Calculation of Risk using Big Data

Technology Reply applies Big Data to the quantitative analysis of portfolio risk, increasing the efficiency and reducing the processing time to less than 5 minutes.

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Background

In leading Savings Management Companies, the main task of the Quantitative Management desks is to monitor the level of risk associated with the customer portfolio, in order to highlight potential risk situations and, consequently, to reallocate the portfolio by varying its composition. The current methodology for calculating risk and optimising the portfolio is based on a correlation matrix of increasing complexity, involving all the financial instruments that are potentially available as part of constantly changing portfolios. The frequency of optimisation is also increasing due to the natural evolution of the sector.

Benefits

  • Scalability

  • Centralised data repository

  • Process efficiency

  • Analytical effectiveness

  • Real-time

  • Data governance

  • Cost efficiency

Solution

Modern data architectures provide insights for new technological and methodological approaches to handle the risk management processes associated with customer portfolios (Portfolio Risk). These not only enable a substantial increase in calculation speed, but also introduce numerous benefits in terms of efficiency and effectiveness.

  • Scalability: The ability to manage increasing demands in terms of data volume and update frequency.

  • Centralised data repository: Ability to store multi-structured data by centralizing heterogeneous datasets.

  • Process efficiency: Ability to significantly enhance the data loading phase (fast ingestion) and to perform batch operations on large volumes of data (distributed processing).

  • Analytical effectiveness: Possibility to leverage both the availability of a large quantity of data and machine learning algorithms to identify elements of interest (such as target lists).

  • Real time: The real-time reception and analysis of data streams and their utilization as triggers to seize available business opportunities for very limited time periods (such as Customer Engagement, Recommendation, Retention, etc.).

  • Data governance: Better opportunities to control the data lifecycle based on the actual needs of the business.

  • Cost efficiency: The ability to base the system on commodity hardware platforms, offering lower costs for equivalent storage and processing capacity.

The entire process, conducted with traditional tools, requires frequent manual interventions and involves high processing times. With the migration to an information architecture based on big data paradigms, risk calculation and portfolio reallocation can be performed with greater efficiency and complete automation, reducing the processing time to less than 5 minutes in most circumstances.