Concurrency Control in Fintech: How to Ensure Data Accuracy in Multi-User Systems

The term “concurrency” is defined as two or more things occurring or operating at the same time. In today’s fast-paced digital world, this term is applicable for almost any business process, especially in the financial sector. From having to conduct multiple transactions at the same time, to opening and closing accounts, managing customer services, and reconciling accounts, concurrency control is a key aspect of the software systems used. 

One of the most complex challenges financial institutions face is managing reconciliation and concurrency. Financial institutions handle thousands of transactions daily, which can lead to potential data inconsistencies and conflicts, as well as serious issues such as overdrawn accounts, mismatched reconciliations, and duplicated transactions. 

In this article, we break down what concurrency control is, its importance in database management, the challenges it presents, best practices, and real-world cases. 

What is Concurrency Control?

In the financial world, concurrency control refers to a process of managing and performing multiple data transactions simultaneously while maintaining data integrity and avoiding data losses or inconsistencies. In the world of banking, the stakes are incredibly high. Even minor inconsistencies and errors can result in significant financial losses, security breaches, regulatory issues, reconciliation errors, and reputational damage. This is where concurrency control comes in.

Importance of Concurrency Control in Database Management

Concurrency control is a central part of database management (or DBMS), allowing you to maintain the consistency and integrity of your institution’s financial and transactional data. It also allows multiple transactions to be conducted simultaneously without affecting one another. Without concurrency control, the following problems often happen:

• Dirty reads: This occurs when a particular transaction reads data modified by another that hasn’t been committed yet. This leads to inconsistencies and integrity issues in data and reconciliation. 
• Poor transaction management: Transactions must be durable (meaning once they have been committed or completed, they must persist even in case of system failures), and they must be atomic (meaning they have to be completed, and in case they fail, they must be rolled back entirely).
• Lost updates: This problem occurs when two or more transactions try to update the same data at the same time, resulting in inconsistent data. One of them may overwrite the other, resulting in data loss and inconsistencies. 

Understanding Concurrency in Multi-User Systems

In multi-user environments, there is a higher probability of overlapping data. For example, suppose two account holders in a bank want to update their information simultaneously. In that case, the database management system’s (DBMS) concurrency control ensures that these updates occur in the proper order and regulates only one update at a time. 

Concurrency control systems are designed to be scalable, reduce and recover from errors, and remain responsive and effective even under high volumes of transactions, such as during market hours or payroll processing. They are the backbone of modern banking infrastructure, ensuring all other financial processes and financial reconciliation remain accurate. 

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Concurrency Control Mechanisms in Fintech

For Fintechs, a lot of mechanisms can be used for effective concurrency control. Broadly, the most common are:

1. Lock-based Protocols: This protocol uses locks to control concurrent access to different data. Before accessing this data, transactions acquire locks. These are released after the operation has been completed.  

2. Timestamp-based Protocols: This assigns unique timestamps to each transaction. Transactions are then ordered based on their timestamps. This means smoother and more streamlined financial reconciliation, serializability, and other banking processes.  

ACID Properties and Their Role in Concurrency

ACID properties stand for “Atomicity, Consistency, Isolation, and Durability.” The ACID properties act as guidelines for how transactions should be processed to ensure database consistency, reliability, and predictability:

1. Atomicity

Atomicity, or ensuring that every transaction conducted is “atomic,” means that the entire transaction must be executed and completed fully or not at all. Put simply, it ensures that transactions are not partial. This avoids any partial updates and data that can lead to inconsistencies. 

2. Consistency

Consistency means that the financial institution’s database must be in a “valid” state both before and after a transaction. This guarantees that transactions only take the database from one valid state to the next. If not, the transaction must be rejected.  

3. Isolation

Isolation means ensuring multiple transactions can be conducted simultaneously without any inconsistencies in the database. They occur independently, that is, without interference from one another. This property guarantees that the result of running them concurrently would be the same as if they were run one by one.

4. Durability

Durability ensures that, after a transaction has been completed, any modifications and updates are stored in the database and persist even in the event of system failures. To put it simply, the updates are now permanent. 

Types of Concurrency Control

There are two primary kinds of concurrency control mechanisms. These include the following: 

1. Pessimistic Concurrency Control 

Known as PCC in short, pessimistic concurrency control operates on the assumption that most transactions will attempt to access the same resources simultaneously. This mechanism is then used to prevent concurrent access to the shared resource and “lock” the data before an operation is performed. However, this system does have certain drawbacks. For example, if a particular transaction acquires a lock on a specific resource and no other transactions can access it, it results in reduced concurrency overall. 

2. Optimistic Concurrency Control 

Shortened to “OCC,” this concurrency control mechanism functions on the assumption that zero (or very few) transactions will attempt to access the same resources simultaneously. Thus, it uses no locks and accesses for conflicts. There are four phases of OCC: 

• Read Phase: This is the initial stage of the transaction, where data is read and the timestamp is recorded. 
• Execution Phase: The execution phase then begins, during which the transaction performs any required operations. 
• Validation Phase: Before the transaction can be committed, a validation check is performed to verify that all data is consistent, timestamps match, etc. 
• Commit phase: Finally, in this phase, the transaction is either committed (or completed and saved) or entirely reversed, depending on whether the previous steps were successful. 

Techniques for Implementing Concurrency Control

Broadly, the mechanisms mentioned above can be applied to ensure your financial data remains secure, accurate, and updated: 

1. Locking protocols: This typically entails two-phase locking. Here, transactions can gain access to shared resources simultaneously without creating a deadlock. It uses lock acquisition, data modification, and release lock. This allows data to be updated without error, ensuring that reconciliation and other processes can run smoothly overall. 

2. Time Stamp Ordering protocols: Timestamps are tags that are assigned to any transaction that occurs. They have the specific time at which the transaction or operation occurred, and are crucial in many ways. It enables data to be ordered and tracked correctly, facilitating smoother reconciliation and operations. 

3. Multi-version Concurrency Control protocols: This protocol is exactly what it sounds like–multiple versions of a data item or transaction are kept. Each time a successful transaction or data write occurs, a new version of the data is saved. This can be tracked using timestamp protocols. 

Real-World Use Cases & Failures

Concurrency control is crucial for banks and financial institutions, particularly as digital technologies continue to advance rapidly and customer access to financial services expands. Here are times where concurrency control protocols have been implemented successfully and unsuccessfully: 

1. Successful Use Cases

When transactions and withdrawals are conducted, account balances must be preserved. An example of concurrency control in this case is pessimistic locking, which prevents two transactions from attempting to withdraw from an account simultaneously. This means that if a transaction is made, money should not be lost or duplicated, and the account balance remains accurate. Pessimistic locks mean if two people try to withdraw money from the same account, the locks will block the second transaction till the first one is completed. 

Reconciliation systems also use concurrency control. These software systems have to match millions of financial records from other banks, internal ledgers, and other partners. Concurrency control procedures, such as two-phase locking, isolation, and batch processing, ensure that data remains consistent and accurate. 

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2. Failures of Concurrency Controls 

Oftentimes, concurrency control protocols may be insufficient or may fail. For example, many have encountered problems when using digital wallets, where a transaction was charged twice. This could be due to inadequate locking protocols, isolation, system or network failures, overload on the system, or other related issues. If locking protocols fail, for example, data may be duplicated, or the system could hang. 

Challenges in Concurrency Control

There are specific challenges with concurrency control. These include:

• Complicated System: The complexity of database management systems increases due to the additional algorithms and control mechanisms required. 

• Slower Performance: Concurrency control checks may cause delays, resulting in slower performance in high-volume and multi-user systems. 

• Higher Costs and Resources: Concurrency controls often require complex systems, increased memory and CPU usage, and additional resources. This tends to drive up costs. 

• Maintenance: Concurrency controls also require regular debugging and have high maintenance requirements and costs. 

• Reconciliation Issues: High volumes of transactions, multiple data sources, and simultaneous use of systems means reconciliation can become challenging without concurrency controls that can handle them. 

Best Practices for Concurrency Control

Here are some steps institutions could take to ensure they get the most out of their concurrency control protocols: 

1. Determine Required Isolation Levels: Determining the correct isolation level means better performance and more accurate data. 

2. Choose a Locking Method: Determine the locks that your system needs. Too many can cause delays and cause the system to hang. Conversely, too few could mean inconsistencies in data. 

3. Regularly Monitor Transactions: Regularly monitoring transactions can help determine if the current isolation levels, locks, and other concurrency measures are working for you. In the event of errors or delays, these settings must be adjusted accordingly. 

How Can Osfin Help?

Concurrency controls can be complicated, but Osfin can help simplify them. Our reconciliation software platform supports your concurrency control requirements by ensuring that transactions are accurately matched and validated across multiple data sources, all in real-time. Our platform enables you to achieve 100% accuracy with advanced validation rules and enhanced data processing capabilities. We also offer rapid data computation, enabling instant data processing from start to finish. 

Osfin allows for seamless integration of data from multiple sources, utilizing over 170 pre-built connectors that easily integrate with any system, as well as 30M+ record scalability within just 15 minutes. Our high-speed agnostic platform ingests data from any source, parsing it into a structured, reconciliation-ready format. We support all file formats, including XML, MT940, CSV, JSON, and ISO 20022. The platform also allows you to handle millions of records across SWIFT, ACH transfer, and more, while working seamlessly with your ERP and CBS. 

Our platform also offers logic-based multi-source and partial transaction matching, auto-reconciliation for payment gateway reports, and transaction-level matching across ledgers, clearing files, ERPs, and bank statements. 

Automate your transaction matching, detect duplicates and outliers during data ingestion, tag any discrepancies, route exceptions in real-time, and enjoy live dashboards for exposure, exception queues, and match statuses.

We use a 256-bit SSL for encrypted data transfer, ensuring that your information is secure and protected. We help fortify your security with end-to-end encryption and role-based access, and stay compliant with PCI DSS, ISO 27001, SOC 2, and GDPR. 

Osfin’s no-code platform enables seamless integration with your existing systems, requiring minimal IT effort. We also provide a dedicated team of experts to guide you through the various processes and ensure the speedy resolution of any issues that may arise. 

These features help identify, address, and prevent duplicate entries or inconsistent balances, as well as other issues caused by simultaneous data updates. Osfin transforms complex, multi-user systems and bottlenecks into an automated, streamlined process with 100% accuracy. With built-in regulatory compliance and audit trails, it ensures your financial teams can manage and track concurrency controls without compromising data consistency. 

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FAQs on Concurrency Control

1. What is the reconciliation process in finance?

In finance, reconciliation refers to the process of matching and validating transactions across multiple data sources, including internal ledgers, external banking records, and other relevant sources. 

2. What is concurrency control with example?

Concurrency controls ensure multiple transactions and operations can be carried out while maintaining data accuracy. For example, if ATMs of Bank “A” did not have concurrency controls, numerous people would not be able to withdraw money from different locations simultaneously. 

3. What does concurrency control do?

Concurrency control ensures that multiple transactions and operations can be carried out simultaneously without interference. It also ensures that data accuracy is maintained throughout. 

4. What are the benefits of concurrency control?

Concurrency control offers numerous benefits, including preventing conflicts between simultaneous transactions, ensuring data accuracy, creating timestamps for accuracy and audit trails, and more. 

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