Engineering Sustainable Data Architectures for Modern Financial Institutions^†

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The paper has some interesting points and deals with a lot of different technologies. However, the scientific level of this study is weak and the novel contributions are not totally clear.

Improvement suggestions:

1. The benefits of big data and AI for financial organizations should be better explored in the Introduction section. Please consider also the use of more references to better support it.
2. Authors note that “Building on the results presented in [5]…” It is important to clarify in the Introduction section the approach and implications of this previous study.
3. Research questions are relevant but I miss some articulation with the research gap that was not totally explored.
4. Authors state “The following sections systematically review….” I do not like the “systematic” word because it gives the wrong idea that the authors have performed a systematic literature review.
5. Distinction between OLTP and OLAP should be performed.
6. Authors note that “Relational databases, big data, and cloud computing are the core technologies used by financial institutions…” How do you know that? Based in what kind of evidence?
7. Why are NoSQL databases important for financial organizations? For all kind of financial entities, to all kind of tasks…or to specific contexts?
8. Security concerns and GDPR should be better explored considering the specific context of financial organizations.
9. Support better the use of Random Forest model. Is it a common approach followed by other studies in the same field?
10. Authors state “MySQL, in particular, remains notable for its open-source licensing model…” this information is not currently correct. Check the differences between Maria DB and MySQL in terms of licensing.
11. Discussion of the results is very weak. Currently, the discussion is only supported in the technical interpretation of the results, which is not enough for a scientific paper. Authors need to discussion the relevance of their results considering previous studies and interpret the implications of their findings from a scientific perspective.
12. Conclusions section should better explore the practical contributions of this work.
13. What is a modern financial institution. This key concept is not enough explored and demonstrated.
14. Sustainable may be a key component, which is presented in the title and abstract. However, the entire paper ignores it. It is important to address the sustainable components of a software engineering solution.

Author Response

Dear reviewer 1,

Thank you very much for taking the time to review this manuscript. Please find the detailed responses below and the corresponding revisions. We tried to address all the concerns while trying to keep the overall size of the article in check.

Comment 1: The benefits of big data and AI for financial organizations should be better explored in the Introduction section. Please consider also the use of more references to better support it.

Response 1: We have expanded the introduction to better explore the benefits of big data and AI for financial organizations. We also added more references from the past 2 years.

Comment 2: Authors note that "Building on the results presented in [5]…" It is important to clarify in the Introduction section the approach and implications of this previous study.

Response 2: We clarified the differences: "Building on the results presented in [9], this extended research significantly expands the scope and depth of the analyzes on the technical and regulatory requirements of financial institutions, showing how technologies such as real-time streaming (Kafka) and distributed processing (Spark, Hadoop) can be orchestrated with standard relational systems in a multilayer hybrid architecture."

Comment 3: Research questions are relevant but I miss some articulation with the research gap that was not totally explored.

Response 3: We clarified the research gap in the Introduction before stating the RQs.

Comment 4: Authors state "The following sections systematically review…." I do not like the "systematic" word because it gives the wrong idea that the authors have performed a systematic literature review.

Response 4: We have rephrased that paragraph: "The following sections analyze the current literature on system integration, investigate practical implementation problems, especially with security and regulatory compliance, and offer an empirical evaluation of various platforms across diverse workloads.

Comment 5: Distinction between OLTP and OLAP should be performed.

Response 5: We have made this distinction within the "Relational Databases in Financial Services" subsection: "Relational databases have long been used for managing financial data, as they are capable of handling both Online Transaction Processing (OLTP), systems specialized in the efficient, real-time execution of transactional tasks such as order entries, payments, and account updates, and Online Analytical Processing (OLAP), systems designed to aggregate and analyze larger volumes of historical data to generate added value for business intelligence. Especially OLAP systems can be used during risk assessment and strategic decision making [14 –16 ]. Furthermore, state-of-the-art cloud native OLTP and OLAP databases offer storage layer consistency, compute layer consistency, multilayer recovery, and HTAP optimization [17,18].”

Comment 6: Authors note that "Relational databases, big data, and cloud computing are the core technologies used by financial institutions…" How do you know that? Based in what kind of evidence?

Response 6: We also restructured the Literature Review to better show how Relational databases, big data, and cloud computing are the core technologies used by financial institutions. We added more references to that claim.  

Comment 7: Why are NoSQL databases important for financial organizations? For all kind of financial entities, to all kind of tasks…or to specific contexts?

Response 7: We addressed this point within the restructured Literature Review, clarifying the specific contexts where NoSQL databases provide value to financial organizations. We added Table 1. Key NoSQL Use Cases in the Financial Sector.

Comment 8: Security concerns and GDPR should be better explored considering the specific context of financial organizations.

Response 8: In the Literature Review we added a subsection regarding GDPR and data security concerns.

Comment 9: Support better the use of Random Forest model. Is it a common approach followed by other studies in the same field?

Response 9: We have added additional justification for our use of the Random Forest model, including references to similar approaches in comparable financial technology studies: “We established testing environments for each technology and also integrated advanced machine learning techniques by employing a Random Forest model which is a well-established ensemble technique used for financial data analysis. Many researchers have compared Random Forest with other machine learning methods and have shown its effectiveness in providing reliable results in areas such as financial fraud detection [ 86], forecasting prices [87 , 88], and corporate financial performance [ 89– 91], as it is capable of handling high-dimensional and noise data. Its interpretability, through measures such as the importance of features, and its scalability across different data types further support its adoption in financial studies [92]. Moreover, it can reduce overfitting through bootstrap aggregation.”

Comment 10: Authors state "MySQL, in particular, remains notable for its open-source licensing model…" this information is not currently correct. Check the differences between Maria DB and MySQL in terms of licensing.

Response 10: We added a short comparison of licensing terms: " Of the Open Source DBs, PostgreSQL offers the most permissive BSD-style license, which comes with minimal restrictions [95]. Both MySQL and MariaDB use the GPLv2 license [96 , 97]. They are free to use within the company,  but distributing them with proprietary software creates copyleft obligations. So, PostgreSQL is the most flexible solution for enterprises that want to maximize license flexibility while reducing legal expense. MySQL provides a well-established dual-licensing option for those who are satisfied with GPL compliance or are prepared to pay for commercial licenses. MariaDB strikes a balance between strong open source commitments and commercial flexibility."

Comment 11: Discussion of the results is very weak. Currently, the discussion is only supported in the technical interpretation of the results, which is not enough for a scientific paper. Authors need to discussion the relevance of their results considering previous studies and interpret the implications of their findings from a scientific perspective.

Response 11: We have comprehensively expanded the discussion section to discuss our findings compared with other studies. We also included a table Connecting Performance Results to Financial Domain Applications.

Comment 12: Conclusions section should better explore the practical contributions of this work.

Response 12: We have expanded the conclusions section.

Comment 13: What is a modern financial institution. This key concept is not enough explored and demonstrated.

Response 13: We further explored this concept in the introduction, especially in the first three paragraphs.

Comment 14: Sustainable may be a key component, which is presented in the title and abstract. However, the entire paper ignores it. It is important to address the sustainable components of a software engineering solution.

Response 14: We have added a subsection regarding Sustainable Practices in the Literature Review and we also discussed these practices in the Discussion section.

Best regards,

The authors

Reviewer 2 Report

Comments and Suggestions for Authors

The article discusses the integration of relational databases, big data, and cloud computing in financial institutions, focusing on sustainability and energy efficiency. The topic is relevant and current.

The article is well-written, methodologically rigorous, and significantly contributes to the field. The practical approach and focus on sustainability make it valuable for professionals and academics.

The comprehensive literature review provides an overview of data integration in financial institutions.

The article provides robust and well-founded answers to the three research questions (RQ1, RQ2, and RQ3) presented in the introduction.

Methodologically, it uses structured, semi-structured, and unstructured financial data sets tested in specific environments (SQL, Python, Spark). Figure 1 clearly illustrates the methodological flow.

They compare the different technologies (relational databases, Big Data, and cloud computing) and identify their strengths and limitations.

The conclusions are well-founded and in line with the initial objectives, reinforcing the usefulness of the proposed architecture.

The article offers clear guidelines for financial institutions, such as the combination of SQL for transactions, Python for ML, and Spark for real-time analysis. The emphasis on sustainability and compliance is a plus.

It identifies limitations and future work, recognizing the need for testing in more complex environments and suggesting integration with blockchain and advanced AI models.

Congratulations! The article is very robust!

Author Response

Dear Reviewer 2,

Thank you very much for taking the time to review this manuscript. We thank you for your appreciation.

Best regards,

The authors

 

Reviewer 3 Report

Comments and Suggestions for Authors

This study proposes a four-layer architectural solution for financial data processing operational issues. Data ingestion, integration, processing, and storage are the architecture layers. The research topic is interesting and within the scope of the journal. However, the authors should address the following comments in their manuscript to increase the quality of the paper.

 

1. The research is guided by the following research questions:

RQ1: What are the main trends, challenges, and strategies in integrating relational databases, big data, and cloud computing in financial institutions, based on a systematic review of recent literature?

RQ2: How can financial institutions implement a hybrid cloud architecture that optimizes operational efficiency while ensuring compliance with EU data protection requirements in the context of EU-US data transfers?

RQ3: How do these technologies impact financial data management and analysis in terms of scalability, processing speed, and cost-effectiveness?

2. The main contribution of the study is to provide financial institutions with a detailed comparative analysis of relational databases, big data, and cloud computing, analyzing key metrics such as processing speed, resource usage, and scalability

3. In section 5.2: please elaborate more about the hardware and software configuration used in the performance testing stage of the proposed module.

4. In section 5.2: you have mentioned that you have analyzed the execution time, memory usage, and CPU utilization to provide a comprehensive evaluation framework that is particularly relevant to financial institutions. Please elaborate more about the selection of these factors and the possibility of the addition of other factors to maximize the test results.

5. The authors have discussed the major findings of the study. In addition, the future direction of research in the field has been discussed.

6. The limitations of the methodologies applied in the study have not been discussed.

7. The authors used appropriate references in the introduction section to present the research gap in the field.

8. Please include the digital object identifier (DOI) for all references where available.

9. In Figures 2, 3 and 7: please increase the font size

10. Figure 4: please correct the caption to “Cloud-Computing platforms”

11. Figure 5: please check the caption of the figure

12. Line “326”: you mention Table 6. Please correct the number of the table in the text

Author Response

Dear reviewer 3,

Thank you very much for taking the time to review this manuscript. Please find the detailed responses below and the corresponding revisions.

Comment 1: In section 5.2: please elaborate more about the hardware and software configuration used in the performance testing stage of the proposed module.

Response 1:  We've expanded Section 5.2 to include more specifics about the hardware and software used during testing. All performance tests were carried out on Google Colab, using an NVIDIA A100 GPU. We went with this setup because it's stable, easy to access, and allowed us to run consistent and reproducible tests without major overhead. The A100’s strong parallel processing capabilities made it a good fit for working with large volumes of structured, semi-structured, and unstructured data. To make sure the results were consistent, we repeated each test 50 times on datasets ranging from 100,000 to 1,000,000 records.

Comment 2: In section 5.2: you have mentioned that you have analyzed the execution time, memory usage, and CPU utilization to provide a comprehensive evaluation framework that is particularly relevant to financial institutions. Please elaborate more about the selection of these factors and the possibility of the addition of other factors to maximize the test results.

Response 2: In the revised section we added clarifications on our resoning behind our focused on execution time, memory usage, and CPU utilization. These made the most sense for our use case execution time shows how fast the system can respond, memory usage gives us an idea of how well resources are being managed (which matters for both cost and performance), and CPU load helps us catch any slowdowns when the system is under stress.

We also included a short note on how this evaluation could be expanded in future work. For example, looking at GPU usage might give more insight into how well the system takes advantage of hardware acceleration. Other useful metrics could include network latency, disk I/O speed, or even fault tolerance, especially for financial systems that rely on distributed or cloud-based infrastructure.

Comment 3: The limitations of the methodologies applied in the study have not been discussed.

Response 3: In the Conclusions we added a “Methodological Limitations” subsection.

Comment 4: Please include the digital object identifier (DOI) for all references where available.

Response 4: We have added DOIs for all the references where we could identify them.

Comment 5: In Figures 2, 3 and 7: please increase the font size

Response 5: We have redesigned the figures and increased the font size.

Comment 6: Figure 4: please correct the caption to “Cloud-Computing platforms

Response 6: We have corrected the caption.

Comment 7: Figure 5: please check the caption of the figure

Response 7: We have redesigned the figure and updated the caption of the figure.

Comment 8: Line “326”: you mention Table 6. Please correct the number of the table in the text

Response 8: We have corrected the references to the table and figure in section “ Integration Strategies”.

Best regards,

The authors

 

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The paper is solid and the revisions performed by the authors were very good.