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Decentralised Finance Analytics and AI-Driven Economic Forecasting in Digital Markets

 Info: Decentralised Finance Analytics and AI-Driven Economic Forecasting in Digital Markets Topics I phdassistance.com

Published: 15th July in Decentralised Finance Analytics and AI-Driven Economic Forecasting in Digital Markets Topics I phdassistance.com

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Introduction

The fast development of artificial intelligence and blockchain technologies is changing financial systems in the modern world because they allow for providing intelligent, transparent, and decentralised financial solutions. Innovations in digital technologies are used by financial organisations, investors, and policymakers to create new investment solutions, increase market transparency, and improve decisions in digital economies. The Decentralized Finance Analytics helps in analysing information that is gained from blockchain transactions and allows the use of predictive analytics in the process of economic decision-making. Nevertheless, there are still several problems that limit the adoption of digital technologies, such as the poor quality of data, the lack of regulations, interoperability issues, cybersecurity threats, and the lack of standard analytical approaches. It is necessary to design a proper framework that would unite artificial intelligence, blockchain technology, and predictive economic models.

Proposed PhD Topic 1: Artificial Intelligence-Driven Investment Intelligence Framework Integrating Blockchain Analytics for Enhanced Decision-Making in Decentralised Finance Ecosystems
Background Context:

The development of Decentralised Finance (DeFi) has changed the financial services industry due to the possibility of conducting peer-to-peer transactions via blockchain technology without the participation of financial intermediaries. At the same time, AI-Driven Forecasting is improving the forecasting ability of trends in the market, investment risks, and economic performance using machine learning and predictive modelling techniques. Rohan et al. (2026) noticed that artificial intelligence has greatly improved the financial market prediction process; however, most forecasting models remain focused on conventional financial markets rather than on the decentralised environment. In addition, the application of AI in Financial Analysis has many possibilities for improving the investment decision-making process, while Blockchain Analysis can provide reliable and immutable transaction information, improving the forecasting ability. Nevertheless, the lack of integration between forecasting models based on AI and financial data from the blockchain hinders the intelligent decision-making process in the Markets.

PhD-Level Verification:

The present research is conducted independently on AI forecast systems, blockchain, and DeFi applications. Very few studies focus on AI-Driven Economic Forecasting along with Blockchain Analysis to develop predictive analytics for decentralised finance systems. Moreover, empirical validation of the forecasting framework by using real DeFi transactions is not widely conducted, presenting a valuable doctoral research area.

Research Questions:
  • How can AI-Driven Forecasting enhance decision-making regarding investments in DeFi?
  • How can Blockchain Analysis be used to increase the accuracy of forecasts using AI-driven economic forecasts?
  • How does AI in financial analysis affect the efficiency of financial markets?
    • Contributions at the PhD-Level:
  • Development of a framework for predicting investments using AI in DeFi.
  • Integration of AI in Financial Analysis and Blockchain Analysis for financial intelligence.
  • Recommendations for policy and strategy in financial forecasting through AI.
    • Suggested Readings:

    Rohan, A., Hossen, M. D., Pranto, M. N., Hossain, B., Yoshi, A. M., & Islam, R. (2026). Artificial intelligence in financial market prediction: Advancements in machine learning for stock price forecasting. Frontiers in Artificial Intelligence, 8, Article 1696423. https://doi.org/10.3389/frai.2025.1696423 .

    AI-Driven Economic Forecasting
    Proposed PhD Topic 2: Integrated Blockchain Intelligence and Predictive Analytics Framework for Financial Risk Assessment in Decentralised Digital Finance Systems
    Background Context:

    The accelerated growth of DeFi has led to the development of new financial innovations, which are efficient and accessible, but they have raised concerns about increased market volatility, cyber risks, liquidity risks, and smart contract vulnerabilities. The use of AI in Financial Analytics has gained wide popularity due to its ability to detect fraud, make predictions, and assess financial risks because of the greater reliance on data in financial systems. According to Chaudhari (2025), artificial intelligence contributes towards the digital economy by improving financial intelligence and analysis. AI-Driven Forecasting assists in predicting economic trends and market fluctuations that affect financial stability. On the other hand, Blockchain Analysis offers accurate and transparent transaction data that can be used in predictive analytics in financial markets. There is no integrated approach using these technologies for decentralised financial risk prediction, thus offering a great opportunity for doctoral-level research.

    PhD-Level Verification:
    Current literature tends to focus on the use of blockchain technology for transparency or AI technology for forecasting. Not much consideration has been paid to the development of an Integrated Blockchain Analytics approach that continuously facilitates AI forecasting within a decentralised finance system.

    PhD-Level Verification:

    Current literature tends to focus on the use of blockchain technology for transparency or AI technology for forecasting. Not much consideration has been paid to the development of an Integrated Blockchain Analytics approach that continuously facilitates AI forecasting within a decentralised finance system.

    Research Questions:
  • How can Blockchain Analysis help improve economic forecasts in financial Markets?
  • What are the elements that affect the efficiency of the AI-based forecasting model using blockchain transactional data?
  • Can we improve the forecasting efficiency in DeFi by incorporating AI in Analytics with blockchain-based data?
    • PhD-Level Contributions:
  • Development of a blockchain analysis framework that is conducive to AI-driven forecasting.
  • Blockchain transaction intelligence integrated into financial analytics.
  • Recommendations for strategy in blockchain and decentralised finance environments.
    • Suggested Readings:

    Pierrò, A. (2025). The convergence of artificial intelligence and blockchain technology: Applications, challenges, and future directions in decentralised finance.

    Proposed Dissertation topic 3: Adaptive Framework for Predictive Financial Risk Mitigation and Market Stability in Decentralised Finance Ecosystems
    Background Context:

    The rapid growth of Decentralised Finance (DeFi) has brought new financial innovations that have made finance more accessible and efficient but at the same time made people more exposed to market fluctuations, cybersecurity challenges, liquidity problems, and vulnerabilities in smart contracts. With the growing complexity of finance in terms of the volume of data required for its proper function, AI in Analytics is getting popular as an instrument for improving fraud detection, predictive modelling, and financial risk evaluation. According to Chaudhari (2025), artificial intelligence is changing the digital economy through improved financial intelligence, financial analysis, and decision-making processes. Also, AI-Driven Forecasting helps to identify emerging economic trends and fluctuations in the market affecting financial security. At the same time, Blockchain Analysis gives trustworthy and transparent information on transactions, making predictive models more accurate in financial markets. Nevertheless, analytical tools using those technologies for decentralised financial risk prediction are still not developed.

    PhD Level Verification:

    Recent academic work is mostly based on AI-based financial predictions or risk management in Decentralized Finance (DeFi) separately. There is a lack of studies that combine both of these aspects into a predictive risk management framework using AI forecasting methods in combination with risk metrics implemented on blockchain technology.

    Research Questions:
  • How can AI in Analytics help improve risk predictions in DeFi?
  • Which blockchain metrics are most effective for AI-based economic forecasting in financial risk management?
  • How can predictive risk analytics affect the stability of the markets in DeFi?
    • PhD-Level Contributions:
  • Development of an AI-based framework for predicting financial risks in the field of DeFi.
  • Application of Blockchain Analysis to the development of financial risk prediction models.
  • Recommendations for enhancing the resilience of economies in decentralised financial environments.
    • Suggested Readings:

    Chaudhari, A. V. (2025). Reimagining finance with artificial intelligence: Smart technologies reshaping the digital economy. ESP Journal of Engineering & Technology Advancements, 5(2), 47–61. https://doi.org/10.56472/25832646/JETA-V5I2P107.

    Proposed Dissertation Topic 4: Intelligent Governance and Policy Decision Support Framework for Transparent and Sustainable Blockchain-Based Financial Ecosystems
    Background Context:

    Governance is essential in ensuring that DeFi is accountable, transparent, and sustainable because more decisions are being made through autonomous systems and smart contracts in DeFi. During the process of designing decentralised platforms, there is a need for governance mechanisms to be put into consideration. According to Naseer et al. (2025), artificial intelligence agents have become an important part of decentralised governance solutions, enhancing the coordination, interaction on markets, and value creation in blockchain ecosystems. Additionally, AI in Financial Analysis allows for the monitoring of the performance of the governance model, while Blockchain Analysis guarantees that the transaction records will be available and unalterable, making the organisation transparent. Also, AI-Driven Forecasting provides information about future economic developments and outcomes of governance in Digital Markets. However, integrated governance intelligence solutions have not yet been explored, which means that it is an area worth researching.

    PhD-Level Verification:

    Current studies focus on the topics of artificial intelligence governance, blockchain governance, and decentralisation in decision-making independently. Very few studies have investigated how governance intelligence from AI analytics can be used to enhance forecasting and governance performance at the same time.

    Research Questions:
  • In what different ways can AI improve Governance Intelligence in DeFi?
  • What is the part that Blockchain Analysis plays in improving governance intelligence and transparency?
  • Does governance intelligence improve AI Economic Forecasting in Markets?
    • Contributions at the PhD-Level:
  • Development of an AI-governance intelligence framework in DeFi ecosystems.
  • Application of governance analytics to AI-based economic prediction models.
  • Recommendations for decentralized governance and sustainable markets.
    • Suggested Readings:

    Naseer, M., Ali, A., & Audi, M. (2025). Autonomous artificial intelligence agents in decentralized finance: Governance, coordination, and value creation. Bulletin of Business and Economics, 15(1), 24–34.

    Proposed Dissertation Topic 5: Hybrid Machine Learning and Blockchain-Based Framework for Predictive Economic Intelligence and Financial Decision Support
    Background Context:

    The use of hybrid analytics in financial forecasting can be used to improve its results since they incorporate various intelligent technologies, which increase the accuracy of prediction and strategic decisions. AI-Driven Forecasting allows organisations to predict economic change using sophisticated machine learning and predictive analytics, while AI in Analytics helps organisations in making sound financial plans and performances and investments. As indicated by Zamil (2025), the adoption of hybrid AI-based decision support models has greatly increased the efficiency of forecasting, even though they have only been implemented within conventional businesses and finance. Besides, Blockchain Analysis has played an important role in providing reliable and up-to-date transaction information that improves the efficiency of the predictive modelling process. Nonetheless, the creation of hybrid forecasting models is insufficiently developed within Decentralised Finance (DeFi).

    PhD-Level Verification:

    There is currently enough scientific evidence about the forecasting ability of artificial intelligence in conventional finance; however, there is scarce scientific evidence about AI-blockchain forecasting models in decentralised finance systems. The lack of predictive model frameworks aimed at predicting decentralised financial markets presents a promising dissertation topic.

    Research Questions:
  • In what ways can hybrid AI models help improve AI-driven Forecasting in Decentralised Finance (DeFi)?
  • What is the contribution of Blockchain Analysis to economic forecasting in decentralised finance?
  • Does integration of AI in Analytics through blockchain improve economic forecasting for financial Markets?
    • PhD-Level Contributions:
  • Development of an AI blockchain-based prediction system for decentralised financial markets.
  • Incorporation of AI into Financial Analytics and Blockchain Analysis for predictive economic modelling.
  • Recommendations on the improvement of financial performance prediction in digital economies.
    • Suggested Readings:

    Zamil, M. H. (2025). AI-driven decision support models for SME financial forecasting: A systematic review and meta-analysis. Review of Applied Science and Technology, 4(2), 86–117. https://doi.org/10.63125/gjrpv442.

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