AI system for assessing donor affinity from public and institutional data

Abstract

An AI system (100) for evaluating donor affinity from public and institutional data, comprising: a data entry module configured to capture structured and unstructured donor-related data from external public sources and internal institutional databases; a data preprocessing module designed to clean, normalize, and convert the input data into a machine-readable format; a feature extraction and enrichment module designed to identify, extract and enhance relevant donor features by using natural language processing, graph-based analysis and metadata tagging; an affinity assessment engine that uses machine learning algorithms to analyze the extracted features and generate a dynamic donor affinity assessment; an explainable AI module that is operationally connected to the scoring engine and configured to generate interpretable, human-readable justifications for each donor affinity score; a visualization and reporting module that is customized to present donor scores, behavioral profiles, and campaign recommendations through interactive dashboards; and an orchestration and feedback module configured to manage real-time data updates, automate model retraining, incorporate user feedback, and enable seamless integration with existing donor management or CRM systems.

Description

[0001] The present invention relates to an artificial intelligence system for assessing donor affinity that ingests and aggregates heterogeneous public and institutional datasets to predict donor propensity, capacity, and likelihood of engagement using machine learning. It includes pipelines for secure data ingestion, identity resolution, normalization, feature engineering, consent management, model training / validation, explainable inference, and the real-time delivery of results via APIs and dashboards.

[0002] Fundraising organizations, healthcare institutions, universities, and nonprofits often face the challenge of effectively identifying and engaging potential donors. Traditional donor profiling methods largely rely on the manual analysis of demographic information, donation history, and limited behavioral indicators. These approaches are time-consuming, have low predictive accuracy, and fail to integrate the diverse data streams available from public records, institutional databases, and digital platforms. As a result, organizations struggle to prioritize potential donors and frequently miss opportunities for targeted outreach and building long-term donor relationships.

[0003] The increasing availability of big data, coupled with advances in artificial intelligence, is enabling a new paradigm in donor engagement. By leveraging machine learning models and natural language processing techniques, donor affinity assessment can integrate vast amounts of structured and unstructured data from diverse sources. This ensures accurate prediction of donor capacity, intentions, and alignment with institutional goals, while adhering to data privacy regulations. However, no existing system provides a unified, secure, and explainable AI-based platform for donor affinity assessment. Therefore, there is a pressing need to develop a solution that optimizes fundraising strategies, strengthens donor trust, and delivers measurable improvements in institutional outcomes.

[0004] One objective of this disclosure is to provide an AI-based donor evaluation system that improves the accuracy of donor outreach for fundraising organizations.

[0005] Another objective of this disclosure is to automate the integration of public and institutional data sources for comprehensive donor profiling.

[0006] Another objective of this disclosure is to reduce the manual effort and time required to identify high-potential donors through intelligent data analysis.

[0007] Another objective of this disclosure is to enable real-time updates of donor ratings based on dynamic data inputs and behavioral changes.

[0008] Another objective of this disclosure is to generate explainable AI results that support transparent and trustworthy donor retention decisions.

[0009] Another objective of this disclosure is the visualization of donor affinity scores through customizable dashboards for strategic campaign planning.

[0010] Another objective of this disclosure is to support continuous model refinement through user feedback and data-driven learning.

[0011] Another objective of this disclosure is to ensure easy deployment and integration into existing donor management systems without technical barriers.

[0012] Further objectives and benefits of the present disclosure will become apparent from the following description, which is not intended to limit the scope of the present disclosure.

[0013] The present invention relates generally to an AI-driven system for assessing donor affinity that integrates public and institutional data. It uses machine learning models to predict donor engagement potential and optimize fundraising strategies through data intelligence.

[0014] One embodiment of the present invention comprises a data input module that aggregates donor-related data from various sources such as social media, news agencies, company data, and internal databases. This results in the creation of a comprehensive and up-to-date donor profile.

[0015] Another embodiment of the invention comprises a preprocessing and feature extraction module that cleans raw data, extracts key attributes, and enriches donor data using NLP and graph analysis. This enables a deeper understanding of behavior and the identification of affinities.

[0016] Another embodiment of the invention is the machine learning-based affinity rating engine, which assigns dynamic ratings to each donor in real time. It adapts to new data patterns and increases targeting accuracy for fundraising teams.

[0017] Another embodiment of the invention is the explainable AI module, which interprets model predictions and generates transparent, human-readable explanations for each donor assessment. This builds trust among users and facilitates decision-making.

[0018] Another embodiment of the invention is a reporting and visualization module that displays donor insights through interactive dashboards and customizable output. This enables fundraisers to prioritize leads and design engagement campaigns efficiently.

[0019] Another embodiment of the invention is the feedback and orchestration module, which manages continuous learning, user feedback loops, and model updates. It ensures that the system evolves with new trends and data inputs to guarantee sustained performance.

[0020] Another embodiment of the invention is the seamless integration capability with existing CRM systems and donor databases, which represents a plug-and-play solution.

[0021] This improves usability and accelerates acceptance in organizations with different technical infrastructures.

[0022] The present invention relates to an AI-based system for accurately predicting and evaluating donor affinity using public and institutional data sources. Conventional approaches to donor outreach are often limited by static datasets and manual analysis, resulting in missed opportunities and inefficient outreach. The invention overcomes these limitations by leveraging advanced data science techniques such as machine learning, natural language processing, and graph analysis to automatically assess the likelihood of a donor's engagement.

[0023] The system operates with a modular framework encompassing data ingestion, preprocessing, feature extraction, scoring, explainable AI, and reporting. It gathers data from various channels, including social media, news feeds, alumni databases, donation records, and CRM systems, and standardizes and enriches this information to create detailed donor profiles. These profiles are analyzed in real time to generate dynamic affinity scores, which help fundraising teams prioritize and personalize their engagement strategies.

[0024] A key feature of the invention is the integration of explainable AI, which ensures transparency regarding the factors influencing the evaluation of individual donors. This not only improves the trustworthiness of AI-supported recommendations but also helps fundraising staff make informed decisions with clear rationales. Furthermore, the system continuously learns and adapts through user feedback and data updates to maintain its relevance over time and as donor behavior evolves.

[0025] This invention significantly increases the efficiency and effectiveness of donor acquisition and retention. By automating data analysis and providing actionable insights via an intuitive dashboard, the system enables organizations to increase donation rates, optimize outreach campaigns, and build stronger, more personal donor relationships with minimal manual effort.

[0026] The invention is explained again below with reference to the figure. This shows: Fig. : an AI system (100) for evaluating donor affinity from public and institutional data.

[0027] Fig.Figure 1 illustrates an AI system (100) for evaluating donor affinity from public and institutional data. The present invention comprises an AI-supported donor affinity evaluation system that operates with several interconnected modules to process, analyze, and generate actionable insights about donors. The Data Ingestion Module collects structured and unstructured data from public sources (e.g., news feeds, social media, donation databases, business registers) and institutional records (e.g., alumni databases, patient data, CRM systems). This raw data is sent to the Data Preprocessing Module, which standardizes and cleanses the inputs, converts them into analyzable formats, removes redundancies, and processes missing values.Next, the feature extraction and enrichment module applies natural language processing and graph analysis to extract key donor attributes such as donation history, professional affiliation, geographic proximity, philanthropic interests, and network influence. These features are then passed to the Affinity Scoring Engine, which uses machine learning algorithms—including classification, regression, and clustering models—to calculate a dynamic donor affinity score based on real-time signals and predictive indicators. The Explainable AI module generates human-readable explanations for each score, increasing transparency and user trust.Finally, the visualization and reporting module provides insights through customizable dashboards and prioritization lists for campaign managers, with options to export or integrate the results into existing donor management tools. All modules are controlled via a central orchestration and feedback module, ensuring continuous learning, model refinement, and seamless updates based on user input and evolving data.

Claims

[1] An AI system (100) for assessing donor affinity from public and institutional data, comprising: a data entry module configured to capture structured and unstructured donor-related data from external public sources and internal institutional databases; a data preprocessing module designed to clean, normalize, and convert the input data into a machine-readable format; a feature extraction and enrichment module designed to identify, extract and enhance relevant donor features by using natural language processing, graph-based analysis and metadata tagging; an affinity assessment engine that uses machine learning algorithms to analyze the extracted features and generate a dynamic donor affinity assessment; an explainable AI module that is operationally connected to the scoring engine and configured to generate interpretable, human-readable justifications for each donor affinity score; a visualization and reporting module that is customized to present donor scores, behavioral profiles, and campaign recommendations through interactive dashboards; and an orchestration and feedback module configured to manage real-time data updates, automate model retraining, incorporate user feedback, and enable seamless integration with existing donor management or CRM systems. [2] AI system (100) according to claim 1, wherein the public data sources include social media feeds, online news articles, philanthropic databases, professional networking platforms and government registers. [3] AI system (100) according to claim 1, wherein the institutional data sources include internal donor databases, alumni records, patient registries, historical donation records and customer relationship management (CRM) systems. [4] AI system (100) according to claim 1, wherein the affinity assessment machine uses machine learning models selected from the group consisting of decision trees, support vector machines, logistic regression, gradient enhancement machines and neural networks. [5] AI system (100) according to claim 1, wherein the feature extraction and enrichment module further applies sentiment analysis and entity recognition to unstructured text data. [6] AI system (100) according to claim 1, wherein the explainable AI module applies explainability techniques including SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations) to interpret the effect of input features on donor values. [7] AI system (100) according to claim 1, wherein the visualization and reporting module enables users to filter and rank donors based on configurable point thresholds, interest categories or historical engagement metrics. [8] AI system (100) according to claim 1, wherein the orchestration and feedback module is further configured to initiate automatic retraining of the scoring engine based on user feedback, performance metrics and periodic data updates.

Images