An asset management system is disclosed where a plurality of financial databases may connect with and be in communication with an asset management tool. The asset management tool is able to create an individual spending plan (scheduled distributions) and matching fixed income investment portfolio for a user based on inputted parameters with automatic distributions from the portfolio consistent with the individual spending plan occurring with a high degree of certainty. The asset management tool may communicate with the plurality of financial databases to put the individual spending plan and matching portfolio into action and is able to calculate and communicate an optimized surplus investment as a means of self-insuring so that the predefined schedule of distributions is met with the effective certainty one would expect from an insured/guaranteed income product.

An object is to improve a technique of automatically determining whether to make an automatic response or to cause an operator to respond. A support apparatus includes a reception unit that receives a request related to asset management from a support subject, and a response control unit that determines whether to cause an operator to respond to the request or to make an automatic response to the request according to content of the request. According to this support apparatus, it is possible to support the decision making of a subject who manages assets.

Computer-based system, method and apparatus aggregating data of equity and debt securities, and investment portfolios, to benefit multiple investment companies (ICs) with complementary objectives by transforming an investment portfolio into a source of significantly more returns for each investor IC's objectives by apportioning unequally the portfolio's benefits, risks and obligations to each IC invested in the portfolio. Components include a securities data aggregation computer (SDAC), a portfolio data aggregation computer (PDAC), a portfolio comparison computer (PCC), a Portfolio Modeling Computer (PMC), a computer modeling and displaying ways to optimize the benefits and obligations of the portfolio (CDBO). Computers use investment characteristics to dynamically display the portfolios and combinations of portfolios, to model alternative portfolios to serve the complementary objectives of each IC. An embodiment of the invention unites, in the creation and management of a single portfolio, two or more ICs with complementary objectives.

Some embodiments are directed to a resource allocation analysis system implemented via a back-end application computer server. A resource data store may contain electronic records associated with a set of resource types, each electronic record including an electronic record identifier and resource parameter. The back-end application computer server may receive, from the resource data store, information about a set of resource types to be analyzed, including the associated resource parameters. The computer server may then execute a differential evolutionary algorithm to optimize the set of resource types based on at least one non-linear constraint and generate resource analysis results. The back-end application computer server may, according to some embodiments, perform a resampling process that uses non-parameterized historical data, regression on at least one resource type, and moment matching.

The system according to the embodiment comprises a collection unit, a storage unit, an analysis unit, an acquisition unit, a provision unit, and an execution support unit. The collection unit collects energy consumption data. The storage unit stores the data collected by the collection unit in the Cloud. The analysis unit analyzes the data stored by the storage unit. The acquisition unit acquires the latest energy trends or investment destination information based on the data analyzed by the analysis unit. The provision unit provides a renewable energy investment proposal based on the information obtained by the acquisition unit. The execution support unit provides specific steps for executing the investment proposal provided by the provision unit.

A non-transitory computer readable medium storing computer-executable instructions that, when executed by a processor, cause a machine learning (ML) engine and/or Artificial Intelligence (AI) to: train a machine learning model of the machine learning engine using financial datasets; providing parameter values to a Savings Plan Creator via a user interface; generating, by the Savings Plan Creator, a savings plan for a short or a long-term savings instrument based on the provided parameter values, the savings plan creator being driven by the machine learning engine that matches the provided parameter values to an existing savings plan selected from a Plans Database having existing savings plans stored therein, or that synthesizes a customized savings plan.

Computer-implemented systems and methods monitor financial risk monitoring using geospatial data. The system includes a data ingestion pipeline that processes heterogeneous geospatial data, normalizes the data into a common spatial reference framework, and applies hierarchical spatial indexing. The indexed data is stored in a geospatial database configured to maintain metadata and alert rules. A financial system stores information linking financial instruments and portfolios to geospatial regions. An application programming interface (API) layer enables spatial querying, rule-based alert evaluation, and notification delivery. A user interface subsystem deployed on client devices facilitates interaction with the system through graphical dashboards, programmatic tools, or natural language inputs. The system supports real-time monitoring and automated alerting based on spatial triggers and financial exposure thresholds, enabling decision-making for risk-sensitive financial domains. The architecture supports high-throughput processing, flexible deployment, and integration with external financial systems and workflows.

The disclosed invention presents an AI-driven, dynamic, task-based information retrieval and processing system tailored for the financial sector. Utilizing advanced machine learning, natural language processing, and data analytics, this system automates the decomposition of complex tasks into manageable sub-tasks, executes these through a sophisticated data processing pipeline, and generates customized reports. The system enhances efficiency by integrating knowledge graphs for query augmentation, employing noise filtering algorithms, and adaptively re-ranking documents to align with user-specific contexts and objectives. This approach addresses the challenges of manual data synthesis and the inflexibility of existing semi-automated systems, significantly reducing the time and labor involved in generating precise and relevant financial reports. By streamlining the retrieval and processing of vast and varied financial data, the system supports informed decision-making and reduces operational costs, making it a valuable tool for financial analysts and investment professionals.