Computing system operating predictive financial operations platform

Abstract

A computing system hosts a financial operations platform comprising three layers: a behavioural prediction layer configured to predict the distribution of behaviours for at least one case, wherein each case is associated with at least one baseline condition defined by the at least one source of financial and economic data; a counterfactual simulation model layer configured to generate a plurality of scenarios based on modifications to the at least one baseline condition; and an aggregate optimization layer configured to computationally evaluate the said plurality of scenarios, and therefrom to select an optimal scenario for each case according to predetermined incentives or constraints. The system determines optimal scenarios for cases by evaluating a plurality of generated scenarios based on predicted behavioural distributions.

Description

[0001] COMPUTING SYSTEM OPERATING PREDICTIVE FINANCIAL

[0002] OPERATIONS PLATFORM

[0003] FIELD OF THE INVENTION

[0004] The present invention relates to computing systems for trading operations in general, and for computing systems for currency trading operations in particular.

[0005] BACKGROUND OF THE INVENTION

[0006] The present application expands the scope and detail taught in the associated US Provisional Application, which focused specifically on computer systems configured for operations in currency exchange environments. The same system taught therein, with some modifications detailed in this application, is applicable to multiple domains - with operations in currency exchange environments as a preferred embodiment.

[0007] Multi-user financial institutions consistently strive to price their products in a way that brings the outcome as close as possible to the optimal profit point, and the computer systems used for this purpose are configured accordingly.

[0008] This optimization leads to two desirable and interdependent outcomes:

[0009] 1. The financial institution increases its profits by capturing close to 100% of its unrealized profit potential; and / or

[0010] 2. The institution may choose to allocate part of that newly realized potential to its users

[0011] - effectively reducing their costs. These two outcomes have a mutually reinforcing, recursive effect: lowering user costs increases demand, bringing in more users, more funds retained within the institution, greater financial activity, and longer money retention periods. This, in turn, raises the institution’s profit potential - and so the cycle continues.

[0012] However, in practice, financial institutions today address this optimization challenge using fragmented tools and siloed approaches, an approach inherently limited by the configuration, architecture, and data processing capacity of the computing systems utilized for their operations. Pricing strategies are often determined separately for each product line or business unit, relying on historical data, static models, and periodic manual adjustments, squandering the potential processing power, real-time predictive capabilities, and autonomous optimization functionalities that modern computing systems could enable. These conventional approaches fail to fully capture dynamic user behavior, cross-product interactions, or system-wide trade-offs between institutional profit and user-side costs because their underlying computational models lack integration, adaptability, and multidimensional optimization across the institution’s operations.

[0013] As a result, financial institutions using conventional computing systems face cumulative inefficiencies that erode long-term profitability and competitiveness. Instead of achieving optimal balance, they risk either overcharging users (leading to customer churn) or underpricing services (sacrificing profits). These systemic misalignments limit the institution’s ability to sustain growth, retain customers, and fully leverage its financial and operational resources.

[0014] The present invention provides a generalized solution to these problems, by implementing a novel and inventive computer system architecture designed to manage and manipulate digital data associated with financial operations. Specifically, the invention introduces a multi-layered computational framework capable of predicting behavioral patterns, simulating alternative economic scenarios, and optimizing aggregate outcomes, thereby overcoming the limitations of static or siloed systems and enabling continuous, data-driven reconfiguration of financial environments toward institution-defined objectives.

[0015] SUMMARY OF THE INVENTION

[0016] The following embodiments and aspects thereof are described and illustrated in conjunction with systems, devices and methods which are meant to be exemplary and illustrative and not limiting in scope. In various embodiments, one or more of the abovedescribed problems have been reduced or eliminated, while other embodiments are directed to other advantages or improvements.

[0017] According to first aspect of the invention, a computing system comprises at least one processor, at least one hardware accelerator component, at least one non-volatile memory source, at least one network interface, at least one source of financial and economic data, and at least one message fabric. Said computing system hosts a financial operations platform comprising three layers: a behavioural prediction layer configured to predict the distribution of behaviours for at least one case, wherein each case is associated with at least one baseline condition defined by the at least one source of financial and economic data; a counterfactual simulation model layer configured to generate a plurality of scenarios based on modifications to the at least one baseline condition; and an aggregate optimization layer configured to computationally evaluate the said plurality of scenarios, and therefrom to select an optimal scenario for each case according to predetermined incentives or constraints. The system determines optimal scenarios for cases by evaluating a plurality of generated scenarios based on predicted behavioural distributions.

[0018] According to another aspect of the invention, the non-volatile memory source comprises at least one columnar case-store and a plurality of case records, wherein each case record includes a user identifier, an asset descriptor, a state vector derived from the financial and economic data, and a range of timestamps.

[0019] According to another aspect of the invention, a machine learning model operated by the hardware accelerator runs in batch-processing mode using on-device memory and parallel execution lanes to compute a time-value metric for each case record.

[0020] According to another aspect of the invention, the counterfactual simulation model layer generates a plurality of scenarios for each case-record by enqueuing parameter sets into the message-queue fabric. Each scenario modifies at least one controllable parameter selected from: spread, interest rate, fee, retention window, and settlement window.

[0021] According to another aspect of the invention, the aggregate optimization layer operates on the hardware accelerator, batching adjacent scenarios together for parallel computation.

[0022] According to another aspect of the invention, the optimization process is executed on at least one processor and is configured to terminate within a real-time budget defined by a system-configuration parameter.

[0023] According to another aspect of the invention, the optimal scenario is committed to an immutable event log in the non-volatile memory source, and instructions associated with the scenario are generated and published via the message fabric. The platform is configured so that processing latency and compute usage are lower than a baseline single-threaded

[0024] CPU-only implementation.

[0025] According to another aspect of the invention, the asset descriptor denotes at least two currencies. The financial operations platform batches opposing exchange requests into bounded-time windows, referencing interbank rates derived from the financial and economic data. Residual imbalances are reconciled by routing orders to at least one liquidity provider node, and the optimization solver allocates costs among users proportionally to their time-value metrics.

[0026] According to another aspect of the invention, each case-record may represent a current account balance where baseline conditions include debit and credit interest parameters. The scenario generator varies these interest parameters to determine an optimal scenario that maintains platform liquidity coverage above a policy threshold. Overdraft exposure is financed from time-value generated by deposit balances.

[0027] According to another aspect of the invention, the asset descriptor denotes an interestbearing product selected from a fixed-term deposit, a flexible savings product, or a hybrid instrument. The counterfactual simulation layer adjusts tenor and rate grids, the behavioural prediction layer estimates early-withdrawal probability, and the aggregate optimization layer maximizes net present value under a predefined customer-churn constraint.

[0028] According to another aspect of the invention, a non-transitory computer-readable storage medium stores instructions that cause the system to execute the steps of the behavioural, simulation, and optimization layers, while satisfying latency and compute usage thresholds.

[0029] According to another aspect of the invention, the financial operations platform further comprises a multi-layered architecture including at least one Convolutional Neural Network (CNN) and at least one Large Language Model (LLM), wherein: (a) the CNN model receives structured numerical data including, but not limited to, user-level financial behaviours, transaction history, economic indicators, and platform-level aggregate trends, and outputs feature embeddings representing behavioural and temporal patterns relevant to each case; (b) the LLM processes unstructured textual and semi-structured data including news reports, financial statements, user-generated inputs, or regulatory publications, and extracts latent features relevant for prediction and scenario configuration; (c) the outputs of the CNN and LLM models are jointly processed by an optimization engine that simulates multiple scenarios for each case in the counterfactual simulation model layer; (d) the aggregate optimization layer selects, in real time, the optimal pricing configuration across the tested scenarios to meet one or more institutional goals such as profitability, user acquisition, or volatility hedging, according to said predetermined incentives and constraints; and (e) the financial operations platform differentially prices each user or transaction based on the predicted value of the case over an evaluated period of time, incorporating both immediate transaction characteristics and expected time-value derived from a predicted set of follow-up economic actions.

[0030] According to another aspect of the invention, a platform hosted on the system connects at least one large player (LP), one small player (SP), and at least one source of economic data.

[0031] At least one SP deposits funds in multiple currencies, earning a first interest during a fixed term. After conversion to E-money, a second interest is earned for the unfixed period during which the E-money remains on the platform. The platform nets exchange requests from SPs for currency pairs using exchange rates transparently derived from the economic data, with imbalances reconciled by purchasing the volume difference from at least one LP. Transaction costs from the purchase are accounted for by the first and second interest, and the netted exchanges are operated in batches.

[0032] According to another aspect of the invention, the case relates to a currency exchange request made by at least one SP between two currencies. Scenarios relate to available exchange rates, and the exchange rates derived from the economic data source are compiled into batches.

[0033] According to another aspect of the invention, the midpoint of exchange values at the end of the batch between markets for both currencies in a pair is selected as the exchange rate available to SPs on the platform.

[0034] According to another aspect of the invention, funds deposited as E-money on the platform belong directly to the SPs.

[0035] According to another aspect of the invention, netting of SP currency exchange requests is optimized per exchange batch by calculating for an imbalance reconcilable by a purchase from an LP of the currency in shortfall.

[0036] According to another aspect of the invention, optimal pricing for currency exchange is determined by comparison with available alternatives from the financial and economic data source. According to another aspect of the invention, when the SPs are plural, exchange rate pricing is further minimized by minimizing the imbalance in netted exchange requests across users.

[0037] According to another aspect of the invention, exchange pricing is further minimized by maximizing the total quantities in each batch, with preference given to E-money expected to remain on the platform longer (i.e., with higher time-value).

[0038] According to another aspect of the invention, the exchange pricing algorithm is run recursively until a profit goal is reached.

[0039] According to another aspect of the invention, the algorithm operates recursively until the SPs have zero-cost transactions.

[0040] According to another aspect of the invention, a CNN is trained on supply and demand for currency pairs using data from the economic source.

[0041] According to another aspect of the invention, the CNN is trained on currency prices relative to one another over various time periods.

[0042] According to another aspect of the invention, the CNN is trained on the volume of currency exchanges at specific prices within given timeframes.

[0043] According to another aspect of the invention, the CNN is trained on the number, currency pair, and margin of arbitrage gaps across different timeframes.

[0044] According to another aspect of the invention, the CNN is trained on a number of technical indicators relevant to global currency markets. According to another aspect of the invention, the CNN is trained on SP behaviour across transaction patterns, personal data, trading history, and account activity.

[0045] According to another aspect of the invention, the CNN forecasts supply-demand gaps in currency pairs to optimize offers made to LPs.

[0046] According to another aspect of the invention, an LLM pre-trained with real-time financial data assesses market sentiment for the behavioural prediction layer.

[0047] According to another aspect of the invention, the financial and economic data include: global and local economic trends, central bank policies, financial exchange reserves, unemployment rates, purchasing managers’ index, GDP, CPI, interest rates, public debt, current account balance, portfolio investment, FDI as a percentage of GDP, real effective exchange rate, and short-term external debt as a percentage of FX reserves.

[0048] According to another aspect of the invention, the system includes: (a) a cohort-assignment engine configured to allocate each user to a group neural network whose parameters match that user's initial or inferred profile; and (b) a training controller configured to collect usage data and retrain or update both group-level and personalized neural networks. The platform adjusts, in real time, financial parameters such as bid-ask spread, interest rate, or liquidityretention window to optimize outcomes for both individuals and user groups.

[0049] According to another aspect of the invention, personalized neural networks are provided for each user via a CNN. These models are trained or updated based on user-specific behaviours and parameters to predict future time-value, which includes: (1) a follow-up transaction such as a disbursement following a currency exchange, and / or (2) the value derived from E-money's retention period (“E-money Duration Value”). According to another aspect of the invention, the platform executes a dynamic pricing algorithm that, in real time: (a) receives inputs including (i) outputs from group or personalized prediction models and (ii) global market data; (b) determines a profitmaximizing bid-ask spread under a configurable constraint, with default constraints being a maximum spread ceiling of zero and no minimum floor; (c) uses linear regression or equivalent statistical techniques to find the spread that meets a predefined profit target; and (d) allocates any realized profit among users based on a fairness rule selected from pro-rata time-value, equal-share, or capped-reward schemes stored in memory.

[0050] According to another aspect of the invention, the system includes a zero-data regime neural network module that: (a) initializes with a shallow architecture and minimal features, using ReLU activation and linear output; (b) generates synthetic training samples and selfsupervised targets when real data are unavailable, expanding the network when validation error exceeds a threshold; and (c) predicts, under negative-margin scenarios, the ratio of deposited or converted funds to the applied spread. It avoids overfitting using Softmax or Sigmoid activations with appropriate loss functions such as Categorical Cross-Entropy.

[0051] BRIEF DESCRIPTION OF THE FIGURES:

[0052] FIG. 1 constitutes a diagram reflecting market failure associated with SPs, for which the computing system of the present invention teaches a solution.

[0053] FIG. 2 constitutes a three-dimensional diagram, integrating Time as a third dimension to a price vs quantity diagram, thereby illustrating the cross-influence among these three dimensions. FIG. 3 constitutes table detailing the configuration of different use cases of the present invention, according to some embodiments.

[0054] FIG. 4 constitutes an overview of a currency exchange network connected by a computing system, according to some embodiments of the invention.

[0055] DETAILED DESCRIPTION OF SOME EMBODIMENTS

[0056] In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will be understood by those skilled in the art that the present invention may be practiced without these specific details. In other instances, well-known methods, procedures, and components, modules, units and / or circuits have not been descried in detail so as not to obscure the invention. Some features or elements described with respect to one embodiment may be combined with features or elements described with respect to other embodiments. For the sake of clarity, discussion of same or similar features or elements may not be repeated.

[0057] Unless explicitly stated, the method embodiments described herein are not constrained to a particular order or sequence. Additionally, some of the described method embodiments or elements thereof can occur or be performed simultaneously, at the same point in time, or concurrently.

[0058] The present invention teaches a financial technology system implemented as a computing system configured to dynamically designed to configure financial environments toward any desired outcome regarding user and / or service provider costs or gains. This technological configuration enables financial service providers to optimize their objectives: whether to maximize revenues, enhance user offerings, or achieve a combination of both.

[0059] Building on the needs described in the background of the present application, the following section presents the core computational architecture and technological foundation of the present invention, which teaches a multi-layer computing system specifically optimized for financial operations. This framework underpins all applicable use cases, offering a unified system approach to predictive modeling, Scenario simulation, and aggregate optimization across different financial environments.

[0060] Below is a description of the invention’s architectural design, which serves as the foundation for all applicable use cases. The system operates using the following multilayer computational architecture:

[0061] Important note: The terms below may, in some cases in this application, be described with slight variations depending on context.

[0062] The term “User A ”, as used herein, refers to a party with a defined financial position or need, typically acting as a resource or rights provider. This may include data contributors, asset providers, currency sellers, depositors, or lenders.

[0063] The term “User B”, as used herein, refers to a counterparty to User A, with either a complementary or opposing financial position or need, in whole or in part.

[0064] The term “Case ”, as used herein, refers to an elementary financial unit representing either a transaction, a static financial state, an individual user, or a defined group of comparable instances. The term “Scenario ”, as used herein, refers to a simulated variant of a Case, produced by altering one or more system-controlled parameters, used to project behavioral outcomes and assess financial performance under varied conditions.

[0065] The term “small players (SPs) ”, as used herein, refers to low-volume market participants (e.g., individual consumers or SMBs), such as User A or User B. In this application, SPs may also be referred to as LTs (Liquidity Takers).

[0066] The term “larger players (LPs) ”, as used herein, refers to high-volume agents, also known as Liquidity Providers, who operate in highly competitive environments and thereby access transactions at near- zero, zero, or even negative costs. The term “E-Money”, as used herein, refers to digitally held funds or financial rights deposited by users onto a financial platform for future use or transaction.

[0067] The term “E-money Duration \ Value ”, as used herein refers to the economic benefit a platform gains by retaining E-money over time, typically increasing with the retention duration. The term “time”, as used herein with respect to the Third Element, refers to economic value stemming from future actions associated with a Case. It typically includes: (1) a follow-up transaction (e.g., a disbursement act using a payment card following a currency exchange), and / or (2) the value derived from the E-money’s retention period ( “E- money Duration Value”).

[0068] The term “Features”, as used herein, refers to inputs required to perform behavioral forecasting and scenario simulations. Features may be derived from multiple data domains, including but not limited to: user behavior within the platform, temporal and contextual parameters, external economic indicators, market conditions, identity-related signals, and other information sources that are technically available to the system. In some embodiments, the feature set may also include dynamic combinations or interactions among multiple categories of inputs.

[0069] The term “Pricing Algorithm ”, as used herein, refers to system component that integrates behavioral predictions, scenario simulations, and optimization logic to determine optimal pricing across Cases based on defined objectives.

[0070] According to some embodiments of the invention, a computing system connects a plurality of nodes representing: at least one User A (a party with a certain financial position or need); Zero or, at least one User B (a counterparty with a complementary or opposing financial position or need); and at least one Feature e.g., source of personal, financial or economic data. According to some embodiments of the invention, a platform hosted on the computing system provides a means for at least one User A to deposit funds or assets, which are represented as digital credits (E-money or equivalent) available for use after a fixed or variable period, during which time a first form of value or yield is accumulated. A second form of value or yield is accumulated over any unfixed period in which the assets remain on the platform.

[0071] According to some embodiments of the invention, the platform operates by netting requests from multiple users (User A and / or User B) for a particular type of financial exchange or transaction, according to parameters derived from the at least one source of financial and economic data, and presents the resulting terms transparently and equivalently to all parties involved. According to some embodiments of the invention, any volume or balance differences resulting from the netting of these requests are reconciled by engaging at least one liquidity provider or external entity, with any associated transaction costs accounted for by the accumulated yields or value streams (first and second) on the platform. These netted transactions are executed in optimized batches.

[0072] Persons skilled in the art will recognize that the term “net” used as a verb in this context refers to the collation and aggregation of requests or obligations across a multi-party network, producing a “many-to-many” transactional structure, as opposed to traditional one-to-one (P2P) interactions. According to some embodiments of the invention, the system receives a reference midpoint value (such as an exchange rate, interest rate, or benchmark condition) from at least one external source of financial and economic data, and applies this reference as the standardized condition, benchmark or rate offered to at least one user participating in a batch transaction, ensuring transparency and parity between both sides of the transaction pair.

[0073] According to some embodiments of the invention, funds deposited as electronic or digital value on the platform belong directly to the participating users (User A and / or User B), without requiring’ or minimizing, the use of intermediary or custodial accounts such as nostro or vostro accounts, or equivalent holding mechanisms.

[0074] According to some embodiments of the invention, netting of SP currency exchange requests is optimized per exchange batch by predicting for an imbalance reconcilable by a purchase from an LP of a currency in shortfall. In this way the present invention optimizes for minimum premiums on exchanges with LPs. According to some embodiments of the invention, optimal pricing for the currency exchange is determined by comparison with available alternatives as provided by the source of financial and economic data.

[0075] According to some embodiments of the invention, pricing for exchanges is further minimized by minimizing the imbalance of currency exchange requests netted together. By minimizing this imbalance, the volume required for reconciliation from an LP, and thus the transaction cost thereby incurred, are also minimized.

[0076] According to some embodiments of the invention, pricing for exchanges is further minimized by maximizing the overall quantities in each exchange in each batch, with a preference for E-money expected to remain on the platform for the longest period. The longer E-money remains on the platform, the greater level of interest it can earn, which allows for the reconciliation of premiums paid to LPs for currency exchange imbalance reconciliations.

[0077] According to some embodiments of the invention, the algorithm by which pricing exchanges is minimized is run recursively until a profit goal is reached.

[0078] According to some embodiments of the invention, the algorithm by which pricing exchanges is minimized is run recursively until SPs have zero cost transactions.

[0079] According to some embodiments of the invention, a computational neural network (CNN) model is trained on supply and demand of different currency pairs using information gleaned from the source of financial and economic data. According to some embodiments of the invention, the CNN model is trained on the prices of currencies relative to one another within periods of a variety of certain lengths.

[0080] According to some embodiments of the invention, the CNN model is trained on the volume of currency exchanges at a certain price within periods of a variety of certain lengths.

[0081] According to some embodiments of the invention, the CNN model is trained on the number, currency pair, and margin of different arbitrage gaps within periods of a variety of certain lengths.

[0082] According to some embodiments of the invention, the CNN model is trained on a number of technical indicators relating to the global currency market.

[0083] According to some embodiments of the invention, the CNN model is trained on the currency exchange behavior of SPs on the platform, integrating data relating to transaction patterns, trading history, account activity, and registration.

[0084] According to some embodiments of the invention, the CNN model is operated to forecast supply-demand gaps in currency pairs and thereby to optimize offers made to LPs.

[0085] According to some embodiments of the invention, a Large Language Model (LLM) is pretrained with real-time data gleaned from the source of financial and economic data and operated to gauge market sentiment.

[0086] According to some embodiments of the invention, data gleaned from the source of financial and economic data includes data relating to: global economic trends; local economic trends; central bank policies; financial exchange reserves; unemployment rates; purchasing managers’ index; gross domestic products; consumer price index; interest rates; public debt; current account; portfolio investment; foreign direct investment as a percentage of gross domestic product; real effective exchange rate; and short term external debt as a percentage of foreign exchange reserves.

[0087] It is an object of the invention to provide a means of currency exchange to liquidity takers that approaches, if not arrives at, a state of zero transactional cost. It is further an object of the invention to optimize currency exchanges with respect to the most up to date financial data.

[0088] According to some embodiments of the invention, the platform provides personalized neural networks for each user, tailored based on the specific behaviors and parameters unique to each individual. A first network predicts the duration that funds will remain in the user's e- wallet, while a second network forecasts the user's distribution patterns.

[0089] According to some embodiments of the invention, users are categorized into groups based on learned parameters, ensuring that new or unknown users are assigned to the group that best fits their profile. Furthermore, the platform continuously collects data, learns, and updates both the group networks and the individual networks of each user. In this way, the present invention ensures that each user benefits from an optimized and continuously updated network, providing the best possible spread for both the user and the platform.

[0090] From a technological standpoint, the present invention offers operators the ability to integrate several models, including Cross Validation, Linear and Polynomial Regression, Random Forest, XGBoost, Support Vector Regression, Transformers for Time Series

[0091] Forecasting, and Neural Networks. According to some embodiments of the invention, a unique third network is introduced to predict the ratio of the amount of money in relation to spreads. This network lacks real data for training, as there is no existing information on how much money will be converted when margins are negative from the converter's perspective. As a result, several adjustments are made to the network being developed.

[0092] Selecting the number of layers for this network is critical for its accuracy. The present invention enables the operation of a process commencing with a simple shallow network that includes one hidden layer, utilizing a Rectified Linear Unit (ReLU) as the activation function in the hidden layer and a linear function in the output layer. The approach is iterative and based on trial and error; should the initial results prove inadequate, additional layers may be added, and if necessary, a transition to a deep network with multiple hidden layers is considered. The absence of historical data necessitates a cautious approach, and to prevent overfitting, the activation functions may be tailored for multi-class classification problems, potentially integrating a Softmax or Sigmoid function, combined with Categorical Cross-Entropy as the loss function.

[0093] In this way, the present invention provides a robust and adaptable neural network system that optimizes predictive accuracy and user experience, even in scenarios lacking historical data.

[0094] According to some embodiments of the invention, a Dynamic Pricing Algorithm is provided for calculating the spreads per user and for groups of users. This real-time algorithm receives a variable number of parameters, including outputs from user predictions and global data, and computes the maximum profit subject to the spread's constraints. By default, these constraints may have a maximum of zero and no minimum, but both values can be set to any other definitions. The algorithm utilizes linear regression techniques to perform these calculations.

[0095] Furthermore, the algorithm can receive a desired profit target and calculate the optimal spreads required to achieve this profit, while balancing and distributing the profits among users. In this way, the present invention optimizes the pricing strategy to maximize profitability while adhering to predefined constraints and ensuring fair distribution of profits across the user base.

[0096] According to some embodiments of the present invention, three computing layers are integrated into the computing system:

[0097] 1. Behavioral Prediction Layer

[0098] For each basic financial unit (referred to as a “case ”) - which may represent a transaction, a static state, a user, or a defined group of similar instances - the system predicts the probability distribution of subsequent user behavior. This includes the expected timing, nature, and method of a follow-up action, or alternatively, the likelihood of no action at all.

[0099] The system integrates behavioral data sources and applies predictive computational models (including Al or machine learning models) to make these determinations.

[0100] Building on the predictive architecture described above, the system may utilize a broad set of input Features in order to perform behavioral forecasting and scenario simulations. These Features may be derived from multiple data domains, including but not limited to: user behavior within the platform, temporal and contextual parameters, external economic indicators, market conditions, identity-related signals, and other information sources that are technically available to the system. In some embodiments, the feature set may also include dynamic combinations or interactions among multiple categories of inputs. Without limiting the generality of the invention, these Features allow the system to characterize and forecast each Case with improved accuracy while maintaining adaptability across a wide range of financial environments

[0101] 2. Counterfactual Simulation Model (“CSM”) Layer

[0102] For every case, the system generates multiple hypothetical “scenarios ” by modifying one or more controllable baseline conditions - such as pricing, incentives, or timing mechanisms - within the service provider's influence. The behavioral prediction process from Layer 1 is applied to each scenario, enabling systematic comparison of potential user behaviors under alternative conditions. This CSM is a key innovation over static or isolated Scenario modeling, requiring sophisticated data handling, dynamic parameter adjustments, and cross-case modeling.

[0103] 3. Aggregate Optimization Laver

[0104] Across a defined set of cases (e.g., all relevant events within a financial institution over a specified period), the system evaluates multiple Scenario combinations — each representing a specific configuration of controllable baseline conditions applied across the case set. It then identifies the precise combination of scenarios, selecting one Scenario per case, that collectively yields the best aggregate outcome relative to the institution’s defined objectives and constraints.

[0105] This aggregate optimization requires balancing trade-offs across the full case set, accounting for interdependencies and opposing effects — such as optimizing cost- reduction strategies in one segment to support profit-maximization strategies in another, all while satisfying internal and external constraints (e.g., zero-cost requirements, regulatory thresholds, or user-specific limitations).

[0106] This architectural approach represents a departure from conventional static or siloed computational methods, offering a unified, dynamic system for institution-wide optimization.

[0107] Illustrative Example: Currency Exchange Environment

[0108] The following example illustrates a leading application of the invention, specifically within the field of currency exchange markets where the present system’s technological framework is concretely applied to computationally resolve known inefficiencies.

[0109] Information Asymmetry

[0110] The currency market, where currencies are exchanged in extremely high volumes, is generally considered the largest market in the world. High-volume agents in this market, referred to herein as “Large Players” (LPs), can exchange currencies at negligible costs because they have the capability, or the potential - to transact directly with other LPs across various established currency exchange trading forums.

[0111] These trading forums constitute platforms that facilitate near-perfect competition, where many large and willing buyers and sellers interact in an environment with minimal barriers or obstacles that could otherwise trigger market failures. In such settings, prices converge closely to equilibrium - represented by the mid-point or mid-market exchange rate - reflecting the fair market value. The term “Large Players (LPs) ” refers to such large financial agents capable of accessing near-zero-cost, zero, or sometimes even negative currency exchanges.

[0112] Unfortunately, this is not the case for smaller financial agents engaged in lower-volume currency exchanges, including virtually all private consumers and most small and midsize businesses. These agents, referred to herein as “Small Players” (SPs), lack the capital and access required to engage in market operations similar to those of LPs.

[0113] While LPs enjoy the efficiencies of near-perfect market dynamics, SPs are limited to accessing currency exchange services through quasi-retail channels - such as foreign exchange booths, ATMs, banks, credit card companies, or online platforms - all operating under the familiar “buy low, sell high” model. This model inevitably leads to the well- known “spread” phenomenon, where the difference between the purchase and sale price translates into an embedded cost for the SP. Typically, such intermediated exchanges involve fees ranging from approximately 1% to 3.5% of the exchanged amount. In general, the smaller the transaction, the less bargaining power the SP holds - and the higher the relative cost they are likely to pay.

[0114] It is precisely in addressing this inefficiency that the present invention offers its technological contribution: through its computational system architecture, the invention enables SPs to access dynamic, real-time optimized pricing configurations that were previously only achievable by LPs, thereby providing a system-based technical solution to the described market failure.

[0115] This phenomenon creates a situation in which prices consistently deviate from equilibrium (i.e., the mid-point exchange rate) in the largest market in the world - a condition that can rightly be regarded as a “market failure ” in the conventional economic sense. The inventive system disclosed herein introduces a computing solution that algorithmically minimizes such deviations, going beyond theoretical descriptions of the market and providing an actionable technological framework. This market failure limits SPs’ ability to compete effectively with LPs, constraining the scale of currency exchanges and fundamentally narrowing the scope of inter-currency trade in goods and services. While this market failure persists, the global economy, and especially its smaller components - remains constrained to the trade activities dominated by LPs, representing only a subset of the full economic potential.

[0116] A diagram reflecting this market failure is shown in FIG. 1.:

[0117] As can be seen in FIG. 1, SPs cannot achieve the competitive advantage afforded to LPs in currency exchanges because they operate within an asymmetric trading environment. The exchange services available to SPs are ultimately provided, or underwritten - by one or more foreign exchange shops, banks, credit card companies, or other financial entities, all of which effectively operate as LPs themselves.

[0118] This structural asymmetry creates the persistent market failure illustrated in FIG. 1 - a phenomenon not unique to the currency exchange market. More broadly, market failures frequently arise in asymmetric trading environments, particularly when driven by disparities in the levels of information available to each side, a condition commonly known as “Information Asymmetry

[0119] The term “Information Asymmetry, ” as used herein, refers to a situation in which one party to a transaction or relationship possesses materially better or more relevant information than the other party. Because the less informed side cannot fully observe or verify what the better-informed side knows, this imbalance distorts decisions and pricing, undermining the efficiency and fairness of the market. The present invention addresses this problem not merely at a theoretical or economic level but through a concrete computational architecture, offering a technological system designed to reduce Information Asymmetry via predictive modeling, real-time adjustments, and optimized configuration..

[0120] In currency exchange markets, a pronounced Information Asymmetry exists between users / customers (who are predominantly SPs) and the financial entities (generally LPs) with which they transact. The financial entities always possess superior knowledge — enabling them to consistently extract profit (i.e., secure rates more favorable than the midpoint exchange rate), while SPs systematically receive inferior rates. Ultimately, Information Asymmetry produces a persistent and systemic gap in relative exchange rates between LPs and SPs, a gap that the invention’s computational system is specifically engineered to detect, model, and close through its technical processes.

[0121] The asymmetric trading environment that drives this market failure is fundamentally rooted in the presence of an intermediary between the two sides — a factor that itself creates and sustains the Information Asymmetry in the system.

[0122] In the LP-to-LP trading environment, large players transact directly with one another under near-perfect competitive conditions. They can transparently observe and adjust prices, negotiate (primarily through price mechanisms) based on real-time market dynamics, and thereby access equilibrium pricing (the mid-point exchange rate) without interference or distortion. In stark contrast, SPs are forced to interact through financial intermediaries such as foreign exchange shops, banks, or service providers, who control both the user-facing interface and the access to underlying market mechanisms. This structural position grants intermediaries privileged knowledge, not only about general market conditions, but also about the specific intentions, needs, and constraints of the SPs themselves.

[0123] Here, the present invention’s technical contribution is decisive: by leveraging algorithmic tools and computational models, it bypasses the need for SPs to independently access the same informational landscape as LPs. Instead, the system provides a technical bridge, modeling and reconfiguring the pricing environment in real time to align SP outcomes more closely with equilibrium values.

[0124] Crucially, it is this very intermediation layer that generates the Information Asymmetry at the heart of the market failure. By acting as the sole gateway between SPs and the broader market, intermediaries effectively control the flow of information, creating a persistent imbalance where SPs cannot access or leverage market knowledge on equal terms. Even if broader market data were theoretically available, the intermediary’s filtering role inherently distorts the relationship, maintaining an environment where supply and demand no longer meaningfully influence pricing for SPs.

[0125] Thus, the core problem in SP currency exchanges is not merely the presence of spread fees or operational inefficiencies - it is the systematic distortion introduced by the intermediary’s position, which the invention’s system is designed to computationally neutralize, through an automated optimization framework that reconstructs equilibriumbased pricing and conditions. Asymmetry in currency exchange is directly linked to the relationship between price and quantity. According to well-established supply and demand theory, the quantities supplied and demanded are the primary determinants of price.

[0126] In the case of an SP transacting through a financial intermediary, the intermediary holds precise knowledge of the SP’s needs (in terms of quantities demanded), whereas the SP has no access to comparable information about the intermediary’s available inventory or sellable quantities. The technical system described here is specifically architected to model these hidden variables computationally, integrating them into a predictive and optimization engine that enables the SP to effectively simulate market-level access. If SPs, acting collectively, were able to access detailed information about the intermediary’s exact demands and holdings in each transaction, this would, by definition, establish a state of Information Symmetry - enabling true economic balance.

[0127] However, under current market conditions and using conventional currency exchange tools, achieving such symmetry is impossible. While financial institutions do actively compete to reduce transaction costs for their customers, this visible competition remains bounded by the underlying structural market failures described above, continuing to impose excessive costs on SPs.

[0128] Thus, a clear and unmet need remains in the market: a technological solution capable of overcoming the inherent competitive advantage that financial institutions hold when exchanging currencies with SPs. The present invention fills this unmet need by providing a system-based, technical approach that transforms asymmetric environments into computationally optimized systems, ensuring that SPs can access transactional conditions that approach those found in the fully competitive LP-to-LP market.

[0129] The “Third Element”

[0130] Resolving Information Asymmetry and the resulting market failure can elevate SPs’ position in the market to match that of LPs in terms of competitive conditions. This correction alone could reduce deviations from equilibrium for SPs, lowering transaction costs from 1 %-3.5% to near zero, as currently enjoyed by LPs.

[0131] However, while this rebalancing would represent an important improvement, it would still fall short of establishing a fully optimized and economically balanced pricing structure for SPs. Achieving true and definitive economic balance in financial markets, including but not limited to currency exchange - a multi-layered technical approach, as disclosed herein, which systematically integrates price, quantity, and also Time parameters through computational modeling and optimization

[0132] As illustrated in FIG. 2, supply and demand curves are traditionally plotted in a two- dimensional coordinate system, with axes representing price and quantity. Yet this framework is fundamentally incomplete in financial markets, particularly in the currency exchange space.

[0133] The currency exchange market is unique because it represents what can be described as a “non-final” or chronologically open market. Unlike most other markets, where the traded good has inherent value and the transaction is self-contained, in currency exchange, money itself holds no intrinsic value beyond its purchasing power. Its true economic meaning is only realized in what can be acquired with it afterward. As a result, a currency exchange transaction is almost always chronologically prior to another future transaction(s) - whether a purchase, an investment, a transfer, or a reverse currency conversion. For the financial institution or service provider facilitating the exchange, this creates two distinct economic sources of value: (1) the waiting period during which the funds remain under their control, generating potential income, (herein referred to as “Money Duration”) and (2) the subsequent transactions themselves, which become additional revenue-generating events.

[0134] Therefore, unlike most other markets, financial markets inherently operate in three dimensions, where Price-Quantity-Time are interdependent and mutually influential. The elapsed time until the next economic action directly affects the overall outcome for the party holding the funds - making time an inseparable part of the optimal pricing calculation.

[0135] Critically, addressing this temporal dimension is not merely an economic observation but forms part of the technical challenge that the present invention is specifically designed to solve

[0136] This dynamic is captured in FIG. 2, where the “Absolute Equilibrium” represents a moving point, effectively a three-dimensional “pixel” - that continuously shifts as supply and demand evolve and as the mid-point exchange rate (an exogenous parameter determined in external LP markets) fluctuates.

[0137] To fully address the complexities of financial pricing systems, one must account for the combined effects of three interacting dimensions: price, quantity, and time. Such a task surpasses manual analysis or static modeling — it requires a sophisticated computational framework capable of integrating these dynamic factors in real time. This creates a clear computational challenge: how to determine, in real time and under changing market conditions, the exact location of the Absolute Equilibrium - the point in the three-dimensional Price-Quantity-Time space that reflects the optimal currency exchange prices for any given situation.

[0138] The present invention provides a concrete technological solution to this challenge: an advanced Al-based computational model running on a computer system. This model forecasts each predefined “case” (as per Layer #1: Behavioral Prediction), simulates all possible “scenarios” for each such case (as per Layer #2: Counterfactual Simulation), and integrates these inputs into an optimization engine (as per Layer #3: Aggregate Optimization). This is not a theoretical or purely economic mechanism, but a technically engineered system implementing algorithmic processes, predictive analytics, and optimization logic at machine scale. Together, the engine and its associated parameters - collectively referred to as the “Pricing Algorithm ” - form the core technological innovation disclosed in this application. This invention enables financial institutions to use pricing dynamics, embedded within an automated computational system, in order to configure their entire business so they can achieve exactly their desired goals of profitability, user cost reduction, or a combination of both.

[0139] Generalization & Additional Use-Cases

[0140] While the currency exchange environment provides the most detailed and fully elaborated example of the invention’s capabilities, it is by no means the only applicable domain. The technological architecture and core principles disclosed herein are inherently generalizable, enabling their adaptation to a wide range of additional financial use cases. Importantly, this generalizability arises not from abstract business logic but from the modular, computational nature of the system, which allows its algorithmic layers to be parameterized and repurposed across domains. By extending the same foundational system — combining behavioral prediction, counterfactual simulation, and aggregate optimization - institutions can configure diverse financial environments to meet their specific goals, whether related to profitability, cost reduction, efficiency, or strategic balance.

[0141] Hence, beyond currency exchange, core financial challenges such as liquidity management, interest management, and many other financial issues that are managed by a financial institution acting as a “middle man” between users with opposing needs - all can be configured using this invention. This is possible because they share not only economic similarities but a common technical structure of predictive data handling, computational intermediation, and optimization processes, as implemented by the invention’s system. This table shown in FIG. 3 serves as a bridge between the domain-specific examples and the generalized framework of the invention, summarizing the similarities between various use cases and generalizing the definitions of the involved parties for the sake of the present invention , taught herein as a configuration model for a range of different financial computing operations..

[0142] Below is a three point discussion of important features of the table shown in FIG. 3.

[0143] 1. Across all use cases, the solution’s architecture is designed for general applicability, allowing it to adapt seamlessly to the distinct players, variables, and optimization goals of each financial environment. This is achieved through the invention’s modular computational architecture, which enables parameterization, dynamic recalibration, and reconfiguration without needing to redesign or manually recode system logic. While the specific context and definitions may vary across domains, the underlying technological framework remains universally relevant and applicable. User A and User B always represent users who have counter, or partial counter needs (e.g. User A wants to sell USD and buy Euros and User B wants to sell Euros and buy USD). The system treats these user definitions not just as abstract roles but as structured data entities processed by the computational layers, allowing the system to model, simulate, and optimize multi-party interactions at scale. Example: Liquidity Management Use-Case

[0144] Consider a situation where User A holds a positive balance (credit) in their current account, while User B holds a negative balance (overdraft) in theirs. In Layer 1 (Behavioral Prediction), the system forecasts when and how each user is likely to act - specifically, when and how User A might withdraw or reallocate funds and when User B is expected to repay or increase their borrowing. This prediction process is not based on manual estimations but is performed by predictive algorithms within the system, leveraging real-time data streams and historical behavioral models. In Layer 2 (Counterf actual Simulation), the system generates alternative scenarios for each user by varying controllable pricing conditions. For User A, it simulates possible combinations of interest rates on the checking account versus a fixed-term deposit over the predicted holding period; for User B, it simulates the impact of different borrowing rates and credit terms. The system then predicts the behavior in each hypothetical pricing configuration. These simulations are algorithmically generated and computed, allowing systematic evaluation of how technical parameter changes (not just business rules) affect user-level outcomes.

[0145] In Layer 3 (Aggregate Optimization), the system identifies the optimal overall configuration - balancing not just the isolated outcomes for User A or User B, but also the cross-influences between them. For example, the system might optimize for the institution’s overall profitability, or for maximizing institutional gains under the constraint that User B pays no more than a fixed (or even zero) cost on the borrowed amount. This optimization is performed through computational solvers embedded in the system, integrating multi-variable inputs and constraints into actionable output configurations, rather than relying on human-led or static optimization approaches.

[0146] The above discussion demonstrates how the invention’s generalized architecture and systematic approach can be applied across a spectrum of financial contexts, each with its own distinct variables, user dynamics, and strategic goals. Importantly, this adaptability is not incidental or theoretical but arises directly from the invention’s core computational design, which enables institutions to systematically predict user behavior, configure financial environments through software-controlled parameters, and optimize outcomes via integrated algorithmic processing.

[0147] The Pricing Algorithm, serving as part of the process operated by the a “computation system ”, refers to any system comprising one or more processors and one or more memory units configured to execute instructions stored in the memory to perform computational tasks. The computing system may include a variety of components such as a central processing unit (CPU), graphics processing unit (GPU), memory (e.g., RAM, ROM, flash memory), storage devices, communication interfaces, input / output devices, and may be distributed across multiple physical devices or nodes. A computing system may be implemented as a server, desktop, laptop, mobile device, embedded system, or a combination thereof. The computing system may operate autonomously or in conjunction with other computing systems via a network connection.

[0148] According to some embodiments, the Time Value of E-money (such as within an E-wallet, bank account, or other balance -holding layer of the platform shown in FIG. 4) is computed using a convolutional neural network (CNN) model integrated into the Pricing Algorithm. This model analyzes time-dependent behavioral patterns, macroeconomic context, and transaction history to forecast which SPs are expected to generate the highest Time Value - either by holding funds longer or by triggering profitable downstream events (e.g., purchases, conversions, or fund reallocations).

[0149] Reference is made to FIG. 4, which constitutes an overview of a currency exchange network connected by a computing system, according to some embodiments of the invention, thereby illustrating an embodiment of the invention as applied to a currency exchange environment. The diagram presents a schematic view of the system’s pricing algorithm and its layered architecture, as implemented within a platform connected to SPs, LPs, and external data sources.

[0150] The diagram shown in FIG. 4 is organized from left to right, visually tracing the flow from data ingestion through to pricing outputs generated by the computing system:

[0151] On the left side, multiple data sources feed into the system. These include internal user data (e.g., user identity, transaction history, behavioral signals), external financial and macroeconomic data, and market sentiment indicators. These sources form the raw inputs for predictive processing.

[0152] In the middle section, the three-layer pricing algorithm is depicted:

[0153] Layer 1: Behavioral Prediction uses Al models (e.g., CNNs, LLMs) to classify user cases and predict behavioral outcomes, such as expected fund retention duration or likelihood of follow-up transactions.

[0154] Layer 2: Counterfactual Simulation generates hypothetical pricing scenarios for each case by adjusting parameters such as spread, discount, or expected hold time, and simulates user responses.

[0155] Layer 3: Aggregate Optimization integrates the simulation results and selects the configuration that optimizes platform-level goals such as profit, SP acquisition cost, or fund duration efficiency.

[0156] On the right side of FIG. 4, the output of the algorithm is presented as optimized pricing actions, such as differentiated exchange rates for SPs based on their predicted value. This includes specific batch-level pricing instructions or user-specific discounting tied to expected economic contribution.

[0157] Overall, the diagram shown in FIG. 4 demonstrates how the system computationally integrates real-world financial data, user behavior, and simulation-based forecasting to generate pricing in the currency exchange domain that is both dynamic and optimized across multiple objectives. As shown in FIG. 4, the financial platform includes a centralized computing system that connects to both SPs and LPs, while interfacing with external sources of financial and economic data. These inputs support the CNN model and a complementary large language model (LLM), which jointly process structured and unstructured data (e.g., sentiment signals, digital signatures, user intent) to evaluate each SP's expected economic contribution.

[0158] According to some embodiments, discount limits are dynamically determined based on regulatory frameworks (e.g., EMI or PSD2 constraints), liquidity coverage mechanisms (e.g., nostro funding buffers), institutional pricing policies, and operational performance metrics - such as prior batch profitability and prediction accuracy. The Pricing Algorithm leverages this input space to assign individualized pricing discounts to each SP, prioritizing capital efficiency and profitability for the platform as a whole.

[0159] This predictive pricing logic operates as part of a broader layered system architecture (described in prior sections), enabling adaptive reconfiguration of financial conditions in real time. These capabilities go beyond conventional rules-based pricing by continuously learning from user behavior, market signals, and prior system performance - illustrating how the invention uses machine-implemented logic to neutralize structural inefficiencies and bring SPs closer to equilibrium pricing, similar to that achieved in LP-to-LP transactions.

[0160] Although the embodiments above describe a use case within currency exchange systems, the invention’s core structure - comprising layered prediction, scenario simulation, and real-time optimization - can be extended across financial domains. The appended claims are therefore intended to cover all computational configurations that preserve the invention’s core functional architecture and technical logic.

Claims

CLAIMS1. A computing system comprising: at least one processor; at least one hardware accelerator component; at least one non-volatile memory source; at least one network interface; at least one source of financial and economic data; and at least one message fabric, where said computing system hosts a financial operations platform comprising three layers: a. a behavioral prediction layer configured to predict the distribution of behaviors for at least one case, wherein each case is associated with at least one baseline condition defined by the at least one source of financial and economic data; b. a counterfactual simulation model layer configured to generate a plurality of scenarios based on modifications to the at least one baseline condition; c. an aggregate optimization layer configured to computationally evaluate the said plurality of scenarios, and therefrom to select an optimal scenario for each case according to predetermined incentives or constraints, wherein a computing system determines optimal scenarios for cases by the computational evaluation of a plurality of scenarios generated from a predicted distribution of behaviors.

2. The computing system of claim 1, wherein the at least one non-volatile memory source comprises: at least one columnar case-store; a plurality of case records, wherein each case record comprises: a user identifier; an asset descriptor; a statevector relating to past behavior derived from the at least one source of financial and economic data; and a range of timestamps.

3. The computing system of claim 2, wherein a machine learning model is operated by the hardware accelerator in batch-processing mode, utilizing the hardware accelerator’s on-device memory and parallel-execution lanes, to compute a timevalue metric for each case record.

4. The computing system of claim 2, wherein for each case-record, a plurality of Scenarios are generated in the counter factual simulation layer by enqueuing parameter sets into the message-queue fabric, each scenario modifying at least one controllable parameter selected from: spread; interest rate; fee; retention window; and settlement window.

5. The computing system of claim 1, wherein the aggregate optimization layer operates the hardware accelerator, and wherein adjacent scenarios are batched together.

6. The computing system of claim 1, wherein the aggregate optimization layer operates an optimization solution process on the at least one processor, and wherein said process is configured to terminate within a real-time budget defined by a system-configuration parameter.

7. The computing system of claim 1, wherein the aggregate optimization layer commits the optimal scenario to an immutable event log within the at least one non-volatile memory source, and wherein instructions associated with said scenario are generated and published on the message fabric.

8. The computing system of claim 2, wherein the asset descriptor denotes at least two currencies, and the financial operations platform is further configured to batch opposing exchange requests into bounded-time windows, referencing an external interbank rate derived from the at least one source of financial and economic data.

9. The computing system of claim 2, wherein each case-record represents a currentaccount balance wherein the at least one baseline condition includes debit interest and credit interest parameters, and wherein the aggregate optimization layer determines an optimal scenario that maintains a liquidity coverage ratio above a pre-determined policy threshold, wherein associated overdraft exposure is balanced by time-value generated by deposit balances.

10. The computing system of claim 2, wherein the asset descriptor denotes an interestbearing product selected from: fixed-term deposit; flexible savings product; hybrid instrument, and wherein the counter factual simulation layer adjusts tenor and rate grids, the behavioral prediction layer estimates early-withdrawal probability; and the aggregate optimization layer maximizes net present value subject to a predefined customer-churn constraint.

1. The computing system of claim 1 , wherein the financial operations platform further comprises a at least one Convolutional Neural Network (CNN) and a at least one Large Language Model (LLM), wherein:(a) the CNN model receives structured numerical data including, but not limited to, user-level financial behaviors, transaction history, economic indicators, and platform-level aggregate trends, and outputs feature embeddings representing behavioral and temporal patterns relevant to each Case;(b) the LLM processes unstructured textual and semi-structured data including news reports, financial statements, user-generated inputs, or regulatory publications, and extracts latent features relevant for prediction and scenario configuration;(c) the output of the CNN and LLM models are jointly processed by an optimization engine that simulates multiple scenarios for each case for the counterfactual simulation model layer;(d) aggregate optimization layer selects in real time the optimal pricing configuration across the tested scenarios according to said predetermined incentives and constraints; and(e) the financial operations platform differentially prices each user or transaction based on the predicted value of the case over an evaluated period of time, incorporating both immediate transaction characteristics and expected time value derived from a predicted set of follow-up economic actions.

12. The system of claim 1, wherein the case relates to a currency exchange request made by at least one SP between two different currencies, wherein scenarios relate to an exchange rate available to at least one SP, and wherein the exchange rates derived from the at least one source of financial and economic data are compiled into batches.

13. The computing system of claim 12, wherein the midpoint of exchange values within the batch for both currencies in a currency pair is selected as an exchange rate available on the platform for the at least one SP to make an exchange.

14. The computing system of claim 12, wherein the funds deposited as E-money on the platform belong directly to the at least one SP.

15. The computing system of claim 12, wherein netting of SP requests is optimized per batch by calculating for an imbalance reconcilable by a purchase from an LP of an asset in shortfall.

16. The computing system of claim 12, wherein the at least one SP is a plurality of SPs, and wherein exchange rate prices are minimized by minimizing the imbalance of quantity requests netted together from said plurality.

17. The computing system of claim 16, wherein exchange rate prices are further minimized by maximizing the overall quantities in each transaction in each batch, with a preference for E-money expected to have the largest time value..

18. The computing system of claim 12, wherein the financial operations platform operates recursively until the at least one SP has zero cost transactions.

19. The computing system of claim 12 wherein a computational neural network (CNN) model is trained on supply and demand of different financial instruments using information gleaned from the source of financial and economic data.

20. The computing system of claim 19, wherein the CNN model is trained on the prices of currencies relative to one another within periods of a variety of certain lengths.

21. The computing system of claim 19, wherein the CNN model is trained on the volume of financial instruments at a certain price within periods of a variety of certain lengths.

22. The computing system of claim 19, wherein the CNN model is trained on the quantity, financial instruments, and margin of different arbitrage gaps within periods of a variety of certain lengths.

23. The computing system of claim 19, wherein the CNN model is trained on a number of technical indicators relating to the global markets.The computing system of claim 19, wherein the CNN model is trained on the financial instruments behavior of SPs on the platform, integrating data relating to transaction patterns, personal data, trading history, account activity, and registration.

24. The computing system of claim 19, wherein the CNN model is operated to forecast supply-demand gaps in financial instruments and thereby to optimize offers made to LPs.

25. The computing system of claim 1, wherein a Large Language Model (LLM) is pretrained with real-time data gleaned from the source of financial and economic data and operated to gauge market sentiment as an input for the behavior prediction layer.

26. The computing system of claim 1 , wherein data gleaned from the source of financial and economic data includes data relating to: global economic trends; local economic trends; central bank policies; financial exchange reserves; unemployment rates; purchasing managers’ index; gross domestic products; consumer price index; interest rates; public debt; current account; portfolio investment; foreign direct investment as a percentage of gross domestic product; real effective exchange rate; and short term external debt as a percentage of foreign exchange reserves.

27. The computing system of claim 1, further comprising: a. a cohort-assignment engine configured to allocate at least one user to a group neural network whose learned parameters most closely match an initial or inferred profile of that user; b. a training controller configured to continuously collect usage data and, in response, to retrain or update both the group-level neural network and a personalized neural network associated with the user; wherein, the financial operations platform is configured to adjust, in real time, at least one financial parameter selected from: bid-ask spread, transaction fee, interest rate, or liquidity-retention window, so as to optimize outcomes for both the individual user and the user group.

28. The computing system of claim 27, wherein a computational neural network (CNN) model provides one or more personalized neural networks for each of at least one user, each personalized neural network being trained or updated based on userspecific behaviors and parameters to personally predict the future time value.

29. The computing system of claim 27, wherein the platform executes a dynamic pricing algorithm that, in real time: a. receives a variable set of inputs comprising: i. outputs from personalized or group-level prediction models; and ii. global market data derived from the at least one source of financial and economic data;b. determines a profit-maximizing bid-ask spread subject to a configurable constraint, the constraint defaulting to a maximum spread ceiling of zero and no minimum spread floor, both limits being user-configurable; c. employs linear regression or an equivalent statistical technique to identify the spread that meets a predefined profit target; and d. allocates any realized profit among users according to a fairness rule selected from pro-rata time-value, equal-share, or capped-reward schemes stored in the at least one non-volatile memory.

30. The computing system of claims 27, further comprising an additional neural network module that operates in a zero-data regime and is configured to: a. initialize with a minimal feature set and a shallow architecture having at least one hidden layer with ReLU activation and a linear output layer; b. generate synthetic training samples and self- supervised targets when real or historical data are absent, and automatically deepen the network — by adding hidden layers or widening existing ones — whenever a validationerror threshold is exceeded; and c. predict, under negative-margin scenarios, a ratio of deposited or converted funds to the applied spread, while mitigating overfitting in multi -class situations by employing Softmax or Sigmoid activations together withCategorical Cross-Entropy or an equivalent loss function.

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