques can assume a linear or nonlinear relationship between factors and risk classes. Furthermore, the underwriting model also uses artificial intelligence tools such as expert systems and fuzzy logic. A company's underwriting standards and best underwriting practices may be updated periodically so that underwriting model based on decision heuristic keep improving the quality of its output over time.

Abstract: A routing system of a call center determines a plurality of advisor clusters to be assigned to each of a plurality of lead records stored in a lead model database. The predictive machine learning model inputs lead model data and advisor model data into a clustering analysis. Various modeling data are extracted from source lead data, sales data, and advisor data, in which the advisor data has been flattened for modeling. The predictive machine learning model applies a combination of a clustering analysis, a cluster model, and an aggregate conversion model to lead model data and user model data. The clustering analysis utilizes unsupervised clustering and supervised clustering, and outputs a plurality of advisor clusters and sales conversion scores. The clustering analysis clusters each of the advisors into one of the plurality of advisor clusters based on degree of similarity of a clustering vector.

Abstract: A system and a method for identifying, ranking, and matching agents are disclosed herein. The system may include an analytical engine which may collect information from external and internal databases. The analytical engine may use the information collected from these databases, in addition to a success criteria, for determining one or more success factors or key attributes of successful agents. An algorithm within the analytical engine may compute probabilities, trends and variability analysis for determining the impact of the defined key attributes in the successful career developments for prospective or active agents. The analytical engine may identify prospective agents and match them to suitable general agents. A list of agents' leads, ranking reports, and ongoing assessments may be communicated or supplied to general agents through the use of agent computing devices operatively coupled with the analytical engine.

Abstract: Methods and systems determine the protection needs and cross-selling capability to RS clients. A retirement protection system includes one or more software modules that interact with each other and are used to determine and identify the protection needs of high propensity clients, and develop strategies to cross-sell annuity/life products to those clients. The retirement protection system includes different components which may be implemented through one or more software modules running on a computing device. The retirement protection system involves the analysis of a sample of RS clients to determine cross selling capability of life, disability income, and annuity products. Additionally, the retirement protection system implements an experimental approach to pilot and refine the direct marketing model in order to create campaigns for high propensity target clients or groups.