US-20260127675-A1 - SYSTEM AND METHOD FOR MANAGING FINANCIAL DATA USING CONTEXT-AWARE DATA FRAMEKWORK FOR REAL-TIME CAPITAL CONTROL

US20260127675A1US 20260127675 A1US20260127675 A1US 20260127675A1US-20260127675-A1

Abstract

A system and method for managing financial data using a context-aware data framework for real-time capital control are disclosed. The method includes receiving, at a plurality of distributed processing nodes ( 106 a , 106 b , 106 c . . . 106 n ), a plurality of financial transaction data streams from a plurality of distributed data sources ( 104 a , 104 b , 104 c . . . 104 n ). The method includes generating the context-aware data framework. The method includes detecting one or more variations in one or more transaction context, assessed risk exposure, and capital flow conditions. The method includes updating one or more database states. The method includes executing one or more real-time capital control operations within the context-aware data framework. The method includes replicating, execution of the one or more real-time capital control operations, the updated one or more database states across the plurality of distributed processing nodes. The method includes generating one or more immutable contextual log records within the context-aware data framework.

Inventors

Vinay Kumar Gali

Assignees

Vinay Kumar Gali

Dates

Publication Date: 20260507
Application Date: 20251229

Claims (20)

1 . A method for managing financial data using a context-aware data framework for real-time capital control, the method comprising: receiving, at a plurality of distributed processing nodes, a plurality of financial transaction data streams from a plurality of distributed data sources in real time; generating the context-aware data framework based on the received plurality of financial transaction data streams, wherein the context-aware data framework comprises one or more adaptive data structures configured to dynamically encode one or more semantic relationships among a plurality of financial entities, a plurality of transaction attributes, and a plurality of regulatory parameters, wherein the one or more semantic relationships establish contextual dependencies used for subsequent database state evaluation; detecting one or more variations in one or more transaction context, assessed risk exposure, and capital flow conditions within the context-aware data framework, by analyzing changes in the generated one or more semantic relationships encoded by the one or more adaptive data structures; updating one or more database states within the context-aware data framework by executing one or more machine-implemented state transition rules based on the detected one or more variations; executing one or more real-time capital control operations within the context-aware data framework by applying one or more adaptive constraint enforcement mechanisms to at least one of transaction routing, liquidity allocation, and compliance threshold management based on the updated one or more database states; replicating, execution of the one or more real-time capital control operations, the updated one or more database states across the plurality of distributed processing nodes by performing consensus-based propagation, wherein the consensus-based propagation maintains a consistent capital control state across the plurality of distributed processing nodes; and generating, subsequent to the replication of the updated database states, one or more immutable contextual log records within the context-aware data framework, wherein the one or more immutable contextual log records configured to store the received plurality of financial transaction data streams and corresponding state transition events, wherein the one or more immutable contextual log records enable traceable regulatory oversight of the one or more real-time capital control operations.
2 . The method of claim 1 , wherein the received plurality of financial transaction data streams includes input data for constructing and updating the context-aware data framework.
3 . The method of claim 1 , wherein the one or more adaptive constraint enforcement mechanisms are dynamically derived from the one or more database states.
4 . The method of claim 1 , wherein the updated one or more database states indicate an evolved capital and compliance posture derived from the detected variations.
5 . The method of claim 1 , wherein generating the context-aware data framework comprises: constructing a multi-layer data representation including: an entity relationship layer encoding associations between the plurality of financial entities, a transaction context layer encoding temporal and behavioral attributes of transactions, and a regulatory context layer encoding jurisdiction-specific compliance parameters, wherein updates in layer trigger recalculation of contextual dependencies in the remaining layers.
6 . The method of claim 1 , wherein the one or more adaptive data structures comprise graph-based data structures having one or more weighted edges, wherein the one or more weighted edges are updated in real time based on observed changes in transaction frequency, capital flow direction, or regulatory parameter relevance.
7 . The method of claim 1 , wherein detecting the one or more variations in the one or more transaction context comprises: determining incremental comparison between a current context snapshot and a prior context snapshot stored within the context-aware data framework; and detecting the one or more variations in the one or more transaction context based on the determined incremental comparison.
8 . The method of claim 1 , wherein updating the one or more database states by executing the one or more machine-implemented state transition rules, wherein execution of the state transition rules is enabled by a rule evaluation pipeline configured to: evaluate a context deviation condition derived from changes in the one or more semantic relationships, and evaluate a capital exposure threshold condition derived from the one or more database states.
9 . The method of claim 1 , wherein the updated one or more database states comprise at least one of: a capital allocation state, a liquidity availability state, and a compliance exposure state, wherein each state is maintained as a versioned data object to enable rollback and historical comparison.
10 . A system for managing financial data using a context-aware data framework for real-time capital control, the system comprising: a memory; at least one processor is operatively coupled to the memory, wherein the at least one processor is configured to: receive, at a plurality of distributed processing nodes, a plurality of financial transaction data streams from a plurality of distributed data sources in real time; generate the context-aware data framework based on the received plurality of financial transaction data streams, wherein the context-aware data framework comprises one or more adaptive data structures configured to dynamically encode one or more semantic relationships among a plurality of financial entities, a plurality of transaction attributes, and a plurality of regulatory parameters, wherein the one or more semantic relationships establish contextual dependencies used for subsequent database state evaluation; detect one or more variations in one or more transaction context, assessed risk exposure, and capital flow conditions within the context-aware data framework, by analyzing changes in the generated one or more semantic relationships encoded by the one or more adaptive data structures; update one or more database states within the context-aware data framework by executing one or more machine-implemented state transition rules based on the detected one or more variations; execute one or more real-time capital control operations within the context-aware data framework by applying one or more adaptive constraint enforcement mechanisms to at least one of transaction routing, liquidity allocation, and compliance threshold management based on the updated one or more database states; replicate, execution of the one or more real-time capital control operations, the updated one or more database states across the plurality of distributed processing nodes by performing consensus-based propagation, wherein the consensus-based propagation maintains a consistent capital control state across the plurality of distributed processing nodes; and generate, subsequent to the replication of the updated database states, one or more immutable contextual log records within the context-aware data framework, wherein the one or more immutable contextual log records configured to store the received plurality of financial transaction data streams and corresponding state transition events, wherein the one or more immutable contextual log records enable traceable regulatory oversight of the one or more real-time capital control operations.
11 . The system of claim 10 , wherein the received plurality of financial transaction data streams includes input data for constructing and updating the context-aware data framework.
12 . The system of claim 10 , wherein the one or more adaptive constraint enforcement mechanisms are dynamically derived from the one or more database states.
13 . The system of claim 10 , wherein the updated one or more database states indicate an evolved capital and compliance posture derived from the detected variations.
14 . The system of claim 10 , wherein to generate the context-aware data framework, the at least one processor is configured to: construct a multi-layer data representation including: an entity relationship layer encoding associations between the plurality of financial entities, a transaction context layer encoding temporal and behavioral attributes of transactions, and a regulatory context layer encoding jurisdiction-specific compliance parameters, wherein updates in layer trigger recalculation of contextual dependencies in the remaining layers.
15 . The system of claim 10 , wherein the one or more adaptive data structures comprise graph-based data structures having one or more weighted edges.
16 . The system of claim 15 , wherein the one or more weighted edges are updated in real time based on observed changes in transaction frequency, capital flow direction, or regulatory parameter relevance.
17 . The system of claim 10 , wherein to detect the one or more variations in the one or more transaction context, the at least one processor is configured to: determine incremental comparison between a current context snapshot and a prior context snapshot stored within the context-aware data framework; and detect the one or more variations in the one or more transaction context based on the determined incremental comparison.
18 . The system of claim 10 , wherein to update the one or more database states by executing the one or more machine-implemented state transition rules, wherein execution of the state transition rules is enabled by a rule evaluation pipeline using the at least one processor is configured to: evaluate a context deviation condition derived from changes in the one or more semantic relationships, and evaluate a capital exposure threshold condition derived from the one or more database states.
19 . The system of claim 10 , wherein the updated one or more database states comprise at least one of: a capital allocation state, a liquidity availability state, and a compliance exposure state, wherein each state is maintained as a versioned data object to enable rollback and historical comparison.
20 . A non-transitory computer-readable medium storing instructions that, when executed, cause a processor to: receive, at a plurality of distributed processing nodes, a plurality of financial transaction data streams from a plurality of distributed data sources in real time; generate, based on the received plurality of financial transaction data streams, the context-aware data framework comprising one or more adaptive data structures configured to dynamically encode one or more semantic relationships among a plurality of financial entities, a plurality of transaction attributes and a plurality of regulatory parameters, wherein the generated one or more semantic relationships establish contextual dependencies used for subsequent database state evaluation; detect, within the context-aware data framework, one or more variations in one or more transaction context, assessed risk exposure, and capital flow conditions by analyzing changes in the one or more semantic relationships encoded by the one or more adaptive data structures; and update, in response to the detected one or more variations, one or more database states within the context-aware data framework by executing one or more machine-implemented state transition rules; execute, based on the updated one or more database states, one or more real-time capital control operations within the context-aware data framework by applying one or more adaptive constraint enforcement mechanisms to at least one of transaction routing, liquidity allocation, and compliance threshold management; replicate, execution of the one or more real-time capital control operations, the updated one or more database states across the plurality of distributed processing nodes by performing consensus-based propagation, wherein the consensus-based propagation maintains a consistent capital control state across the distributed nodes; and generate, subsequent to the replication of the updated database states, one or more immutable contextual log records within the context-aware data framework, wherein the one or more immutable contextual log records configured to configured to store the received plurality of financial transaction data streams and corresponding state transition events, wherein the one or more immutable contextual log records enable traceable regulatory oversight of the one or more real-time capital control operations.

Description

COPYRIGHT AND TRADEMARK NOTICE This application includes material which is subject or may be subject to copyright and/or trademark protection. The copyright and trademark owner(s) have no objection to the facsimile reproduction by any of the patent disclosure, as it appears in the Patent and Trademark Office files or records, but otherwise reserves all copyright and trademark rights whatsoever. TECHNICAL FIELD The present invention relates generally to field of financial data management systems. More particularly, to systems and methods for managing financial data using context-aware data framework with autonomous database state evolution for real-time capital control. BACKGROUND OF THE INVENTION In financial computing environments, financial institutions operate across distributed systems that continuously generate large volumes of transaction data originating from various sources. The transaction data streams are often processed independently by separate platforms responsible for transaction execution, liquidity management, compliance monitoring, and reporting. As a result, contextual relationships among financial entities, transaction attributes, regulatory constraints, and capital exposure conditions are fragmented across isolated data repositories, limiting the ability of existing systems to form a unified, real-time view of capital position and compliance posture. Conventional financial data management solutions typically rely on static data schemas and predefined rule sets that are evaluated in batch or near-batch modes. The aforementioned approaches are not well suited to dynamically changing transaction contexts, evolving regulatory requirements, or rapid shifts in capital flow conditions. When transaction volumes increase or when regulatory parameters vary across institutions, the aforementioned systems struggle to adapt without manual intervention, leading to delayed responses, inconsistent capital controls, and increased operational risk. Existing real-time capital control mechanisms are constrained by limited coordination across distributed processing nodes. Updates to capital allocation or compliance thresholds are often applied locally, with synchronization occurring asynchronously or through centralized reconciliation processes. This can result in transient inconsistencies in capital control states across the distributed processing nodes, increasing the risk of over-allocation, liquidity imbalance, or regulatory breaches during high-throughput transaction periods. Additionally, many known financial data frameworks provide audit and compliance functionality through separate logging or reporting subsystems that operate independently of transaction processing and state management. Such decoupled audit mechanisms often fail to preserve the contextual linkage between transaction data, state transitions, and applied control actions. Consequently, reconstructing an accurate and verifiable history of how capital control decisions are derived becomes complex, time-consuming, and error-prone, particularly in regulatory review or dispute resolution scenarios. Therefore, there is need to develop a system and method to overcome aforementioned problems. SUMMARY OF THE INVENTION This summary is provided to introduce a selection of concepts, in a simple manner, which is further described in the detailed description of the disclosure. This summary is neither intended to identify key or essential inventive concepts of the subject matter nor to determine the scope of the disclosure. In accordance with an embodiment of the present disclosure, a method for managing financial data using a context-aware data framework for real-time capital control is disclosed. The method includes receiving, at a plurality of distributed processing nodes, a plurality of financial transaction data streams from a plurality of distributed data sources in real time. The method includes generating the context-aware data framework based on the received plurality of financial transaction data streams. The context-aware data framework comprises one or more adaptive data structures configured to dynamically encode one or more semantic relationships among a plurality of financial entities, a plurality of transaction attributes, and a plurality of regulatory parameters. The one or more semantic relationships establish contextual dependencies used for subsequent database state evaluation. The method includes detecting one or more variations in one or more transaction context, assessed risk exposure, and capital flow conditions within the context-aware data framework, by analyzing changes in the generated one or more semantic relationships encoded by the one or more adaptive data structures. The method includes updating one or more database states within the context-aware data framework by executing one or more machine-implemented state transition rules based on the detected one or more variations. The method includes executing one or more rea