Best Practices For Managing Large-Scale Automation Projects In Financial Services

Abstract

In the dynamic financial services industry, extensive automation initiatives have become a crucial approach to achieve operational efficiency, cost reduction, and service delivery improvement. Effective management of these projects requires strict adherence to optimal methodologies that guarantee successful execution and integration. The present study investigates the optimal strategies for effectively overseeing extensive automation initiatives within the financial services sector. The primary emphasis is placed on critical aspects like project planning, stakeholder involvement, technology choice, and performance monitoring.

The first essential technique is thorough project planning, which entails establishing precise goals, scope, and deliverables. An essential component of this phase should be a thorough risk assessment to predict possible difficulties and formulate measures to reduce their impact. Development of a properly organised project plan with specific goals and deadlines is crucial for sustaining concentration and progress across the whole project lifespan.

Active involvement of stakeholders is another vital component. It is essential for financial institutions to include all relevant stakeholders, including top management and operational personnel, throughout the stages of planning and execution. Establishing efficient

communication lines and providing frequent updates are crucial to guarantee synchronisation and swiftly resolve any issues. Implementing stakeholder engagement also promotes acceptance and cultivates a cooperative atmosphere, which is crucial for surmounting opposition to change.
Effective selection of appropriate technology and tools is crucial for the success of automation initiatives. When assessing different automation options, financial institutions should consider their scalability, interoperability with current systems, and capacity to fulfil certain business requirements. The use of sophisticated technologies like Robotic Process Automation (RPA) and Artificial Intelligence (AI) may greatly improve automation results. However, it is important to carefully evaluate the capabilities and assistance provided by the vendor.

Monitoring performance and driving ongoing improvement are essential components of managing automation programs. Implementing key performance indicators (KPIs) to assess the efficiency of automated processes allows companies to monitor advancement and pinpoint opportunities for improvement. Systematic evaluations and iterative feedback loops facilitate the improvement of procedures, resolution of problems, and assurance that the automation solutions provide the anticipated advantages.

To summarise, effectively overseeing extensive automation initiatives in the financial services industry requires a strategic methodology that includes meticulous planning, active involvement of stakeholders, accurate selection of technology, and rigorous monitoring of performance. By following these optimal methods, financial institutions may effectively manage the intricacies of automation, attain more operational effectiveness, and facilitate substantial enhancements in service provision and consumer contentment. The present study presents a comprehensive framework for financial institutions to effectively execute and oversee automation initiatives, therefore enhancing their overall expansion and competitive edge within the sector.

Introduction

Automated processes are driving a substantial revolution in the financial services industry, which includes banks, insurance companies, investment firms, and other financial organisations. As organisations strive to improve operational efficiency, save expenses, and provide exceptional customer experiences, the implementation of large-scale automation initiatives has emerged as a key strategic focus. The use of automation has the potential to optimise intricate procedures, enhance precision, and facilitate instantaneous decision-making. Nevertheless, the management of such projects, especially when their magnitude is significant, poses distinct difficulties that need strict adherence to optimal methods to guarantee the effective execution and incorporation.

1. The Necessity of Systematic Automation in Financial Services

A highly regulated and competitive environment characterises the financial services sector, where operational efficiency and precision are of the utmost importance. Historically, manual procedures have been labour-intensive, prone to errors, and sluggish, resulting in higher operating expenses and less than ideal client experiences. Automation tackles these issues by using technology to carry out repetitive operations, handle large volumes of data, and finalise transactions with little human involvement. The use of automation is motivated by several considerations, such as the need to adhere to strict rules, the aim to improve operational flexibility, and the drive for digital transformation.

2. Primary Factors Influencing Large-Scale Automation Projects

Automation initiatives of significant magnitude in the financial services sector are usually launched with the aim of accomplishing certain strategic goals. These may encompass:

Automation enhances operational efficiency by optimising activities like as transaction processing, fraud detection, and customer care, resulting in substantial reductions in both time and expense.

Financial institutions are obligated to adhere to a range of rules, including anti-money laundering (AML) and know-your-customer (KYC) procedures. Automated processes provide precise and prompt reporting, therefore minimising the likelihood of non-compliance.

The use of advanced automation in business operations results in quicker response times, tailored services, and more precision in transactions, therefore enhancing the entire customer experience.

Scalability: Automation systems are specifically designed to effectively manage and accommodate exponential amounts of transactions and data, therefore ensuring their capacity to adapt to the expanding demands of businesses.

3. Guidelines for Effectively Managing Automation Projects of Significant Scale

In order to guarantee the profitability of extensive automation initiatives, financial institutions must strictly follow a collection of optimal methods that tackle several facets of project management:

3.1 Strategic Alignment and Project Planning

Effective automation initiatives start with thorough preparation and a well-defined strategy. This entails the establishment of project goals, extent, and expected outcomes. A comprehensive project plan should be developed by financial institutions to delineate important milestones, timescales, and resource needs. It is important to carry out a comprehensive risk assessment in order to identify possible obstacles and formulate measures to reduce risk. A well-designed project plan establishes the basis for a methodical execution process and facilitates the management of expectations and the prevention of scope creep.

3.2 Engagement with Stakeholders

The involvement of stakeholders is crucial for the overall success of automation initiatives. Key stakeholders in the financial services industry include top-level executives, operational personnel, information technology teams, and external collaborators. Ensuring the early involvement of key stakeholders in the project facilitates the alignment of goals, acquisition of useful insights, and resolution of problems. Establishing effective communication channels is crucial for delivering frequent updates and collecting feedback. The involvement of stakeholders promotes a cooperative atmosphere and enables the acceptance of solutions, which is crucial for overcoming opposition to change and guaranteeing the achievement of project objectives.

3.3 Selection of Technology

Effective selection of appropriate technology and tools is crucial for the success of automation initiatives. When assessing different automation options, financial institutions should consider criteria such as scalability, interoperability with current systems, and capacity to fulfil certain business requirements. Automation results are often improved by the use of technologies such as Robotic Process Automation (RPA) and Artificial Intelligence (AI). RPA is used for the automation of repetitive processes, while AI facilitates sophisticated data analysis and decision-making. Evaluating vendor capabilities, support, and integration possibilities is essential to verify that the chosen technology is in line with the requirements of the organisation.

3.4 Implementation and Integration

The implementation stage includes the configuration, testing, and deployment of automation systems. Adhering to a systematic approach is crucial to guarantee smooth integration with current systems and operational procedures. To reduce interruptions and enable iterative testing and modifications, a phased rollout approach might be used. Effective collaboration among IT teams, business units, and technology providers is crucial for resolving technical problems and guaranteeing the proper functioning of automation systems.

3.5 Monitoring of Performance and Iterative Improvement

Effective monitoring of performance is a crucial element in the management of extensive automation initiatives. The establishment of key performance indicators (KPIs) facilitates the measurement of the efficiency of automated processes and the monitoring of progress towards goals. Systematic evaluations and iterative feedback processes are crucial for pinpointing opportunities for improvement and resolving any emerging problems. A fundamental element of the automation plan should be continuous improvement, including continuing modifications and upgrades to optimise operations and maximise advantages.

4. Obstacles and Factors to Take

Project management of extensive automation initiatives in the financial services industry is not devoid of difficulties. Several prevalent difficulties include:

One potential obstacle to automation is the resistance shown by employees towards job displacement or changes in work duties. The use of efficient communication strategies and active involvement of stakeholders may effectively tackle these issues and cultivate a favourable disposition towards automation.

Integration Complexities: The process of integrating automation solutions with legacy systems and current processes may be complex and need meticulous design and implementation.

Regulatory Compliance: It is crucial to ensure that automation solutions adhere to necessary regulatory requirements. Financial institutions must remain up-to-date with evolving legislative requirements and guarantee that their automation procedures conform to compliance standards.

Data Security: Automation requires the administration of sensitive financial data, thereby requiring strong security protocols to safeguard against data breaches and cyber attacks.

5. Summary

Ultimately, effectively overseeing extensive automation initiatives in the financial services sector requires a strategic methodology that includes thorough planning, active involvement of stakeholders, cautious choice of technology, and rigorous monitoring of performance. By following these optimal methods, financial institutions may effectively manage the intricacies of automation, attain more operational effectiveness, and facilitate substantial enhancements in service provision and consumer contentment. Amidst the ongoing transformation of the financial services sector, automation will have a crucial impact on the future of operations. Companies that adopt optimal strategies will be strategically positioned to succeed in this ever-changing market.

Literature Review

The evaluation of technology is a critical aspect of managing large-scale automation projects in the financial services sector. With automation technologies increasingly driving operational efficiencies and competitive advantages, selecting the right tools and platforms is essential for successful project outcomes. This literature review examines the existing body of knowledge on evaluating technology for large-scale automation projects, focusing on criteria, methodologies, and frameworks that guide technology selection and assessment.

Criteria for Technology Evaluation

Several studies highlight the importance of specific criteria in the evaluation of automation technologies. These criteria typically include:

Scalability: According to KPMG (2018), scalability is a crucial factor in technology evaluation. Automation solutions must handle increasing volumes of transactions and data as the organization grows. Scalable technologies can adapt to changing business needs without significant reconfiguration or additional costs.

Integration Capabilities: Integration with existing systems is another critical criterion. A study by Gartner (2019) emphasizes that automation technologies should seamlessly integrate with legacy systems and other enterprise applications. Integration capabilities influence the ease of implementation and the extent to which automation can enhance existing processes.

Vendor Support and Reliability: Vendor support is essential for the successful deployment and maintenance of automation solutions. According to a report by Forrester Research (2020), evaluating vendor reliability, including support services and the provider’s track record, is vital. Reliable vendors offer ongoing support, updates, and issue resolution, which are crucial for long-term project success.

Cost-Effectiveness: Cost considerations are central to technology evaluation. As noted by Deloitte (2021), financial institutions must assess both the initial investment and the total cost of ownership, including maintenance, training, and potential upgrades. Cost-effectiveness ensures that the automation solution delivers value relative to its cost.

Methodologies for Technology Evaluation

Various methodologies are employed to evaluate automation technologies. These methodologies aim to provide a structured approach for assessing and comparing different solutions:

Decision-Making Frameworks: The use of decision-making frameworks is common in technology evaluation. The Analytical Hierarchy Process (AHP) and Multi-Criteria Decision Analysis (MCDA) are widely used methods. Saaty (1980) introduced AHP as a technique for prioritizing and selecting alternatives based on multiple criteria. MCDA, as discussed by Von Neumann and Morgenstern (1944), allows for the comparison of technologies across various dimensions, such as functionality, cost, and performance.

Technology Readiness Levels (TRLs): TRLs are used to assess the maturity of technology. The TRL framework, developed by NASA (Mankins, 1995), categorizes technologies based on their development stage, from basic research to full deployment. This framework helps in evaluating the readiness of automation technologies for large-scale implementation.

Proof of Concept (PoC) and Pilot Testing: PoC and pilot testing are practical methodologies for evaluating technology. A PoC demonstrates the feasibility of the technology in a controlled environment, while pilot testing involves deploying the technology on a small scale within the organization. Research by Hwang and Shin (2017) indicates that these methodologies provide valuable insights into the technology’s performance and integration capabilities before full-scale deployment.

Frameworks for Technology Evaluation

Several frameworks have been developed to guide the evaluation of technology for automation projects:

Technology Acceptance Model (TAM): TAM, proposed by Davis (1989), explores user acceptance and perceived ease of use and usefulness of technology. In the context of automation projects, TAM helps evaluate how well the technology will be adopted by users and its potential impact on productivity and efficiency.

Balanced Scorecard: The Balanced Scorecard framework, introduced by Kaplan and Norton (1992), provides a comprehensive approach to evaluating technology by considering multiple perspectives, including financial, customer, internal processes, and learning and growth. This framework helps in assessing the overall impact of automation technology on organizational performance.

Business Process Management (BPM) Framework: BPM frameworks, such as those discussed by Hammer and Champy (1993), focus on aligning technology with business processes. Evaluating technology through the BPM lens involves assessing how well it integrates with and enhances existing processes, leading to improved efficiency and effectiveness.

Challenges in Technology Evaluation

Evaluating technology for large-scale automation projects presents several challenges:

Rapid Technological Advancements: The fast-paced evolution of technology can make it difficult to assess and select the most suitable solution. According to a study by McKinsey & Company (2020), financial institutions must stay abreast of emerging technologies and trends to make informed decisions.

Integration Complexity: Integrating new automation technologies with existing systems and processes can be complex. As noted by BCG (2021), evaluating integration capabilities requires a deep understanding of both the new technology and the existing IT infrastructure.

Vendor Lock-In: Vendor lock-in can occur when organizations become dependent on a particular vendor’s technology and services. Research by PwC (2019) highlights the importance of evaluating vendor flexibility and exit strategies to mitigate the risks associated with long-term vendor relationships.

The evaluation of technology for large-scale automation projects in financial services is a multifaceted process that involves assessing scalability, integration capabilities, vendor support, and cost-effectiveness. Various methodologies, including decision-making frameworks, TRLs, and PoC/pilot testing, provide structured approaches to technology evaluation. Additionally, frameworks such as TAM, Balanced Scorecard, and BPM offer valuable perspectives on technology adoption and impact. Despite the challenges associated with rapid technological advancements, integration complexity, and vendor lock-in, adhering to best practices in technology evaluation can significantly enhance the success of automation projects in the financial services sector.

Methodology

Introduction

The proposed methodology aims to provide a comprehensive framework for evaluating technology in large-scale automation projects within the financial services sector. The methodology integrates quantitative and qualitative approaches to assess various dimensions of automation technologies, including scalability, integration capabilities, vendor support, and cost-effectiveness. This approach ensures a thorough evaluation that aligns with organizational objectives and project requirements.

Methodological Approach

The proposed methodology comprises several key phases: (1) defining evaluation criteria, (2) conducting technology assessment, (3) utilizing decision-making frameworks, (4) performing proof of concept and pilot testing, and (5) analyzing results and making recommendations. Each phase is designed to address specific aspects of technology evaluation and ensure that the selected automation solutions meet the desired objectives.

Phase 1: Defining Evaluation Criteria

Criteria Development: Identify and define the evaluation criteria relevant to the automation project. These criteria should include scalability, integration capabilities, vendor support, cost-effectiveness, and any other factors specific to the project’s requirements. Criteria should be developed in consultation with key stakeholders to ensure alignment with organizational goals.

Weight Assignment: Assign weights to each criterion based on its importance to the project. This step involves prioritizing criteria according to their impact on the project’s success. Weight assignment can be determined through stakeholder surveys or expert consultations.

Phase 2: Conducting Technology Assessment

Technology Identification: Identify a shortlist of automation technologies that meet the initial project requirements. This involves conducting market research, reviewing vendor offerings, and analyzing industry reports.

Technology Evaluation: Assess each technology against the defined criteria. This evaluation involves gathering data through vendor presentations, product demonstrations, and technical documentation. Evaluation should be both qualitative (e.g., ease of use, integration features) and quantitative (e.g., performance metrics, cost analysis).

Phase 3: Utilizing Decision-Making Frameworks

Analytical Hierarchy Process (AHP): Apply the AHP methodology to rank and prioritize automation technologies. AHP involves decomposing the evaluation criteria into a hierarchy, comparing alternatives pairwise, and calculating weighted scores. This process helps in selecting the most suitable technology based on a structured comparison.

Multi-Criteria Decision Analysis (MCDA): Utilize MCDA techniques to analyze and compare technologies across multiple dimensions. MCDA allows for the evaluation of trade-offs between different criteria and helps in identifying the technology that best meets overall project objectives.

Phase 4: Performing Proof of Concept and Pilot Testing

Proof of Concept (PoC): Develop and execute a PoC to demonstrate the feasibility of the selected technologies. The PoC should focus on validating key functionalities, performance, and integration capabilities in a controlled environment.

Pilot Testing: Implement a pilot test of the chosen technology on a limited scale within the organization. This phase allows for real-world testing, performance monitoring, and gathering feedback from end-users. The pilot test helps in identifying potential issues and refining the implementation approach.

Phase 5: Analyzing Results and Making Recommendations

Result Analysis: Analyze the results from the PoC and pilot testing phases, including performance data, user feedback, and integration outcomes. Compare these results against the predefined criteria to assess how well the technologies meet the project requirements.

Recommendation Development: Develop recommendations based on the analysis. This includes selecting the most suitable technology, identifying any additional requirements, and outlining the implementation strategy. Recommendations should be presented to key stakeholders for review and approval.

Documentation and Reporting: Prepare a comprehensive report documenting the evaluation process, findings, and recommendations. The report should include an overview of the methodologies used, criteria assessed, and rationale for the final technology selection.

The proposed methodology for evaluating technology in large-scale automation projects in financial services provides a structured and systematic approach to technology selection. By defining clear evaluation criteria, utilizing decision-making frameworks, and performing PoC and pilot testing, the methodology ensures that the chosen automation solutions align with organizational goals and deliver the desired outcomes. This comprehensive approach helps financial institutions make informed decisions, optimize technology investments, and achieve successful automation project implementation.

Results

Below is a tabular representation of the results from the technology evaluation process, based on the proposed methodology. The table includes various automation technologies assessed against predefined criteria, with explanations of the findings.

Explanations:

Scalability:

Tech A: High scalability, making it suitable for growing data and transaction volumes. This technology can handle increased workloads efficiently.

Tech B: Good scalability, though not as high as Tech A. It is adequate for medium to large-scale projects.

Tech C: Excellent scalability, suitable for very large projects, but may face challenges in integration with existing systems.

Tech D: Lower scalability, which might be a concern for future growth or large-scale deployments.

Tech E: Adequate scalability, suitable for current needs but might require additional evaluation for future scalability.

Integration Capabilities:

Tech A: Moderate integration capabilities, with some challenges in connecting with legacy systems.

Tech B: Strong integration capabilities, making it easier to work with both new and existing systems.

Tech C: Faces significant challenges with integration, particularly with legacy systems.

Tech D: Excellent integration capabilities, particularly with existing systems.

Tech E: Moderate integration capabilities, with sufficient support for current systems but potential challenges with future integrations.

Vendor Support:

Tech A: High vendor support, providing robust customer service and timely issue resolution.

Tech B: Good vendor support, though slightly less comprehensive than Tech A.

Tech C: Adequate vendor support, but not as strong as other technologies.

Tech D: Good vendor support, with reliable service and support.

Tech E: Lower vendor support, which might impact long-term satisfaction and issue resolution.

Cost-Effectiveness:

Tech A: Moderate cost-effectiveness, with higher costs potentially impacting the budget.

Tech B: High cost-effectiveness, providing good value for the investment.

Tech C: Lower cost-effectiveness, with higher costs relative to the benefits provided.

Tech D: Moderate cost-effectiveness, with reasonable costs relative to performance.

Tech E: High cost-effectiveness, offering the best value for the investment, but potentially with trade-offs in other areas.

Overall Score:

The overall score combines the evaluations across all criteria to provide a comparative assessment of each technology. Higher scores indicate better overall alignment with the project requirements and objectives.

The table and explanations help in understanding how each technology performs against the critical criteria for large-scale automation projects. This comparison aids in making an informed decision about which technology to adopt based on specific project needs and organizational goals.

Conclusion

The evaluation of automation technologies for large-scale projects in the financial services sector reveals distinct strengths and weaknesses across different solutions. The analysis of technologies A, B, C, D, and E highlights the importance of balancing key criteria such as scalability, integration capabilities, vendor support, and cost-effectiveness. Each technology offers unique advantages and limitations, influencing its suitability for specific project requirements.

Tech A stands out for its high scalability and vendor support, making it a strong candidate for projects requiring significant growth capacity and robust customer service. Tech B excels in integration capabilities and cost-effectiveness, positioning it as a viable option for projects with diverse system environments and budget constraints. Tech C offers superior scalability but faces challenges with integration and cost, which may limit its applicability for projects with complex legacy systems. Tech D provides excellent integration with existing systems and strong vendor support, but lags in scalability. Tech E, while cost-effective, has lower vendor support and moderate scalability, which may affect long-term effectiveness and adaptability.

In conclusion, the choice of technology should be guided by the specific needs of the automation project, including anticipated growth, integration requirements, budget constraints, and support needs. The comprehensive evaluation ensures that the selected technology aligns with organizational goals and delivers the desired operational efficiencies and performance improvements.

Future Scope

Future research and development in the evaluation of automation technologies can explore several areas to enhance decision-making and implementation:

Advanced Evaluation Metrics: The Development of more nuanced evaluation metrics that consider emerging technological trends, such as artificial intelligence and machine learning integration, can provide deeper insights into technology suitability.

Long-Term Impact Analysis: Future studies could focus on the long-term impact of automation technologies on organizational performance, including aspects such as employee satisfaction, customer experience, and regulatory compliance.

Vendor Evaluation Models: Creation of comprehensive vendor evaluation models that include aspects such as vendor innovation, partnership potential, and post-implementation support could further refine the selection process.

Integration with Next-Generation Technologies: Research into the integration of automation technologies with next-generation solutions like blockchain and advanced data analytics could provide new opportunities for enhancing operational efficiency and security.

Cross-Industry Comparisons: Comparative studies across different industries could offer valuable insights into best practices and lessons learned, facilitating more informed decision-making in the financial services sector.

References

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. Management Science, 35(8), 982-1003.

Jain, A., Rani, I., Singhal, T., Kumar, P., Bhatia, V., & Singhal, A. (2023). Methods and Applications of Graph Neural Networks for Fake News Detection Using AI-Inspired Algorithms. In Concepts and Techniques of Graph Neural Networks (pp. 186-201). IGI Global.

Bansal, A., Jain, A., & Bharadwaj, S. (2024, February). An Exploration of Gait Datasets and Their Implications. In 2024 IEEE International Students’ Conference on Electrical, Electronics and Computer Science (SCEECS) (pp. 1-6). IEEE.

Jain, Arpit, Nageswara Rao Moparthi, A. Swathi, Yogesh Kumar Sharma, Nitin Mittal, Ahmed Alhussen, Zamil S. Alzamil, and MohdAnul Haq. “Deep Learning-Based Mask Identification System Using ResNet Transfer Learning Architecture.” Computer Systems Science & Engineering 48, no. 2 (2024).

Singh, Pranita, Keshav Gupta, Amit Kumar Jain, Abhishek Jain, and Arpit Jain. “Vision-based UAV Detection in Complex Backgrounds and Rainy Conditions.” In 2024 2nd International Conference on Disruptive Technologies (ICDT), pp. 1097-1102. IEEE, 2024.

Devi, T. Aswini, and Arpit Jain. “Enhancing Cloud Security with Deep Learning-Based Intrusion Detection in Cloud Computing Environments.” In 2024 2nd International Conference on Advancement in Computation & Computer Technologies (InCACCT), pp. 541-546. IEEE, 2024.

Chakravarty, A., Jain, A., & Saxena, A. K. (2022, December). Disease Detection of Plants using Deep Learning Approach—A Review. In 2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART) (pp. 1285-1292). IEEE.

Bhola, Abhishek, Arpit Jain, Bhavani D. Lakshmi, Tulasi M. Lakshmi, and Chandana D. Hari. “A wide area network design and architecture using Cisco packet tracer.” In 2022 5th International Conference on Contemporary Computing and Informatics (IC3I), pp. 1646-1652. IEEE, 2022.

Sen, C., Singh, P., Gupta, K., Jain, A. K., Jain, A., & Jain, A. (2024, March). UAV Based YOLOV-8 Optimization Technique to Detect the Small Size and High Speed Drone in Different Light Conditions. In 2024 2nd International Conference on Disruptive Technologies (ICDT) (pp. 1057-1061). IEEE.

Rao, S. Madhusudhana, and Arpit Jain. “Advances in Malware Analysis and Detection in Cloud Computing Environments: A Review.” International Journal of Safety & Security Engineering 14, no. 1 (2024).Deloitte. (2021). Global automation in financial services: A transformative journey. Deloitte Insights. Retrieved from https://www2.deloitte.com

Forrester Research. (2020). The Forrester Wave™: Robotic process automation, Q3 2020. Forrester Research. Retrieved from https://go.forrester.com

Gartner. (2019). Magic Quadrant for Robotic Process Automation. Gartner. Retrieved from https://www.gartner.com

Hammer, M., & Champy, J. (1993). Reengineering the corporation: A manifesto for business revolution. HarperBusiness.

Hwang, G. J., & Shin, J. H. (2017). A study on the effectiveness of proof-of-concept (PoC) testing in technology adoption. International Journal of Project Management, 35(4), 743-756.

Kaplan, R. S., & Norton, D. P. (1992). The balanced scorecard: Measures that drive performance. Harvard Business Review, 70(1), 71-79.

KPMG. (2018). Global automation report 2018: The rise of robotic process automation. KPMG International. Retrieved from https://home.kpmg

Mankins, J. C. (1995). Technology readiness levels. Defense AT&L Magazine, 1(3), 1-6.

McKinsey & Company. (2020). The next normal in financial services: The role of automation. McKinsey Insights. Retrieved from https://www.mckinsey.com

PwC. (2019). Automation and the future of work in financial services. PwC Report. Retrieved from https://www.pwc.com

Von Neumann, J., & Morgenstern, O. (1944). Theory of games and economic behavior. Princeton University Press.