Traditionally, that meant feeds from companies like Bloomberg and Reuters. Financial services firms would hire the best mathematicians and data scientists to analyze this data and try to predict the future. One example is “alpha generation,” which uses mathematical models to make data-driven investment decisions that outperform the market.

Today, AI can crunch the numbers at lightning speed and massive scale. What’s more, AI can analyze complex datasets with unstructured and non-numerical information. Financial services firms are capturing and analyzing data from a wide range of sources, including financial news, social media, geolocation data and more. Grandview Research predicts that the alternative data market will explode, with a compound annual growth rate of 63.4 percent through 2030.

The adoption of AI and machine learning is driving the growth. However, the key is to get access to the data as quickly as possible. Real-time data streaming allows financial services firms to leverage live and on-demand data flows to drive intelligent decision-making, automation and operational efficiency.

What Is Real-Time Data Streaming?

Real-time data streaming enables the near-instantaneous ingestion, analysis and output of data. It sounds relatively straightforward, but the underlying technology can be complex. Real-time data streaming systems must be able to process large amounts of data with minimal latency.

Real-time data streaming systems consist of five core components.

Data Sources. Raw data is generated continuously by a wide range of devices, applications and platforms. These diverse data sources must be integrated using connectors or APIs to ensure reliable ingestion and processing.

Streaming Ingestion. Streaming ingestion is the continuous collection and processing of data as it arrives from real-time data sources. Data is filtered, transformed and enriched in preparation for analysis. High throughput and low latency are essential.

Streaming Storage. Once data is ingested, it must be stored for querying and analysis. Streaming storage systems are designed to handle large volumes of data and scale to accommodate increasing data loads. 

Stream processing. Stream processing analyzes, transforms and manages data as it arrives so that organizations gain valuable insights faster. It’s distinct from batch processing, which processes data in discrete chunks. 

Destination. Once data is processed, it must be made available to decision-makers so that the organizations can capitalize on it. The final destination could be a database, data lake, application or real-time dashboard.

How Is Real-Time Data Streaming Used in Financial Services?

Alpha generation is a primary use case of real-time data streaming in financial services. Portfolio managers analyze company fundamentals and historical price data and trading volumes to identify patterns and predict market movements. This strategy relies on the ability to analyze data as it arrives, enabling fast reactions to market signals.

There are other use cases as well. Financial services firms use real-time data streaming to track market fluctuations, calculate value at risk and rebalance portfolios automatically. Payment systems use stream processing to monitor transactions in real-time, detecting patterns such as sudden spikes in volume or atypical purchase amounts that could signal fraud. Financial advisors can use real-time data to offer tailored recommendations that can be adjusted dynamically.

Real-time data streaming and AI can automate many repetitive tasks in loan and mortgage processing and other functions. It also enables financial services firms to monitor social media platforms for customer sentiment so they can proactively engage customers, adjust their marketing strategies and manage their online reputations.

How Technologent Can Help

Technologent has a long history of serving the financial services industry, providing sound advice and specialized solutions that address complex challenges. This experience, combined with our expertise in AI, makes us well-positioned to help financial services firms capitalize on the value of real-time data streaming. Let us help you capture, process and analyze data in real time to enhance decision-making and streamline your operations.