A Comprehensive Study of Distributed Transaction Handling in Microservices-Based Financial Applications

A Comprehensive Study of Distributed Transaction Handling in Microservices-Based Financial Applications

Mohammad Rezwanul Huq

Department of Computer Science & Engineering East West University, Dhaka 1212, Bangladesh

Shah Md. Ziaur Rahaman

Department of Computer Science & Engineering United International University, Dhaka 1212, Bangladesh

Abstract – Managing distributed transactions presents signi- cant challenges as nancial systems increasingly transition to a microservices architecture for improved scalability, agility, and resilience. Traditional techniques like Two-Phase Commit (2PC), SAGA, Try-Conrm/Cancel (TCC), and Event Sourcing have strengths but often fail to address the diverse requirements of large-scale, microservices-based nancial applications. This paper proposes a hybrid transaction management framework that combines the advantages of these techniques, leveraging dy- namic transaction prioritization and a hybrid SAGA-TCC model. The framework categorizes transactions based on criticality, employing SAGA for high-scalability operations and an enhanced TCC-SEGA model for critical transactions to ensure atomicity and real-time consistency. A decision tree approach is used to assign transaction weights based on attributes such as risk, importance, and type, enabling an adaptable and efcient system. This comprehensive solution provides a balance between perfor- mance, consistency, and scalability, addressing the complexities of distributed transaction management in modern nancial systems. Future directions include exploring blockchain integration for enhanced transparency and immutability, adaptive decision trees for dynamic prioritization, and machine learning models for predictive transaction management to optimize efciency and resilience further.

KeywordsDistributed Transactions, Microservices Architec- ture, Transaction Management, ACID Properties

1. Introduction

   As enterprises adopt microservices architecture to improve scalability and agility in application development, managing transactions in distributed environments become increasingly complex. In traditional monolithic systems, a single transaction manager ensures that all operations within a transaction adhere to the ACID propertiesAtomicity, Consistency, Isolation, and Durability [1]. However, in microservices, where services are loosely coupled and often backed by independent databases, achieving the same level of consistency and reliability across distributed systems requires new approaches to transaction management [2]. Each microservices in a distributed archi- tecture manages its own data, and transactions may span multiple services, making the traditional monolithic approach to transaction management unsuitable. To address these chal- lenges, new transaction management models have emerged, such as Two-Phase Commit (2PC) and SAGA, along with Oracles newly introduced Transaction Manager for Microser- vices [3], [4]. These techniques allow developers to manage

   distributed transactions while balancing the trade-offs between performance, consistency, and availability in microservices- based systems. Oracles Transaction Manager is specically designed to handle distributed transactions in microservices environments, providing tools to ensure that transactions span- ning multiple services maintain data integrity and consistency across independent databases. This transaction manager helps developers coordinate transactions across different services by providing event-driven and orchestrated workows, ensuring that each step of the transaction is managed carefully, even in case of failures [3]. In the nancial sector, the impact is particularly high when it comes to transaction integrity. Financial systems handle various critical transactions, from customer payments to loan disbursements, and even a minor inconsistency in these transactions can lead to serious conse- quences such as nancial loss or regulatory non-compliance [5]. Traditional ACID compliance becomes difcult to enforce in a microservices environment due to the distributed nature of data across multiple services and databases [6]. The use of distributed databases in microservices based systems offers improved scalability and resilience but requires sophisticated transaction management to prevent data anomalies such as dirty reads, non-repeatable reads, and phantom reads [2]. Methods such as SAGA and Oracles Transaction Manager for Microservices offer a exible approach to managing these distributed transactions, ensuring that the system maintains consistency without sacricing performance.

   Contribution of This Paper

   This paper provides a comprehensive review of transaction management techniques in microservices-based applications specially for the nancial sector, with three key contributions:

   • Analysis of Distributed Transaction Techniques: This section reviews key techniques such as Two- Phase Commit (2PC), SAGA, Try-Conrm/Cancel (TCC), and Event Sourcing, discussing their advan- tages and limitations in nancial microservices appli- cations.
   • Distributed Transaction Manager: The paper evalu- ates popular Transaction Manager, focusing on its ca- pabilities for managing distributed transactions across microservices.

     between monolithic and microservices architectures are signif- icant.

     Fig. 1. Microservices Architecture

   • Practical Recommendations: Provides guidelines for adopting appropriate transaction management strate- gies in nancial systems, balancing integrity, perfor- mance, and scalability.

     The rest of this paper is organized as follows: Section II contrasts the characteristics and transaction management chal- lenges of monolithic versus microservices architectures. Sec- tion III provides an overview of transactions in database sys- tems, highlighting their critical role in nancial applications. Section IV discusses distributed transactions, their challenges, and trade-offs in microservices-based systems. Section V ex- plores different distributed transaction handling techniques. Section VI evaluates distributed transaction managers. Section VII presents the proposed approach for managing distributed transactions, emphasizing the TCC models suitability for nancial applications. Finally, Section VIII concludes with insights and recommendations for distributed transaction man- agement strategies in nancial systems.

2. Microservices vs Monolithic Systems

   The rise of microservices architecture has fundamentally transformed how applications are designed and deployed, es- pecially compared to traditional monolithic systems. In mono- lithic systems, the entire application is built as a single unit, with all components tightly coupled and sharing a common database. This centralized design allows for easier management of transactions and adherence to the ACID properties [2]. However, monolithic architectures face signicant scalability, exibility, and deployment challenges. Updating or scaling a monolithic application requires redeploying the entire system, making it unsuitable for rapidly evolving, large-scale applica- tions [7]. In contrast, microservices architecture as shown in Figure 1 decomposes an application into smaller, independent services, each responsible for a specic business function [8]. These services are loosely coupled, independently deployable, and often backed by their own databases, which improves scalability and exibility. However, this architectural shift introduces complexity, particularlyin managing transactions, as they span multiple services and databases. Transaction man- agement in a microservices environment requires special at- tention because traditional approaches like ACID transactions become difcult to implement across distributed databases [9]. As shown in Table I, the transaction management differences

3. Transactions in Databases: A Practical Overview

   A transaction in database systems represents a sequence of operations that must either complete entirely or have no effect at all. This is essential to ensure that the database remains in a consistent state, even in the presence of concurrent operations or system failures. Transactions are particularly critical in nancial systems, where the integrity of operations like fund transfers, loan disbursements, and payment processing must be guaranteed.

   ACID Properties: To ensure reliability and correctness, transactions in databases adhere to the ACID properties:

   • Atomicity: This property ensures that a transaction is treated as a single unit. If any operation within a transaction fails, the entire transaction is rolled back, and the database remains unchanged. For instance, in a fund transfer, if the debit operation succeeds but the credit operation fails, the system must roll back the debit to prevent data inconsistency [6].
   • Consistency: A transaction must guarantee that the database moves from one valid state to another, main- taining predened rules such as constraints, triggers, and integrity checks. This ensures that even after a transaction, the system remains consistent according to its business rules and schema [10].
   • Isolation: In multi-user environments, isolation en- sures that concurrent transactions do not interfere with one another. This means that intermediate states of a transaction are invisible to other transactions until the transaction is complete. Isolation levelssuch as Read Uncommitted, Read Committed, Repeatable Read, and Serializabledetermine how isolated a transaction is from others. Serializable provides the highest isolation level but can reduce performance due to the extensive locking of resources [11].
   • Durability: Once a transaction is committed, its changes are permanent, even if the system crashes immediately afterward. Techniques like logging, repli- cation, and backup ensure that committed results are durable and cannot be lost [1].

     For example, when a customer transfers money between ac- counts in a Banking System, the transaction involves two fundamental operations: debiting the senders account and crediting the recipients account. The system must ensure that either both operations succeed or neither does. If the system fails after debiting the sender but before crediting the recipient, the transaction must be rolled back to avoid data inconsistency [9]. Scalability Problems In traditional database systems, scaling to increase transaction volumes can be challenging. There are two primary approaches to scaling a database: vertical scaling and horizontal scaling. Both have limitations when applied to ACID-compliant systems. Verti- cal Scaling:Vertical scaling involves adding more powerful hardware to the existing database serversuch as more CPU,

     TABLE I. Comparison of Transaction Management in Microservices vs Monolithic Systems

     Aspect	Monolithic System	Microservices System
     Architecture	Single, unied system with tightly coupled components	Decoupled, independent services with separate databases
     Transaction Boundaries	Single, centralized transaction manager	Multiple independent transaction managers (distributed)
     Transaction Scope	Single database, transactions span the whole system	Transactions span across multiple services and databases
     ACID Compliance	Easier to maintain strict ACID properties	Difcult to enforce ACID across distributed services, often BASE is used
     Scalability	Limited scalability due to centralized architecture	Highly scalable due to independent services
     Transaction Coordination	Simpler, as all components interact within one system	Complex, requires coordination between services (e.g., SAGA, 2PC)
     Complexity of Transactions	Lower complexity since transactions happen within one system	High complexity due to the distributed nature of transactions
     Concurrency Control	Centralized concurrency control with simpler mechanisms	Decentralized concurrency control often needs advanced mechanisms (e.g., MVCC)
     Failure Handling	Centralized rollback and recovery mechanisms	Distributed failure handling with compensating transactions (e.g., SAGA)
     Performance Impact	Performance degrades with system growth due to centralized architecture	Better performance per service, but high complexity impacts performance for cross-service transactions

     Fig. 2. Typical Distributed Transaction in Banking System

     memory, or faster storage. While vertical scaling can improve performance in the short term, it has inherent limitations, such as hardware limits and a single point of failure. Horizontal Scaling: Horizontal scaling (sharding) involves distributing the data across multiple servers or database nodes. Each node handles a portion of the data or certain types of transactions in this setup. However, implementing ACID properties in a horizontally scaled environment introduces several challenges:

   • Distributed transactions
   • Data consistency
   • Complexity of management
4. Distributed Transactions

   Distributed transactions span multiple services or databases across distributed systems, often requiring coordination to ensure that a series of related operations either complete successfully as a unit or fail. This is crucial for maintaining data integrity and consistency in cloud computing, microser- vices, and distributed databases. Unlike traditional monolithic systems, where all components are tightly coupled and operate under a single database, distributed transactions must handle the complexities of coordinating between multiple independent systems, each with its data store. This complexity grows as distributed services must work together to perform a single business operation, such as processing a payment, managing inventory, or completing an order [9]. Figure 2 illustrates a typical distributed transaction initiated by a mobile application, which involves three services: CASA, Loan, and IB-Service. Key Challenges in Distributed Transactions

   • Transaction Boundaries: In distributed systems, each database operates independently, which means that distributed transactions span multiple services and databases. Coordinating these transactions requires distributed coordination protocols [6].
   • Consistency and Reliability: Ensuring strong consis- tency across multiple independent systems is signi- cantly more challenging in a distributed environment. Distributed systems often prioritize high availability and scalability [12]. While this model is acceptable for many applications, it may be insufcient for mission- critical operations in domains like banking, nancial services, or e-commerce, where real-time data accu- racy and integrity are essetial [9].
   • Transaction isolation level: It species the amount of data visible in a transaction when the other services access the same data simultaneously.
   1. CAP Theorem

      The CAP Theorem (Consistency, Availability, Partition Tol- erance), rst introduced by Eric Brewer, highlights the inherent trade-offs in distributed systems. According to the theorem, it is impossible for a distributed system to simultaneously guarantee strong consistency, high availability, and partition tolerance [12]. In the context of distributed transactions, this means that system architects must prioritize one or two of these properties while sacricing the others. As shown in Figure 3, the CAP theorem highlights the trade-offs between Consistency, Availability, and Partition Tolerance in distributed systems.

      • Consistency: All nodes in the system see the same data at the same time. In distributed transactions, this would mean ensuring that all participating services either commit or roll back together.
      • Availability: The system continues to operate, even in the presence of failures. For distributed transactions, this implies that services are responsive and can com- plete their local tasks even when the entire system is not in sync.
      • Partition Tolerance: The system remains functional even when communication between some nodes fails (network partitions).

        Fig. 3. CAP Theorem

        In distributed transactions, prioritizing consistency (as seen in traditional ACID transactions) often comes at the cost of avail- ability. Conversely, systems prioritizing availability sacrice strong consistency in favor of eventual consistency [12]. In nancial systems, consistency should typically be the highest priority, followed by partition tolerance, with some acceptable trade-offs on availability. The nancial industry cannot af- ford inconsistencies, especially in systems handling payments, loan processing. Ensuring that transactions remain atomic and isolated across distributed services is critical, even if that means services are occasionally unavailable during network issues or when consistency must be maintained. Therefore, while nancial systems typically prioritize consistency and partition tolerance, mission-critical systems cannot afford to trade off availability. To address this challenge, various failover mechanisms and redundancy techniques are applied, ensuring that services remain available even during network partitions or failures.

   2. BASE in Distributed Transactions

   The BASE model offers an alternative to ACID in dis- tributed environments, emphasizing availability and partition tolerance over immediate consistency. In a BASE system: Basically Available: The system remains operational even in the presence of failures. Soft State: The state of the system may change over time, even without new inputs, as updates propagate through the system. Eventually Consistent: The system guarantees that, over time, all nodes will converge to the same consistent state, even though this may not happen immediately [6]. In distributed transactions, the exibility of the BASE model allows services to commit their operations independently, ensuring high availability and fault tolerance, but only guarantees eventual consistency. While this trade- off is suitable for highly scalable, distributed systems where immediate consistency is not critical, nancial applications cannot afford such a compromise. Therefore, necessary mea- sures are implemented to ensure strong consistency, such as adopting stricter transaction management protocols and incorporating failover mechanisms and redundancy to maintain both availability and consistency.

5. Techniques of Distributed Transaction Management

   There are several techniques for managing distributed transactions in microservices environments, each with its own trade-offs between consistency, availability, and fault tolerance:

   Fig. 4. Two phase commit

   1. Two-Phase Commit (2PC)

      The Two-Phase Commit (2PC) protocol ensures atomicity and consistency across multiple services by involving a central coordinator. The protocol is divided into two phases as shown in Figure 4

      • Phase 1 (Prepare): The coordinator asks each service if it can commit the transaction. Each service replies whether it is ready to commit.
      • Phase 2 (Commit/Abort): If all services agree to commit, the transaction is committed; otherwise, it is aborted.

        While 2PC guarantees strong consistency, it suffers from:

      • Performance overhead: The communication and syn- chronization required among services lead to latency.
      • Locking Issues: If the coordinator crashes, the trans- action may be blocked until it is resolved [6]. In a high-volume banking environment, this can cause performance degradation, as other transactions would be unable to access those locked resources, leading to delays and poor scalability. This becomes un- acceptable in a mission critical system where high throughput, availability, and reliability are essential
      • Single point of failure: The central coordinator can become a bottleneck or point of failure.
   2. Saga Pattern

      The Saga pattern is a design pattern used for manag- ing distributed transactions in microservices architectures. It breaks down a global transaction into a series of smaller, local transactions, each executed by a different microservice. If any of these transactions fail, compensating transactions are triggered to undo the completed steps. This pattern is particularly useful when maintaining strict ACID properties (Atomicity, Consistency, Isolation, Durability) across multiple services is impractical, due to the complexity or inefciency

      Fig. 5. Sega Transaction Flow

      of traditional techniques like Two-Phase Commit (2PC) [13]. Figure 5 shows how to visualize the Saga pattern for a fund transfer between CASA and Loan account. The Saga pattern ensures eventual consistency, meaning that while a system may not be immediately consistent across all microservices, it will eventually converge to a consistent state. This makes it well-suited for environments where high availability and performance are prioritized over immediate consistency, as is often the case in e-commerce, banking, and travel booking systems. Saga Coordination Strategies There are two primary ways to implement the Saga pattern, each with its own advan- tages and trade-offs: Choreography: In choreography-based sagas, there is no central coordinator. Each service involved in the transaction listens for and responds to events from other services. For example, after one service completes its local transaction, it publishes an event that triggers the next service to start its part of the transaction. Pros: Choreography is highly decentralized and scalable. Each service operates independently, reducing the chances of a single point of failure. Cons: As the number of services grows, managing the ow of events can become complex, leading to what is often referred to as event spaghetti, where services are too tightly coupled through a web of event dependencies [6]. Orchestration: In orchestration-based sagas, a central orchestrator is responsible for managing the ow of the transaction. The orchestrator calls each service in sequence and handles the compensating actions in the event of a failure. Pros: Orchestration provides better control and visibility over the transaction ow. The central orchestrator knows the entire sequence of operations, which simplies failure handling and debugging. Cons: The orchestrator can become a single point of failure and might introduce performance bottlenecks due to its central role n coordinating all the steps [13].

   3. Try-Conrm/Cancel (TCC)

      The Try-Conrm/Cancel (TCC) model is a distributed transaction management technique designed to handle trans- actions across multiple microservices. It is a variation of the Two-Phase Commit (2PC) protocol and is particularly suitable for managing transactions in systems where high availability, graceful failure handling, and exibility are important. Figure 6 shows how to visualize the TCC model for a fund transfer between CASA and Loan account The TCC model divides the transaction lifecycle into three distinct phases: Try, Conrm, and Cancel. Phases of the TCC Model:

      • Try Phase: In the Try phase, resources are reserved or locked, and any necessary preconditions are checked. This phase is essentially a preparation step where each service involved in the transaction ensures that it can successfully complete its part of the operation. No

        Fig. 6. TCC Model for a fund transfer transaction

        actual changes are made to the system at this stage, but the resources that will be needed for the transaction (such as database records or inventory items) are temporarily reserved to prevent conicting operations from taking place.

      • Conrm Phase: If all the participants in the transac- tion succeed in the Try phase, the Conrm phase is executed. In this phase, the actual transaction is com- mitted, and the reserved resources are nalized.Once the Conrm phase is completed, the transaction is considered successful, and all operations across the distributed services are committed.
      • Cancel Phase: If any participant fails in the Try phase or the transaction needs to be aborted due to some failure or timeout, the Cancel phase is initiated. In this phase, all resources reserved during the Try phase are released or rolled back. The system ensures that the partial effects of the transaction are undone, and no permanent changes are made to the system.

        Pros:

      • Graceful Failure Handling: TCC allows for a clean rollback of transactions if anything goes wrong, ensur- ing that the system remains in a consistent state even when failures occur.
      • Flexibility: Each service can operate independently, handling failures and retries as necessary.
      • Resource Reservation: By reserving resources up- front, the system ensures that the necessary resources will be available when the transaction is ready to commit. For example, in a fund transfer from CASA to Loan, during the Try phase, the system checks whether the transaction is feasible and temporarily locks the requested amount in the CASA account. This reserved amount is blocked, meaning it cannot be used for other transactions, but no actual debit occurs yet. The funds

        remain reserved until the transaction proceeds to the Conrm phase, where the nal debit is made.

        Cons:

      • Complexity: Implementing TCC requires careful co- ordination between services, especially in handling failures and ensuring that reserved resources are prop- erly released in the Cancel phase.
      • Performance Overhead: Reserving resources during the Try phase can introduce performance bottlenecks, particularly if many transactions are pending or if services are slow to conrm or cancel operations.
   4. Eventual Consistency with Event Sourcing

   Eventual Consistency with Event Sourcing is a key archi- tectural pattern often employed in distributed systems to handle state changes asynchronously across microservices, leading to a nal consistent state. In event sourcing, all changes to the systems state are captured as a sequence of immutable events, which are stored in an append-only log. These events are then processed by other services asynchronously, eventually bringing the entire system to a consistent state. This approach contrasts with traditional methods that update the current state directly. Instead, event sourcing allows for the systems state to be rebuilt at any time by replaying the event log. It also provides a complete audit trail, which is particularly useful in systems like banking, where tracking every action is critical. The inherent exibility of this pattern allows for multiple views or projections of the data to be generated, enabling specic optimizations for querying and reporting without affecting the underlying event log. Eventual consistency in event sourcing means that there is often a delay between when an event is recorded and when all services reect that change, known as the inconsistency window. This model is particularly effective in high-scalability systems where performance and availability are prioritized over strict real-time consistency. In nancial systems, event sourcing with eventual consistency provides benets such as scalability and high availability, as services can handle state changes independently and asynchronously. However, the asynchronous behavior is not well-suited for critical nancial operations, where real-time consistency is essential to prevent issues like double-spending or transaction mismatches. Financial transactions often require strong con- sistency to ensure that all parts of the system reect the latest state immediately, making the delayed consistency model of event sourcing a potential risk in this context.

6. Distributed Transaction Manager

   This section explores industry-standard transaction man- agers used for handling distributed transactions in microser- vices and distributed systems

   • Oracle MicroTx: Supports protocols like XA, SAGA, and TCC to manage distributed transactions. Designed for microservices, it integrates seamlessly with con- tainerized environments like Kubernetes, providing strong consistency and scalability.

     Use Case: Ideal for nancial and enterprise sys- tems requiring strict transaction guarantees across distributed services

   • Spring Boot with JTA (Java Transaction API): Provides a standardized API for managing distributed transactions in Java applications. It enables transac- tions across multiple resources such as databases and message brokers using the 2PC (Two-Phase Commit) protocol.

     Use Case: Widely used in enterprise applications where transactional consistency is crucial across mul- tiple microservices.

   • Apache Kafka with Exactly Once Semantics (EOS): Kafkas exactly-once delivery guarantees transactional consistency in event-driven architectures. It ensures atomic processing of messages and data consistency across distributed microservices.

     Use Case: Common in high-throughput, event-driven applications where scalability is critical but eventual consistency is acceptable.

   • Atomikos: A lightweight, high-performance transac- tion manager that supports distributed transactions using JTA and XA protocols. Its designed for cloud- native and microservices environments.

   Use Case: Suitable for cloud and microservice ap- plications where traditional heavyweight transaction managers may not be efcient.

7. Proposed Technique

   Selecting the right technique for distributed transaction management is critical for nancial systems, where atomicity, consistency, availability, integrity, and scalability must coexist. Existing techniques such as Two-Phase Commit (2PC), SAGA, Try-Conrm/Cancel (TCC), and Event Sourcing each excel in specic scenarios but often fall short when applied to large- scale, diverse microservices environments. This section pro- poses hybrid transaction management approaches to overcome the limitations of individual techniques by leveraging their strengths in a unied framework. These approaches aim to ensure the reliablity, efciency, and scalability required in modern nancial systems.

   1. Hybrid SAGA-TCC Model

      The hybrid SAGA-TCC model integrates the compensating transaction mechanism of the SAGA pattern with the resource reservation capability of TCC.

      • Design: Begin by categorizing transactions based on their criticality and impact. For non-critical transac- tions, where high availability and scalability are the primary goals, adopt the SAGA pattern to decompose the global transaction into smaller, independent local transactions. Each local transaction is managed by a microservice and includes a corresponding compen- sating action to reverse the effects in case of a failure, ensuring eventual consistency without compromising system performance. For critical transactions, where immediate consistency and atomicity are essential, employ a hybrid TCC-SEGA model. In the Try phase, reserve necessary resources and validate precondi- tions, ensuring that all required components are ready to proceed with the transaction. Instead of a traditional

        Conrm phase, implement the SEGA (Simple, Exten- sible, Generalized Atomicity) pattern. SEGA assigns priorities or weights to sub-transactions, executing higher-priority operations (e.g., debits) before lower- priority ones (e.g., credits). This ensures a more controlled and efcient commit process, maintaining atomicity while providing exibility to handle com- plex dependencies. This approach enhances the con- sistency and reliability of critical nancial operations, addressing their unique transactional requirements.

      • Key Benets:
        • Ensures real-time consistency for high-value transactions.
        • Provides scalability and fault tolerance for less critical operations.
        • Reduces system bottlenecks by using appropri- ate models for specic transaction types.
   2. Weighted Transaction Prioritization

      This approach assigns weights to transactions based on their importance, criticality, Transaction type and risk, using a decision tree to determine the appropriate transaction manage- ment technique.

      • Design: High-weight transactions, such as fund transfers, are managed using TCC to ensure imme- diate consistency. Low-weight transactions, like notications or audit logs, are processed using SAGA or Event Sourcing to achieve scalability and fault tolerance.
      • Key Benets:
        • Offers a tailored strategy for each transaction type, balancing system load and reliability.
        • Minimizes delays for non-critical operations while maintaining rigorous standards for high- value transactions.

   These hybrid approaches aim to balance the trade-offs inherent in distributed transaction management techniques. By com- bining the strengths of existing models, the proposed tech- niques can handle diverse transactional requirements efciently and reliably. Financial systems, which demand both real-time consistency for critical operations and scalability for high transaction volumes, stand to benet signicantly. The use of monitoring, dynamic switching, and prioritization mechanisms ensures that resources are utilized effectively.

8. Conclusion and Future Work

In this paper, we have proposed a hybrid transaction management approach for distributed nancial systems that combines the strengths of various techniques, including SAGA, TCC, and dynamic transaction prioritization. By evaluating the nature and criticality of each transaction, our approach intel- ligently selects the most appropriate transaction management strategy, balancing performance, consistency, and scalability. The use of decision trees to compute transaction weights based on factors such as risk, importance, and transaction type enhances the systems adaptability and efciency. This framework provides an effective solution to the complexities of managing distributed transactions in microservices-based

nancial applications, ensuring that both high-priority and low-priority transactions are handled optimally. Future work can focus on expanding and rening the proposed hybrid transaction management approach. One potential avenue is the integration of blockchain technology to enhance transaction transparency and immutability. Blockchain could be utilized to securely record transaction logs, ensuring an immutable history of events, which would be especially useful for regulatory compliance and fraud detection. Additionally, blockchains inherent decentralization could contribute to more robust and fault-tolerant transaction management in distributed environ- ments. Another area for future research is the development of adaptive decision trees that evolve based on transaction patterns and system load, thereby enabling dynamic, time- varying optimization of transaction prioritization. Furthermore, research into machine learning models for predicting transac- tion failure risk and adjusting the decision-tree logic could further improve the systems efciency and resilience.

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