Machine learning-based financial behavior prediction and adaptive budget optimization system
Patent Information
- Application Number
- DE202025104778
- Authority / Receiving Office
- DE · DE
- Patent Type
- Utility models
- Current Assignee / Owner
- Filing Date
- 2025-08-15
- Publication Date
- 2025-10-16
- Estimated Expiration
- 2035-08-31
Smart Images
Figure 00000000_0000_ABST
Abstract
Description
FIELD OF THE INVENTIONThe present invention relates to the field of financial technology systems and, more particularly, to a secure and modular machine learning based system for predicting user specific financial behavior and optimizing budgets through adaptive recommendations.BACKGROUND OF THE INVENTIONFinancial management tools have evolved from manual accounting to highly automated applications that can collect revenue, expenses, and investments. Conventional budgeting applications are based on static rule sets or user-defined constraints and cannot capture differentiated and evolving personal output behavior. With the increasing availability of digital transaction data from various sources, there is a clear need for smart systems that perform predictive financial behavior analyses, thus enabling precise and personalized budget recommendations.Existing AI-based approaches in this area often encounter several challenges. First, the integration of predictive machine learning models into consumer financial tools poses significant privacy concerns, particularly when processing personal financial histories in third party cloud environments. Second, previous systems often do not dynamically adapt to changed user circumstances, resulting in rapidly managed budget suggestions. Third, security risks resulting from unencrypted storage, insufficient access control, and lack of hardware level security integration remain largely unsolved.The present invention overcomes these deficiencies by providing a modular, secure, and scalable system for financial prediction and budget optimization. It uses simple, but powerful machine learning models to predict output patterns. The invention also provides hardware execution - structured as a secure personal financial center, that performs sensitive computations locally, is integrated into the cloud for non-sensitive operations, and permeates stringent access protection mechanisms.The development of personal financial management tools has been advanced by the increasing digitization of financial transactions, the spread of online and mobile banking, and the development of data analytics technologies. Early solutions in this area were primarily static accounting applications that allowed users to manually enter expenses and revenue and allow basic salt calculations as well as manual budget tracking. These systems were rudimentary, required a lot of user's displice in data entry, and were unable to automatically analyze trends or adapt to changed circumstances. The introduction of the end of 1990 online banking and the beginning of the 2000 years, while allowing semi-automatic data retrieval, integration has often been limited to certain institutions, and users have continued to manually categorize transactions and monitor their output behavior.In the following decade, the rise of financial aggregators such as mint, you need a budget (YNAB), and personal capital has provided more automation in personal financial management. These platforms were connected to multiple financial institutions via APIs or entitlement-based scrapping, thus enabling automatic retrieval and categorization of transaction data. Users could visualize output patterns, track their net capability, and set budget targets. These systems, although improving comfort, have typically relied on fixed, rule-based budgeting mechanisms. This meant that adjustments were made manually after a budget was established and did not reflect real-time behavioral changes. Such static constraints often failed to detect sudden changes in lifestyle, irregular revenue patterns, or unscheduled financial obligations, leading to inaccurate or legacy recommendations.In parallel, the Fintech industry began to experiment with predictive analytics to predict financial developments. Early predictive systems utilized linear regression, simple time-series predictions, and clustering techniques to identify output trends and potential bottleneckes. These models, while allowing some level of prediction, their prediction accuracy was limited by the constrained feature extraction, inability to model complex temporal dependencies, and dependency on historical averages rather than dynamic behavior adaptation. As a result, they tended to mispregnoses in scenarios with atypical output events, seasonal variances, or economic disturbances such as inflational drawers or market volatility. Moreover, many predictive budgeting tools have been embedded in proprietary banking applications, which restricted access to customers of certain financial institutions.Integration of machine learning into financial tools represents a significant technical conversion. Recurrent neural networks (RNNs), long short-term memory networks (LSTMs), and more recently also transform-based models, have enabled to detect sequential output dependencies, detect subtil behavior changes, and generate more robust predictions. Some platforms began incorporating anomaly detection modules to identify unusual transactions that could indicate fraud or excessive expenses. Despite these advances, implementation has been associated with significant challenges. Extensive machine learning models often required computing resources that exceeded mobile device performance, requiring the computing power to be offloaded to cloud servers. This has been a serious privacy concern because sensitive financial data has to be transmitted and processed remotely, creating potential points of attack for privacy violations or unauthorized access.Another limitation of existing machine learning based solutions is the lack of personalization of budget optimization. While some systems use predictive results to suggest economy goals or yield limits, these recommendations are often generalized and based on population-level statistical profiles rather than individual behavioral models. Users with individual financial patterns, such as Gig Economy workers with irregular revenue streams, people with multi-currency accounts, or people with cyclic seasonal expenses, often find these recommendations unsuitable. The inability to accommodate highly individualized constraints such as debubbling schedules, scheduled investments, or cultural output habits limits the practical utility of these systems.Security and data protection still belong to the most critical issues in this area. Many widely used platforms for budgeting and financial predictions store unencrypted transaction profiles in central databases and are thus a lucrative target for cyber attacks. Suspect security violations of financial data aggregation services have demonstrated the susceptibility of centralized architectures: millions of user data have been exposed in individual incidents. Even with encryption, key management methods are often insufficient and multi-factor authentication is not consistently enforced. Moreover, some services monetrate the users' financial data by aggregating and selling anonymous issue patterns to third parties. This approach can still be redeveloped to identify individuals in certain contexts. For privacy aware users, especially in jurisdictions with stringent privacy laws such as the European Union under the DSGVO, these practices present significant compliance and trust hurdles.From the standpoint of system integration, interoperability problems additionally limit the effectiveness of existing financial management tools. Financial data formats differ depending on institute, and although there are standards such as OFX (Open Financial Exchange) and ISO 20022, not all providers are attached. This results in inconsistent data granularity, delayed transaction reporting, and incomplete categorization. For example, a single purchase may be reported with only dealer name and amount, without categorization or location metadata. This reduces the effectiveness of predictive models. Attempts to use third party aggregation APIs often encounter rate constraints, authentication changes, or are totally blocked by financial institutions wishing to protect their proprietary customer data.In addition to these structural challenges, scalability of AI-based financial prediction systems is another area in which existing solutions have come into roll. As the number of users increases and the transaction volume increases, maintaining low-latency predictions and recommendations becomes computation-intensive. Many platforms respond by simplifying models or reducing the frequency of predictions, which in turn affects prediction accuracy. Cloud-based scaling can solve this computational problem, but involves cost printing, latency times in real-time use cases, and additional safety risks due to the increased attack area with distributed processing.Another problem is the engagement of the users and compliance with rules of behavior. Even if precise predictions and informed budget recommendations are generated, users often ignore them due to poor user interface, lack of contextual explanations, or continuous alert fatigue. Some applications bombard users with general issue alerts, causing them to completely disable them. Others present recommendations without transparent reasoning, resulting in lack of confidence. In AI-controlled systems, the problem of clarity is particularly pronounced; black box models can generate precise predictions, but without interpretable reasons users may not meet the recommendations.Efforts to solve data protection and computational problems have led to federated learning and edge computing studies in financial prediction. Federated learning enables model training on distributed user devices without transmitting raw data to the central server, which theoretically increases data protection. However, implementing such systems in heterogeneous device environments is complex, requires robust handling of non-independent and identically distributed (non-IID) data, and often has a lower convergence speed compared to centralized training. Edge AI approaches, where the inference is performed locally on the user device, are subject to hardware constraints, particularly in low power mobile environments, which limits the complexity of the usable models.Budget optimization in current systems is also technically restricted. Traditional optimization is based on a simple budgeting with turnover, in which fixed amounts are assigned to different issue categories regardless of current financial changes. More advanced tools utilize static optimization techniques such as linear programming, but do not accommodate non-linear constraints or stochastic fluctuations in revenue and expenses. Without adaptive optimization mechanisms that account for predictive uncertainties, such systems cannot provide budgets that flexibly adapt to varying conditions. This is particularly problematic for users in volatile business environments or with complex financial obligations.The integration of external services such as control explanation tools, plant platforms or insurance systems is often limited in existing solutions, which additionally limits their use as comprehensive financial planning tools. While there are APIs for some of these areas, differences in data models, privacy policies, and update cycles make seamless integration difficult. As a result, users are often forced to manage fragmented financial information across multiple platforms, which defeats the ultimate purpose of uniform budget optimization.In summary, although current solutions for financial tracking and prognosis have evolved from static master books to AI-based platforms, they are further limited by static budgeting models, insufficient personalization, data protection gaps, interoperability problems, computational scaling constraints, lack of explanation, and weak integration into more comprehensive financial ecosystems. These disadvantages result in an unsatisfaction need for a system that not only predicts individual financial behavior with high precision, but also optimizes budgets in real time, runs securely in cloud and local hardware environments, adjusts to highly individual financial contexts, and smoothly integrates into the more comprehensive financial service landscape. The proposed invention overcomes these limitations by combining secure, modular, and scalable architectures with lightweight, adaptive machine learning models, privacy-compliant computation, and both cloud-hosted and device-based execution modes, thus ensuring that users receive highly personalized, secure, and implementable financial insights.SUMMARY OF THE INVENTIONThe present invention provides a system for financial behavior prediction and machine learning based adaptive budget optimization. It includes a secure cloud hosted architecture, a transaction data acquisition and preprocessing module, a machine learning prediction controller, an adaptive budget recommendation module, and a user interface. The system acquires structured and unstructured financial transaction data, performs normalization, categorization, and feature extraction, and uses a lightweight prediction model such as a recurrent neural network (RNN), a temporal convolutional network (TCN), or a transformer-based time-series predictor to predict short- and medium-term output trends.The budget optimization module uses a dynamic constraint solver supported by optimization techniques such as linear programming, mixed integer programming, or evolutionary computation to generate customized budget assignments that take into account predicted outputs, user priorities, and economy goals. A security layer ensures that all personal financial information is encrypted, access is controlled by multi-factor authentication, and processing is separated between secure local execution and distributed cloud analysis.In a hardware embodiment, the invention also includes a secure financial computing device designed as a tamper-proof processing node with integrated trusted platform module (TPM), hardware encryption accelerators, and an embedded AI inference engine. The device securely connects to banking APIs, point-of-sale transaction (POS) services, and personal financial applications, and enables localized execution of sensitive ML computations while only encrypting model parameters and aggregated analyses are synchronized with the cloud system.The main object of the present invention is to provide a secure, intelligent and adaptive system for predicting financial behaviour and for budget optimisation which provides personalised recommendations based on individual output behaviour of individual users. The invention seeks to overcome the rigidity of existing budgeting tools by employing machine learning models that continually adapt to changing financial circumstances, thus ensuring that budget suggestions remain relevant and accurate over the long term. Another object is to improve sensitive financial data protection by an architecture that includes idle and during transmission encryption, hardware level secure enclaves, and multi-factor authentication. This ensures data protection and data integrity in both cloud and local processing environments. Another object is to provide a modular and scalable system design in which modules for transaction detection, data preprocessing, predictive analytics, budget optimization, and user interaction operate as loosely coupled but interoperable components. This allows seamless integration with third party services and future function extensions. Moreover, the invention aims to remedy the lack of transparency and confidence in AI-controlled financial tools by implementing clearable AI mechanisms that allow users to understand the reasons for predictions and budget adjustments. Another object is to provide a hardware-based solution - a secure personal financial computing device - that can perform localized machine learning processes for sensitive transaction data. This reduces the disclosure of private information to external networks and at the same time only encrypted or anonymized data is synchronized with the cloud. The invention also aims to ensure operating efficiency by employing slender and yet precise prediction models running on both high performance cloud servers and resource constrained local devices, thus extending accessibility. Through these combined goals, the invention seeks a comprehensive financial management solution that combines prediction accuracy, adaptive budget optimization, robust security, system scalability, and high user confidence in a single, practical, and technologically advanced platform.BRIEF DESCRIPTION OF THE FIGUREThese and other features, aspects, and advantages of the present invention will become more fully understood when the following detailed description is read with reference to the accompanying drawings, in which like numerals represent like parts throughout. The following applies here: FIG. 1 shows a block diagram of a system for financial behavior prediction and machine learning based adaptive budget optimization.Those skilled in the art will also appreciate that the elements in the drawing are shown for simplicity and are not necessarily to scale. For example, the flowcharts illustrate the method using the key steps to improve understanding of aspects of the present disclosure. Moreover, with regard to the construction of the apparatus, one or more components of the apparatus may be represented in the drawing by conventional symbols, and the drawing may only show the specific details relevant to understanding the embodiments of the present disclosure so as not to obscure the drawing with details readily apparent to those skilled in the art upon reading the present specification.DETAILED DESCRIPTION OF THE INVENTIONFor a better understanding of the principles of the invention, reference is made below to the embodiment shown in the drawing and this is described in specific language. However, the scope of the invention is not limited thereby. Changes and further modifications of the illustrated system, as well as further applications of the principles of the invention, are possible, as would normally occur to a person skilled in the art in the field of the invention.It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not intended to be limiting thereof.References throughout this specification to "one aspect," "another aspect," or similar terms mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Therefore, the appearances of the phrases "in one embodiment," "in another embodiment," and similar phrases in this specification may, but need not, all refer to the same embodiment.The terms "comprises," "comprising," or other variations thereof are intended to cover a non-exclusive inclusion, such that a process or method comprising a list of steps includes not only those steps, but may also include other steps not expressly listed or inherent in such a process or method. Likewise, the phrase "comprises... a" in one or more devices or subsystems or elements or structures or components does not exclude, without further limitations, the existence of other devices or other subsystems or other elements or other structures or other components or additional devices or additional subsystems or additional elements or additional structures or additional components.Unless otherwise defined, all technical and scientific terms used herein have the same meaning as understood by one of ordinary skill in the art. The system, methods, and examples provided herein are for illustration purposes only and are not to be considered limiting.Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.Referring now to FIG. 1, a block diagram of a system for machine learning based prediction of financial behavior and adaptive budget optimization is shown. The system 100 includes: a plurality of distributed processing nodes (102) for enabling communication between the nodes with low latency; a transaction data receiving processor (104) configured to establish authenticated connections with a plurality of financial data sources via application programming interfaces (APIs) supporting OAuth 2.0 or equivalent secure token-based authentication protocols. The capture module is also configured to normalize received transaction records into a standardized schema that includes at least a merchant identifier, a transaction category, a time stamp, a transaction amount, and optional geolocation metadata; a preprocessing engine (106) having a classification sub-module trained by supervised learning to assign transaction categories based on merchant identifiers and context attributes; and a feature extraction sub-module configured to calculate temporal, statistical, and behavioral feature vectors from the normalized transaction data; a prediction controller (108) comprising a plurality of lightweight neural network architectures including at least one recurrent neural network (RNN) and at least one attention-based temporal model, the prediction controller configured to predict short- and medium-term output trends by sequential processing of the feature vectors; a budget optimization computational unit (110) configured to solve multi-constrained budget assignment problems using a hybrid approach comprising a primary linear programming solver and an additional heuristic optimization technique, the budget optimization computational unit further configured to dynamically adapt budget assignments based on updated predictions and user-defined constraints; a security subsystem (112) configured to encrypt static data with AES-256 encryption, transmitted data with TLS 1.3, and securely store keys in a hardware-based trusted platform module (TPM); and a user interaction interface (114) configured to display budget recommendations, predicted output trends, and outputs to model explanation capability via at least one of the following interfaces: a web application, a mobile application, or a hardware device interface.In one embodiment, the transaction data collection processor (104) is also configured to implement a rate limiting mechanism and asynchronous batch protocol to minimize latency and API throttling, and validate incoming records by performing cross-source matching using hash-based consistency checks to identify incomplete or inconsistent transaction entries prior to storage in a secured data file.In one embodiment, the preprocessing engine (106) also includes a dealer classification neural network that has been pre-trained from a tagged dataset of dealer codes and fine-tuned using user-specific transaction histories. The classification network is executed in a federated learning framework in which updates to model weighting are aggregated in the cloud without raw transaction data being transmitted from user devices.In one embodiment, the prediction controller (108) also includes a transformer-based sequence model configured with position coding for time alignment, a multi-head attention mechanism for long distance dependencies detection, and a recurrent residual link for stability of predictions over non-uniform time intervals, the model being optimized for execution with few parameters to enable inference on resource constrained local devices.In one embodiment, the budget optimization computation unit (110) comprises a stochastic constraint solver configured to account for probabilistic fluctuations in predicted revenue and outputs by introducing uncertainty limits into the optimization model. The solver is also configured to generate multiple alternative budget allocation plans that are ordered by robustness under simulated scenarios with conjunction fluctuations.In one embodiment, the security subsystem (112) also includes a policy-based access control framework that forces role-based permissions for microservices, a policy for rotation of cryptographic keys with automatic execution, and a secure enclave for isolation of sensitive computing processes from non-privilege system components.In one embodiment, the device further comprises a hardware-based, secure personal financial computing device comprising: a tamper-proof enclosure having an intrusion detection circuit; a secure processing unit having an integrated AI inference accelerator optimized for recurrent and attention-based models; a hardware-based cryptographic coprocessor for device internal key generation, encryption, and signature verification; a local solid state memory having full hard disk encryption and a physical key management interface; and a secure communication interface configured to establish VPN tunnels to the secure computing environment hosted in the cloud, the device configured to perform local inferences of the prediction control unit while transmitting only encrypted aggregated analyses to the cloud.In one embodiment, the secure personal financial computing device further includes an integrated capacitive touch screen interface configured to render real-time budget visualizations using a local rendering engine, the engine capable of rendering interactive output heat maps, output temporal trend diagrams, and budget compliance indicators without dependence on external rendering services.In one embodiment, the user interaction interface (114) also includes an artificial intelligence (XAI) declarable module configured to generate interpretable argumentation paths for each budget recommendation. The module uses Slapley Additive Explantation (SLAP) values to determine the relative contribution of each input feature to the prediction and provides the explanation of an interactive visualization component.In one embodiment, the prediction controller (108) and budget optimization compute unit (110) are provided as containerized microservices that are orchestrationd using a Kubernetes-based control plane. This allows elastic scaling of prediction workloads based on real-time transaction recording rates, with service discovery and communication between modules secured using mutual TLS authentication between containers.In one embodiment, the system is implemented in a physically secure rack-mounted computing device that includes separate dedicated hardware subsystems for each functional unit. The distributed processing nodes of the secure cloud environment are implemented as blade servers with fixed high-speed backplane connections to enable low-latency communication between the nodes. The transaction data acquisition processor is implemented on a dedicated circuit board and includes a hardware accelerated data normalization pipeline implemented by field programmable gate arrays. These are configured to analyze incoming transaction streams in real time and to map them to the standardized schema. The preprocessing engine is located on a separate processing module with a graphics processor optimized for parallel matrix computations. This speeds up classification and feature extraction without relying on general CPU cycles. The prediction controller is made up of application specific integrated circuits with hard-wired recurrent neural network cell structures and fixed function attention mechanisms that enable the direct execution of the temporal sequence processing in silicon. The budget optimization computing unit includes a dedicated linear programming solver implemented as a fixed logic array and a supplemental heuristic search accelerator implemented in an FPGA structure to process constraint-based allocation tasks in hardware. The security subsystem integrates a discrete trusted platform module soldered to the system's motherboard. A tamper resistant enclosure and physical intrusion detection sensors provide protection for encryption keys and secure boot sequences. The user interface may be presented via a dedicated hardware console with touch screen LCD and physical navigation controllers directly connected to the system motherboard. As a result, all functional components are maintained in a tangible, hardware-implemented architecture.The system is based on a secure, distributed architecture that integrates data acquisition, preprocessing, machine learning predictions, adaptive budget optimization, and secure user interaction in a unified platform for predicting financial behavior and budgeting. The process begins with the transaction data receiving processor that establishes authenticated connections to multiple financial data sources, including bank institutions, payment handlers, and digital wallets. Once the connections are made, the recording module retrieves transaction records in different formats and normalizes them into a standardized schema that includes merchant identifiers, transaction categories, time stamps, transaction amounts, and optional geolocation metadata. The module uses asynchronous batch protocols and rate limiting mechanisms to prevent API throttling, while hash-based consistency checks ensure the completeness and integrity of each record. Transactions with inconsistent data are marked for re-recording or tuning before entering the preprocessing pipeline.The preprocessing engine is designed to convert raw datasets of transactions into structured inputs which are suitable for predictive modelling. A classification submodule, implemented as a dealer classification neural network, assigns output categories based on dealer identifiers, text descriptions, and contextual transaction attributes. This model is pre-trained on the basis of a global dataset of dealer codes and further refined using user-specific transaction profiles via a federation learning framework. Federation learning ensures that the financial raw data remains on the local user device during model training and only weight updates are securely transmitted to the central aggregator for model synchronization. After categorization, a feature extraction submodule calculates a comprehensive set of descriptors for each user, including temporal features such as transaction frequency distributions, moving averages, and seasonal indicators, statistical features such as variance, skewness, and output concentration metrics, and behavioral features that detect deviations from historical output standards.The prediction controller processes these feature vectors to predict both short-term and medium-term output trends. The engine is designed as a hybrid of recurrent neural networks (RNNs) and transformer-based sequence models. The RNN component implemented with gated recurrent units (GRU) efficiently captures sequential dependencies in the output behavior of the users, while the transformer-based component equipped with position coding and multi-head attention, models long-term temporal dependencies and context-specific variations in transaction patterns. A recurrent residual connection mechanism stabilizes predictions over irregular time intervals and thus weakens deviations in low transaction activity. These models are deliberately designed with configurations with few parameters so that inferences can be performed in both high capacity cloud environments and on local devices with limited resources without appreciable performance degradations.Once the predictions have been made, they are fed into the budget optimization computing unit, which implements a multistage computation process. The engine first formulates the budgeting task as an environmental optimization problem, taking into account predicted outputs, predicted revenues, and user-defined constraints such as economy goals, Schuldentilgungsverpflichtungen and limits for discretionary outputs. The primary solver uses linear programming to efficiently handle deterministic constraints, while a secondary heuristic optimization level - implemented as genetic technology - examines non-linear and multi-modal solution spaces. This hybrid approach allows the system to handle complex budgeting scenarios, including variable revenue streams, stochastic expenses, and competing financial goals. To account for the prediction uncertainty, the engine integrates handling stochastic constraints by applying probability limits to the predicted inputs. This generates several alternative budget assignments that are ordered by their robustness under simulated conjunction fluctuations.Safety is integrated deeply into each operating level. All quiescent transaction data is encrypted with AES-256, while data is encrypted with TLS 1.3 during transmission between modules, devices and cloud services. Cryptographic keys are generated and stored in a hardware-based trusted platform module (TPM) to prevent extraction or duplication. The system uses a policy-based access control framework with role-based permissions to ensure that microservices can only access the data required for their function.Key rotation policies are automated to reduce long term key exposure, and sensitive computations, such as those with unencrypted financial data, are isolated in secure enclaves inaccessible to non-privilege processes.The hardware implementation of the invention, the secure personal five-way hub (SPFH), performs the same prediction and optimization techniques locally to improve data protection. The SPHF has a tamper-proof enclosure with intrusion detection circuitry that disables operation upon detection of physical manipulations. The secure processing unit integrates an AI inference accelerator optimized for the recurrent and attention-based models described above, as well as a hardware-based cryptographic coprocessor for on-device encryption and decryption as well as signature checking. Local memory uses full disk encryption with physical key management interfaces for secure provisioning. The SPFH maintains encrypted VPN tunnels to the secure cloud environment and only transmits anonymous aggregates and model updates. This ensures that raw transaction data never leaves the device.The user interface, which is accessible via the Internet, mobile devices or directly via the SPFH touchscreen, provides real-time visualizations of the predicted output trends, budget recommendations and compliance metrics. An Artificial Intelligence (XAI) module processes model predictions and budget outputs to generate feature attribution maps using Shapley Additive Explant (SHAP) values. Thus, users can understand the relative impact of various input factors, such as recently made high value transactions, recurrent subscription costs, or seasonal patterns, on the generated predictions and recommendations. Visual outputs include interactive outputs heatmaps, temporal output trend plots, and budget compliance indicators-all in real time and without external rendering services.The system is provided as containerized microservices managed by a Kubernetes control plane. This allows flexible scaling based on transaction recording rates and predictive computational requirements. Communication between services is secured by mutual TLS authentication so that only verified services can communicate. Data storage takes place in geographically distributed redundant clusters with erasure coding for fault tolerance. Preprocessing nodes are deployed in edge computing environments to reduce latency for users in different geographic regions. Data protection is further enhanced by the application of differential privacy during model training. In this case, calibrated noise is inserted into aggregated gradient updates in order to prevent the reverse engineering of individual user data from trained models.The system sequence is therefore cyclic and adaptive: transaction data is securely captured and preprocessed to form high-quality feature vectors; the prediction control unit predicts the output behavior over a plurality of time horizons; the arithmetic unit for budget optimization generates adaptive assignments which take account of both deterministic restrictions and probabilistic variations; safety measures ensure data confidence and system integrity throughout; and user-oriented interfaces provide transparent, clearable recommendations. Continuous model training through federated learning and regular reassessment of constraints ensures that both predictions and budgets remain matched to the evolving financial realitys and provide a dynamic, safe, and user-specific financial management experience.The system according to the invention is implemented as a modular, service-oriented architecture and is hosted on a secure cloud infrastructure. The data acquisition module is configured to interface with multiple data sources, including banking APIs, credit card processors, electronic wallets, and personal financial software. Incoming transaction streams are authenticated with OAuth 2.0 or similar secure token-based authentication and the data is transported over encrypted channels with TLS 1.3. Raw transaction data is normalized to a standard schema that includes transaction type, merchant category, time stamp, geolocation (if available), and transaction amount.A preprocessing sub-module performs extended categorization. Rule-based filters and classification models of machine learning are used here, which are trained to identify dealer patterns and output classes. Feature extraction techniques are then used to calculate statistical metrics (moving averages, variances, seasonal components), behavioral vectors (frequency of particular output categories, deviations from historical standards), and temporal features for subsequent predictive modeling.The prediction controller supports a number of lightweight models that are optimized for inference on devices and in the cloud. These include gated recurrent units (GRUs) for detecting sequential output patterns, temporal attention based transformer architectures for modeling long distance dependencies, and ensemble regression techniques for robust predictions under unsafe input conditions. Models are retraining step by step with new transaction data. In this case, federated learning may be used to hold raw data locally and at the same time update the common model weights across the user.The budget optimization module is a constraint-based solver that is integrated into the prediction results. It takes into account user-defined constraints such as minimum saving goals, upper limits for survey outputs, and debubbling plans. The optimization takes place with the aid of a hybrid approach, in which a primary solver, such as the simplex method for linear constraints, is supplemented by heuristic or genetic techniques for nonlinear budget restrictions. The result is adaptive budget recommendations that are regularly re-calibrated based on the incoming financial activities.The security and data protection subsystem is a central part of the invention. All data stored in the cloud is encrypted with AES-256, while the on-device memory uses full disk encryption with hardware-based key storage in the TPM. Access to user interfaces-whether mobile, web-based or device-based-is effected by multi-factor authentication and biometric verification, if supported. Detailed access policies ensure that only authorized microservices can access sensitive records.The hardware execution, the so-called secure personal five-way hub (SPFH), consists of a hardened housing with tamper-proof seals, an ARM-based secure processing unit with integrated AI inference accelerators, a dedicated cryptographic coprocessor for key management and a solid-state memory protected by physical and logical security levels. The SPFH provides Ethernet and WLAN connectivity with VPN tunneling for cloud communication. Local execution of prediction models ensures that the raw data of the transactions remain in the device and only model updates and anonymous sums are transmitted to the central system. The device also provides a touch screen interface for budget visualization, real-time issue alerts, and secure configuration management.The invention relates to the field of intelligent infrastructure monitoring and fault detection, and more particularly to systems and methods for real-time detection and localization of water leaks in pressure line networks using artificial intelligence, advanced signal processing, and multisensor fusion. More specifically, the invention relates to a hybrid acoustic-ultrasound pressure monitoring architecture integrated with deep learning based anomaly detection techniques and geospatial leak triangulation frames. The disclosed system finds application in municipal water distribution networks, industrial liquid transport systems, and critical infrastructure pipelines where undetected leaks can cause economic losses, resource waste, and safety risks. The technical field includes sensor network technology, signal processing techniques, spatio-temporal data modelling, AI-driven event classification and probabilistic geolocation methods for diagnosing fluid leaks.The drawings and the foregoing description show examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be divided into multiple functional elements. Elements of one embodiment may be added to another embodiment. For example, the order of the processes described herein may be changed and is not limited to the manner described herein. Moreover, the actions of a flow chart need not be implemented in the order shown; nor do all actions necessarily have to be performed. Also, actions that are not dependent on other actions may be performed in parallel with the other actions. The scope of the embodiments is by no means limited by these specific examples. Numerous variations, whether or not explicitly stated in the specification, such as differences in structure, dimensions, and material use, are possible. The scope of the embodiments is at least as broad as recited in the following claims.Advantages, other advantages, and solutions to problems have been described above with respect to certain embodiments. However, the advantages, merits, solutions to problems, and any components that may result in a particular advantage or solution occurring or becoming clearer are not to be construed as critical, required, or essential features or components of individual or all claims.REFERENCES100 A system for machine learning-based prediction of financial behavior and for adaptive budget optimization. 102 processing node 104 processor for receiving transaction data 106 preprocessing engine 108 prediction control unit 110 budget optimization computing unit 112 security subsystem 114 user interaction interfaceReferences included in the specificationThis list of documents cited by the applicant has been produced in an automated manner and is only included for the better information of the reader. The list is not part of the German patent application or utility model application. The DPMA does not take any adhesion for any faults or omissions.Cited Non-Patent LiteratureISO 20022
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Claims
A computer-implemented system for predicting financial behavior and adaptive machine learning budget optimization, comprising: a plurality of distributed processing nodes to enable communication between the nodes with low latency; a processor for receiving transaction data configured to establish authenticated connections with a plurality of financial data sources, the receiving module further configured to normalize received transaction records into a standardized schema comprising at least a merchant identifier, a transaction category, a timestamp, a transaction amount, and optional geolocation metadata; a preprocessing engine comprising a classification sub-module trained by supervised learning to assign transaction categories based on dealer identifiers and context attributes, and a feature extraction sub-module configured to calculate temporal, statistical and behavioral feature vectors from the normalized transaction data; a prediction controller comprising a plurality of lightweight neural network architectures including at least one recurrent neural network (RNN) and at least one attention-based temporal model, wherein the prediction controller is configured to predict short-term and medium-term output trends by sequential processing of the feature vectors; a budget optimization compute unit configured to solve multi-constrained budget allocation problems using a hybrid approach that includes a primary linear programmer solver and an additional heuristic optimization technique, wherein the budget optimization compute unit is further configured to dynamically adjust budget assignments based on updated predictions and user-defined constraints; a security subsystem configured to encrypt idle data and during transmission and secure key storage in a hardware-based trusted platform module (TPM); and a user interaction interface configured to display budget recommendations and predicted output trends via at least one of a web application, a mobile application, and a hardware device interface.The system of claim 1, wherein the transaction data receiving processor is further configured to implement a rate limiting mechanism and an asynchronous batch protocol to minimize latency and API throttling, and validate incoming records by performing cross-source matching using hash-based consistency checks to identify incomplete or inconsistent transaction entries prior to storage in a secured data series.The system of claim 1, wherein the preprocessing engine further comprises a merchant classification neural network pre-trained from a tagged dataset of merchant codes and fine-tuned using user-specific transaction histories, wherein the classification network is executed in a federated learning framework in which updates of model weighting are aggregated in the cloud without raw transaction data being transmitted from user devices.The system of claim 1, wherein the budget optimization computation unit includes a stochastic constraint solver configured to account for probabilistic fluctuations in the predicted revenue and outputs by introducing uncertainty limits into the optimization model, the solver further configured to generate a plurality of alternative budget allocation plans ordered by robustness under simulated scenarios with conjunction fluctuations.The system of claim 1, further comprising a hardware-based secure computing device for personal financial, the device comprising: a tamper-proof enclosure with intrusion detection circuitry; a secure processing unit incorporating an AI inference accelerator optimized for recurrent and attention-based models; a hardware cryptography coprocessor for on-device key generation, encryption, and signature verification; local solid-state storage with full hard disk encryption and a physical key management interface; and a secure communication interface configured to establish VPN tunnels to the secure computing environment hosted in the cloud, wherein the device is configured to perform local inferences of the prediction control unit while only encrypted aggregated analyses are being transmitted to the cloud.The system of claim 1, wherein the prediction controller and budget optimization computational unit are provided as containerized microservices that are orchestrationd using a Kubernetes based control plane, thereby enabling elastic scaling of prediction workloads based on real-time transaction recording rates, wherein service discovery and communication between modules are secured through mutual TLS authentication between containers.