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Architecting a Unified Data Platform

Authored by: Narsimhan Bramadesam, Sr. Director – Software, Synechron and
Sandeep Mahendru, Sr. Director – Software, Synechron

The financial industry today is facing new challenges like increasing regulatory and compliance pressures, improving their control and governance capabilities, and enhancing oversight and transparency to prevent reputation damage and financial loss, all while still driving performance and profitability. Financial institutions are looking for innovative ways to ensure compliance, enhance performance and maintain a competitive advantage. Some of the challenges financial institutions have been faced with due to increasing regulatory obligations include:

  • Increasing regulatory requirements forcing financial institutions to disclose data at an increased frequency and granularity (e.g., BCBS 239, daily LCR)
  • Increased analytical and compute workloads (e.g., FRTB, CCAR)
  • Increased transparency and reporting needs where banks should report pre-trade and post-trade data to regulators (e.g., MiFID II)

All these challenges are forcing financial institutions to evaluate their:

  • System architecture, infrastructure capability
  • Data strategy, integration pattern
  • Data analytics platform, Business Intelligence solutions
  • Storage and compute stability

Organizations are looking at building a unifying platform to run analytics and machine learning models on data. To address this, this paper explores the options of using some of the open source tools available today. As part of this, there is a focus on creating data lake, and building and operationalizing (deployment and cluster management) the analytics/ML platform for creating a reference implementation for Fundamental review of the trading book (FRTB). In our example, we can use the data lake for landing trade and reference data from the various trading systems, and then transform them into a common format for creating a successful analytics solution.


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