Database Conversion System for Financial Transaction Discrepancies
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Solution Overview
Problem
Legacy databases in financial institutions face challenges in evolving to meet changing business requirements, particularly in handling mobile services, social networks, and big data, and struggle with discrepancies in financial transactions, which are complex and time-consuming to identify and resolve.
Innovation Solution
A method and system for converting a structured database into an unstructured database using a distributed environment, where a structured database is retrieved, an optimized target data model is generated based on extracted database objects and pre-defined conversion rules, and a Blockchain is created to identify and notify discrepancies in financial transactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If legacy databases are modernized using traditional methods, then system functionality is improved, but time consumption and cost increase significantly
Solution Approach 1:
The patent segments the database modernization process into distinct components: schema extraction from legacy database, automated mapping to target schema, data transformation rules generation, and incremental migration. This segmentation allows parallel processing and reduces overall modernization time while maintaining system functionality.
Solution Approach 2:
The patent performs preliminary actions by automatically generating the target database schema and transformation rules before actual data migration. The system extracts metadata from the legacy database, pre-defines mapping relationships, and validates the target schema structure in advance, enabling faster execution during the actual modernization phase.
2Ease of operation
If data is uploaded to new data store using middleware solutions, then data sharing is improved, but data loss risk increases
Solution Approach 1:
The patent implements feedback mechanisms through automated validation rules that verify data integrity during transformation and migration. The system continuously monitors data consistency between source and target databases, detecting and correcting anomalies in real-time, thus preventing data loss while enabling easy data sharing.
Solution Approach 2:
The patent introduces an automated data transformation layer as an intermediary between the legacy database and the new data store. This intermediary applies predefined mapping rules and validation logic, ensuring data integrity is maintained during the transition while still enabling seamless data sharing across systems.
3Adaptability or versatility
If legacy systems are replaced with modern technology databases, then business requirements adaptability is improved, but economic cost and time resources increase
Solution Approach 1:
The patent creates a copied and adapted version of the legacy database schema in the target modern database system. Instead of completely redesigning the system, it automatically replicates the essential structure and relationships from the legacy database while adapting to modern database conventions, reducing both cost and time while maintaining business requirements adaptability.
Solution Approach 2:
The patent modifies key parameters during migration by automatically adjusting data types, schema structures, and relationship definitions to match modern database requirements. This parameter transformation enables the system to meet current business requirements while avoiding the need for complete system replacement, thereby reducing modernization costs.
4Measurement precision
If bank reconciliation is performed on legacy databases, then transaction discrepancy identification is possible, but process complexity and time consumption increase
Solution Approach 1:
The patent replaces manual bank reconciliation processes with automated computational algorithms. The system uses machine learning models and rule-based engines to automatically detect transaction discrepancies, eliminating the need for complex manual reconciliation procedures while maintaining high accuracy in identifying issues.
Solution Approach 2:
The patent enables the database system to perform self-diagnosis and self-correction of transaction discrepancies. The automated reconciliation system continuously monitors data integrity, detects anomalies, and triggers corrective actions without external intervention, reducing process complexity while maintaining high measurement precision for discrepancy identification.
Data Source
AI summary
Embodiments of present disclosure discloses system and method for conversion of structured database into unstructured database. Initially, structured database and database descriptor is retrieved in distributed environment. Structured database comprises data fields, and each of the data fields corresponds to financial transaction. Optimized target data model is generated for storing data in the unstructured database, based on at least one of database objects extracted from data fields of structured database, and pre-defined conversion rules. Further, a Blockchain comprising blocks corresponding to each financial transaction of structured database. Each of the blocks comprise at least one of extracted database object. Conversion of the database object of each of the blocks into data fields of unstructured database is performed based on optimized target data model and notification corresponding to data fields of unstructured database is generated which are associated with discrepancies corresponding to the financial transaction.