Banks and other financial institutions have long been early adopters of open-source software for big data processing and analytics. In 2010, Morgan Stanley began using the open-source Apache Hadoop framework as part of a small experiment. The company was struggling to successfully scale traditional databases to the massive volumes of data its scientists wanted to leverage, so it decided to explore alternative solutions. Hadoop is now a staple at Morgan Stanley, helping with everything from managing CRM data to portfolio analysis. Other open-source relational database software such as MySQL, MongoDB, and PostgreSQL have been indispensable tools for making sense of big data in the finance industry.
Technology is what gives the financial services industry a competitive edge, and artificial intelligence (AI) is rapidly becoming the standard approach to extracting valuable insights from big data and analyzing activity in real-time across the banking, asset management, and insurance sectors. By using AI algorithms to convert unstructured data such as images, audio, or video to vectors, a machine-readable numeric data format, it is possible to run similarity searches on massive million, billion, or even trillion vector datasets. Vector data is stored in high-dimensional space, and similar vectors are found using similarity search, which requires a dedicated infrastructure called a vector database.
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