By Robert Metzger, Co-founder, Engineer Lead at Ververica
Financial organisations across the world are going through an extensive digital transformation as market volatility intensifies, consumer behaviour changes and regulators’ requirements increase. The capital markets and finance industries are transitioning from minimum digitisation and automation in their business processes to becoming highly-automated and software-operated. According to analysts, financial services spend on big data technologies will surpass $14 billion in 2021, up from $9 billion in 2018, growing at a CAGR of approximately 17 percent over the next three years.
By adopting stream processing and a streaming data architecture, financial services organisations are able to implement the necessary changes to business models and processes in an agile and cost-efficient manner. This streaming data architecture environment allows financial services organisations to transform into a new way of reacting to information. In this new reality, businesses can benefit from real-time insight and build 24/7, data-driven applications that make any required changes and alerts instantly.
As more and more financial services organisations modernise their infrastructure, let’s take a closer look at the ways we can expect stream processing to act as a primary catalyst for innovation in the finance industry of the future.
Detecting Cybersecurity Fraud In Real-time
By using a streaming data architecture, financial services organisations are able to process data at the moment it is generated i.e. at the exact point when it’s most valuable. Using stream processing, businesses can build real-time fraud detection systems and powerful machine learning algorithms. This combination enables the detection of fraudulent actions in real-time and can, therefore, avoid any potential losses for the business and mitigate any potentially negative customer experience. The more sophisticated these tools become, the more powerful the response that financial services organisations have in the face of credit card fraud, identity theft, fraudulent transactions or any other forms of cyber security threat.
One example of a bank using stream processing to power its real-time fraud detection engine is ING. They were able to build an Apache Flink-powered risk engine that allowed the company to respond to new, previously-unknown threats instantly. ING’s fraud detection system supports multiple goals for the business – it supports a range of machine learning models and has built a risk-engine that can be deployed in different environments.
A 360 Customer Experience
A whole slew of fintech companies is starting to exhibit a significant competitive threat to the finance industry. These newcomers to the game can innovate faster and can also provide personalised experiences for their customers. Therefore by adopting stream processing, the finance industry can stay ahead of competition by building a 360° customer view program that can analyse real-time data. Some financial services organisations have millions of customers that generate billions of transactions on a daily basis and who engage with the company’s web and mobile portals, as well as customer services tools and agents. Imagine being able to process and respond to such data transactions in real-time. This will surely allow for much better monitoring of customer health and loyalty. Also, such real-time processing enables a company to create a more tailored product offering, depending on what transaction types the customer favours or how they interact with the organisation’s website, mobile app, customer support portal etc. Completely understanding the customer viewpoint and providing real-time response is the best way to meet the challenges of the modern financial services industry.
Capital One uses stream processing and Apache Flink for real-time monitoring of customer activity data to ensure issues are detected and resolved proactively, something that provides an enhanced digital customer experience.
Simplifying Regulatory Compliance
The financial services (and capital markets) industry often operates in a complex regulatory environment. There is still a fair amount of manual work involved in reporting to regulatory bodies creating additional cost and overhead for the finance industry. The pace of change associated with this regulatory landscape throws up some major challenges for financial services organisations. Stream processing can enable technology-led, real-time compliance that moves away from manual checks, instead offering always-on, data-driven systems that alert and report instantly on the current state of the business to different regulatory bodies. Stream processing is often used for maintaining a real-time market position and ground-source-of-truth state across the organisation that, in turn, provides a real-time view of the bank’s risk exposure. Streaming data architectures allow compliance departments to continuously process information in real-time avoiding any hefty fines by regulatory bodies due to non-compliance.
These are just some examples of how stream processing can power modern financial services organisations to become real-time and software-operated businesses of tomorrow. According to IDC by 2025, nearly 30 percent of the so-called “global datasphere” will become real-time information, we can surely expect stream processing to be a pivotal new paradigm for data processing in the finance industry of tomorrow.