A Reference Architecture Model for Big Data Systems in the Finance Sector (John Soldatos & Dimosthen Kyriazis, 2024)

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  • BOOST 4.0 five domains
    • Networked commissioning and engineering
    • Cognitive production planning
    • Autonomous production automation
    • Collaborative manufacturing networks
    • Full equipment and product availability
  • Simplifying and Accelerating Data Pipelines in Digital Finance and Insurance Applications
  • Architectural Patterns for Data Pipelines in Digital Finance and Insurance Applications
  • Semantic Interoperability Framework for Digital Finance Applications
    • Physical/technical interoperability: It is concerned with the physical connection of hardware and software platforms.
    • Syntactical interoperability: It is concerned with data format, i.e., it relates on how the data are structured.
    • Semantic interoperability: It is concerned with the meaningful interaction between systems, devices, components, and/or applications.
    • Organizational interoperability: It is concerned with the way organizations share data and information
  • Methologies
    • Methontology
      • Specification
      • Conceptualisation
      • Formalization
      • Integration
      • Implementation
      • Maintenance
    • Simplified Agile Methodology for Ontology Development (SAMOD) 
      • Test case definition
      • Merging current model with modelet
      • Refactoring current model
    • Distributed, loosely controlled, and evolving engineering of ontologies (DILIGENT)
      • Build
      • Local adaptation
      • Analysis
      • Revision
      • Local Update
    • Lightweight unified process for ontology building (UPON Lite) 
      • Domain terminology
      • Domain glossary
      • Taxonomy
      • Predication
      • Parthood
      • Ontology
  • INFINITECH
    • Identifying potential data sources and data owners
    • Assigning roles and responsibilities to data management processes
    • Defining the granularity of the data according to the type of applications needed to deliver it
    • Alignment with the overall reference architecture
    • Defining schema identification requirements and protocol standards for common data formats
    • Developing methodologies and best practices for modeling data, developing ontologies, defining business glossaries, etc.
    • Setting principles for data onboarding and consumption
  • Towards Optimal Technological Solutions for Central Bank Digital Currencies
    • CBDC
    • Convertible
    • Convenient
    • Accepted
    • Low cost
    • Secure
    • Instant
    • Resilient
    • Available
    • High throughput
    • Scalable
    • Interoperable
    • Flexible and adaptable
  • Historic Overview and Future Outlook of Blockchain Interoperability
    • Through anchoring and pegged sidechains
  • Efficient and Accelerated KYC Using Blockchain Technologies
    • Focusing on KYB and KYC
  • Leveraging Management of Customers’ Consent Exploiting the Benefits of Blockchain Technology Towards Secure Data Sharing
    • Consent management lifecycle
    • Consent collection
    • Consent storage
    • Consent usage
    • Consent update
    • Opt out
  • Addressing Risk Assessments in Real-Time for Forex Trading
  • Modern Portfolio Theory (MPT) 
  • Value at Risk (VaR)
    • Parametric - Variance-Covariance (VC) 
    • Non-parametric - Historical Simulation (HS)
    • Semi-parametric -Monte Carlo (MC)
    • Semi-parametric with RNNs
  • Next-Generation Personalized Investment Recommendations
    • Data Preparation Principles
      1. Discovering and Accessing Data 
      2. Profiling and Assessing Data
      3. Cleaning and Validating Data 
      4. Transforming Data 
      5. Anonymizing Data 
      6. Enriching Data 
      7. Storing Data 
    • Infinitech Way
      1. Data Retrieval 
      2. Data Mapper 
      3. Data Cleaner 
      4. Data Anonymizer
  • Personalized Portfolio Optimization Using Genetic (AI) Algorithms
    • Data requirements
      • Financial market price data fetched from several market data providers
      • Financial asset master data fetched from several data providers
      • Customer risk profile data fetched directly from the financial institution
      • Mutual fund, ETF and structured product allocation and breakdown data fetched from several market data providers
      • Individual data points received from financial institutions and maintained as customer-specific data points
      • Customer economic outlook fetched directly from financial institutions based on
      • questionnaires and customer (risk) profiles
    • Risk/Return-Related Fitness Factors
      • Volatility-capped performance factor minimizes the difference between the return of the recommended portfolio and the target return of the client under the condition that the recommended portfolio has a volatility that is not higher than the risk limit. The risk limit (in %) is in most cases linked to the risk level.
      • Portfolio volatility factor measures the risk of the recommended portfolio; it minimizes the volatility of the recommended portfolio compared to the current or defined model portfolio.
      • Portfolio performance factor measures the performance of the recommended portfolio; it maximizes the return of the recommended portfolio.
      • Sharpe ratio factor measures the Sharpe ratio of the target goal allocation portfolio.
    • Portfolio Constraint-Related Fitness Factors
      • Product diversification factor measures is the diversification of different financial products within a portfolio.
      • Reuse of existing asset factor measures reuse of the investor’s existing holdings for the new portfolio. The objective of that factor would be to reduce turnover of holdings in the customer’s portfolio and primarily define whether an asset should be kept within a customer’s portfolio. The so-called frozen assets can be completely excluded from the optimization calculations.
      • Preferred asset factor measures the use of preferred assets (premier funds or other defined preferences) in the recommended portfolio.
      • FX minimization factor minimizes the foreign exchange (FX) exposure in imple- menting the recommended portfolio by reducing FX transactions. It measures for each currency (cash and securities denominated in these currencies) in the recommended portfolio what percentage of such currency is already in the existing portfolio.
      • Preferred currency factor measures the extent of the recommended portfolio that complies with the investor’s preference for the investment currency.
    • Asset Characteristics’ Related Fitness Factors
      • Asset class allocation factor measures how well the recommended portfolio follows a given target portfolio/target allocation with respect to selected or predefined asset classes.
      • Sustainability/ESG allocation factor measures how well the recommended portfolio follows a given target portfolio/target allocation with respect to ESG/sustainability criteria/rating.
      • Region allocation factor measures how well the recommended portfolio follows a given target portfolio/target allocation with respect to specified regional preferences based on asset breakdowns.
      • ETF allocation factor measures how well the recommended portfolio follows a given target portfolio/target allocation with respect to ETFs as a “low-cost” investment product selection.
      • Core/satellite allocation factor measures how well the recommended portfolio follows a given target portfolio/target allocation with respect to assets tagged as core investments or satellite investment products.
      • Sentiment factor measures how well the recommended portfolio follows a given target portfolio/target allocation with respect to a sentiment dataset or criteria/rating.
  • Analyzing Large-Scale Blockchain Transaction Graphs for Fraudulent Activities

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