5 popular use cases for streaming data in FinTech
How streaming data is transforming the FinTech landscape
Managing financial data has long been central to FinTech applications, from consumer budgeting apps to enterprise risk management tools. But now, the rise of streaming data is powering the next phase in the industry’s evolution.
Continuously generated and processed in real time, streaming data allows FinTechs to introduce new capabilities that enable faster decision-making, higher efficiency, and improved customer experiences.
Here are a few cool streaming data use cases transforming the FinTech landscape.
Use case #1. Real-time fraud detection
Financial institutions face myriad forms of fraud, from purchases made with stolen credit cards to sophisticated AI-enabled attacks that cost banks millions annually. Real-time fraud detection helps FinTechs fight back against these growing threats, enabling in-the-moment interventions to suspicious transactions.
How streaming data fits
Effective real-time fraud detection requires ingesting multiple data streams, including not only transaction data, but also device data, authentication logs and more. By analyzing these diverse data streams in real time, anti-fraud systems can instantly flag suspicious activity, halting fraudulent transactions before they cause damage.
Streaming data example
Imagine a user trying to send a large amount of money via a digital banking app. However, they’re logged in at a location far from where they live, on a different device than they usually use.
Comparing those data points against the user’s known behaviors in real time results in a flag that sends a fraud alert to the user’s phone or email to confirm the transaction is legitimate. By leveraging Pinecone’s vector-search-as-a-service tool and Redpanda’s data streaming platform, you can build a fraud detection pipeline that compares data points in this way.
Streaming data benefits
By leveraging streaming data, financial institutions enhance their ability to prevent fraud, minimizing financial losses and bolstering customer trust. Real-time monitoring also reduces the cost and complexity of post-fraud investigations, leading to faster resolutions and a protected customer experience.
Use case #2. Personalized financial recommendations
Consumers expect personalized experiences across all digital platforms, and financial applications are no exception. Banks and FinTechs must deliver tailored advice relevant to specific users in specific moments — and that means ingesting and analyzing data in real time.
How streaming data fits
Useful algorithmic recommendations start with analyzing multiple data streams. For example, tracking a consumer’s financial transactions increases visibility into their spending habits, including how those habits may evolve in real time. Combined with other real-time data, such as stock market conditions, a FinTech can provide tailored financial recommendations, such as investment advice, budget tips, or product suggestions.
Streaming data example
Many robo-advisors already leverage streaming data to monitor market conditions and individual portfolios. By continuously analyzing both factors, FinTechs can automatically adjust investment strategies, ensuring customers receive real-time, personalized financial advice tailored to the latest market trends and opportunities.
Streaming data benefits
Personalized financial recommendations increase customer satisfaction by delivering relevant and timely advice. This experience not only fosters deeper engagement but also improves financial outcomes for users, who receive guidance uniquely suited to their individual needs and situations.
Use case #3. Real-time risk management
In today’s fast-moving financial markets, conditions can change in a matter of seconds. That means FinTechs and financial institutions can no longer wait hours or days to perform risk analyses — instead, they require insights they can act on at a moment’s notice.
How streaming data fits
Streaming data helps firms assess risk factors on the fly, enabling them to react instantly to changing inputs. For example, a stock trading algorithm could automatically rebalance its portfolio as prices fluctuate to ensure its holdings remain diversified at all times. Likewise, banks and FinTechs can use streaming data tools to manage other types of risk, such as liquidity risk and credit risk.
Streaming data example
Jump Trading, a proprietary trading firm, uses Redpanda’s streaming data infrastructure to process millions of data points per second from telemetry pipelines. This capability allows the company to manage risk in real time with minimal latency, even when data traffic suddenly spikes.
Streaming data benefits
With the ability to dynamically assess and mitigate risks, banks and FinTech companies can make informed decisions faster, reducing potential losses and maintaining financial stability. This proactive risk management strategy ensures better financial health for both institutions and their customers.
Use case #4. Instant credit scoring and lending decisions
Traditional credit scoring and lending decisions often take days to complete, relying on static credit histories and outdated models as inputs. Today, real-time data analysis can accelerate these legacy processes, boosting customer satisfaction and improving outcomes for financial firms.
How streaming data fits
Streaming data allows banks and FinTechs to analyze real-time financial behavior, such as recent spending, cash flow and repayment patterns. This process enables instant assessments of creditworthiness and lending decisions, reducing wait times for loan approvals.
Streaming data example
Buy Now, Pay Later (BNPL) payment options let customers take out small, unsecured loans at point of sale, which are then paid back in installments over time. Without the time to perform a traditional credit check, banks and financial institutions can rely on alternative credit data — including streaming data — to judge each borrower’s risk of default.
Streaming data benefits
With real-time credit scoring powered by streaming data, banks and FinTechs can provide faster loan approvals, reducing friction in the customer experience and enabling new financial products such as BNPL. This approach can also reduce default rates, as decisions are based on the most current financial information available.
Use case #5. Enhanced customer service and support
Compared to other industries, customer support in financial services is especially high-stakes. Customers expect their financial providers to quickly resolve any issues that surface, especially when problems involve large sums or high-priority transactions. Real-time data is vital for answering these types of customer needs at scale.
How streaming data fits
Heading off financial issues before they occur is the best way to support customers. To do so, support teams can turn to streaming data to monitor system health in real time and make quick fixes to bugs or other problems, preventing impacts to end customers. Additionally, streaming data can fuel automated systems like chatbots, which can quickly respond to customer inquiries without delays.
Streaming data example
A payment gateway might use streaming data to continuously track latency and error rates across various transaction nodes. When the system detects an anomaly, such as an unexpected spike in failed transactions or increased processing times, it can respond almost instantly. For example, the payment gateway might re-route transactions through other nodes, bypassing the bottleneck and avoiding issues for customers.
Streaming data benefits
Streaming data improves customer service by enabling faster response times, reducing downtime, and avoiding frustrating delays. It can also enable banks and FinTechs to flag and address potential problems before customers even notice, protecting the overall customer experience.
Final thoughts
The five use cases outlined above illustrate just some of the ways streaming data is transforming the FinTech industry. From fraud detection to instant credit scoring, streaming data empowers today’s FinTech companies to level up their operations and experiences. As FinTech continues to evolve, streaming data will continue to enable new innovations and growth opportunities.
Interested in tapping into streaming data for your FinTech projects? Take Redpanda for a spin with a free trial.
