Julia Wiltshire | Nstream 20 Jul 2023
Maximizing ROI with Streaming Data Applications and Fraud Detection
Consumers lost nearly $8.8 billion to fraud in 2022 — an increase of more than 30% over the previous year.
Despite the growing prevalence of malicious activity, many companies still struggle to identify and mitigate it effectively. That’s because spotting and stopping fraudulent activity – or anomalies in company data – requires a nuanced and time-sensitive approach that many traditional data processing architectures cannot attain. These architectures struggle with real-time data processing due to their stateless services, inefficiencies with structured data, and scalability constraints as volumes grow or latencies compound.
However, fraud is not a business challenge that can be swept under the rug for another time. Left unaddressed, fraud can have detrimental impacts on company revenue, reputation, and customer trust and loyalty.
Under pressure to address fraud or suspicious activity, many organizations commit large sums of money to niche anomaly or fraud detection solutions, assuming the cost is worth the investment should there be a breach. However, there’s another way to spot anomalous events within your company – and one that doesn’t have to break the bank.
Streaming data applications excel in the use case of real-time anomaly detection and mitigation, offering companies a resource- and cost-effective investment to more effectively spot fraud in real-time and implement mitigation measures before it causes irreparable damage.
H2: Why streaming applications excel in spotting anomalies
Many companies struggle to identify fraudulent activity in a timely way because you must compare large amounts of data from either stored archives and disparate sources (e.g. customer transaction history, geolocation data, inventory levels, etc) to:
- First, identify an errant event;
- Second, determine if it’s an acceptable anomaly (e.g., a credit card charge from Spain because that customer is on vacation) or something more malicious (e.g., a credit card charge from Spain due to identity theft);
- And act swiftly if the latter rings true.
Most data processing solutions are not the best option for anomaly detection because their architectures are built using multiple data systems. This approach is inherently more time-consuming and expensive to manage, and it adds latency each time data passes through an additional system. Perhaps more importantly, in the case of anomaly detection, context is dependent on polling/querying, further hampering efforts to spot odd events as they occur.
Streaming data applications built on the Nstream Platform reduce the complexity of traditional data processing architectures to unlock real-time business visibility, enhanced automation, and sophisticated business logic with net-zero latency. This is possible because Nstream’s full-stack application development platform does the following:
- Eliminate the need for additional data systems – such as stream processing frameworks, additional databases, additional applications servers, and data visualization software – for a significantly reduced total cost of ownership (TCO), network-level latency, and streamlined system management.
- Continuously perform stream-to-stream joins at scale (millions per second), meaning companies can filter and merge large amounts of data without incurring prohibitive costs as data scales.
- Isolate events at the real-world object level and push context updates (rather than polling/querying), meaning organizations can see updates to the state of real-world objects (such as customers, assets, IoT devices, etc.) at the speed of their fastest data sources.
In other words, streaming applications’ unique capabilities allow a company to see, understand, and act on the real-time state of their business – whether they want to look at their entire inventory or a single product.
Streaming data applications and fraud detection
In the case of fraud detection, streaming data applications can fuse and process data from multiple sources incrementally (e.g., transaction history, location, current transaction data, etc.) to identify and flag suspicious transactions in true real-time.
Companies can then implement sophisticated automation to better stop fraudulent events in their tracks (e.g., In credit card fraud: sending an automated notification and freezing the card).
As a result, companies that implement streaming data applications for fraud detection can enjoy both internal and external benefits: Not only does improved fraud detection help companies improve operational efficiency and reduce losses, but it can also improve customer trust and loyalty.
Examples of fraud detection
With streaming data applications, a wide range of organizations can enact anomaly detection use cases to suit their specific needs, no matter the industry. Here are examples of how several sectors utilize streaming data applications for fraud detection.
Retail: Combating payment and customer fraud
While the cost of a single missing shoe return may not seem significant, regular instances of fraudulent activity can add up. In 2022, the estimated global e-commerce losses to online payment fraud totaled more than $40 billion. Signifyd’s State of Fraud 2023 Report also reveals that consumers committing non-payment online fraud – such as requesting a refund but keeping the product – is rising.
With streaming data applications, retailers can gain a full, real-time view of their operations, inventory, and relevant customer activities to identify and stop fraud sooner. For example, a retailer may use a streaming data application to look at data from both online and in-store sources – such as customer activity, identity verification, geolocation, recent browsing data, transaction history, and real-time analytics – to identify known fraud patterns, such as suspicious logins or errant loyalty program point redemptions. A retailer could also better track returned merchandise to ensure items reach the proper end destination before triggering a refund.
Manufacturing: A flexible solution for various instances of fraud
The manufacturing industry faces a broad range of fraud-related challenges, such as product counterfeiting, warranty-claims fraud, intellectual property (IP) infringement, and theft of inventory. IP theft alone costs the United States more than $225 billion annually.
Companies can customize their streaming data applications to address the specific type of fraud they’re facing. For example, with streaming data applications, you can track the real-time state of real-world objects, such as trucks, pallets of inventory, or individual assets. You can also track these real-world objects in relation to one another in real-time since streaming data applications can perform stream-to-stream joins at scale. As a result, this granular view can help manufacturers track their inventory, detect anomalies in the supply chain (such as counterfeit products), and better monitor supplier performance in real-time.
Financial Services: Flagging fraud immediately to build customer trust
Financial institutions can’t stop scammers from attempting to access consumer information, but they can minimize losses and build customer trust by taking steps to alert customers of and mitigate fraudulent activity as soon as it occurs.
Streaming applications allow financial institutions to analyze transactions as they coincide with real-time streams from supplemental sources. By applying machine learning algorithms to the streaming data, banks can identify patterns indicative of fraudulent activity, such as unusual transaction volumes, suspicious locations, or atypical transaction times. When potential fraud is detected, alerts can be generated immediately, allowing for a quick response and potentially stopping the fraudulent transaction.
The ability to spot patterns more easily with streaming applications also makes it easier for financial institutions to uncover anomalous behaviors that might indicate market manipulation, money laundering, insider threats, or insider trading.
What to consider when implementing streaming applications for fraud detection and beyond
Here are some practical considerations to remember when adopting a streaming application to better detect instances of fraud.
1. Compliance mandates and governance
Certain mandates and regulations (like anti-bias laws) may prevent government agencies from readily approving algorithms used by streaming applications. In these cases, it’s important to check with potential business partners before application creation to ensure a project is possible.
2. Data warehouse sources
Data warehouse sources likely won’t need to change to adopt streaming applications, but organizations should use the historical data housed in warehouses to train their fraud algorithms. Organizations can train their fraud detection systems on 30% to 40% of their existing data and then use the rest of said data to test their confidence in their fraud algorithm.
Even though streaming data applications can act as a strong component in fraud detection, organizations should still make time to analyze past data to discover other areas of vulnerability within the company.
3. Integration and scalability
Companies that deploy a streaming application with Nstream don’t need to invest in additional data systems, such as stream processing frameworks, additional databases, additional application servers, or data visualization software. As a result, companies can reduce the number of software vendors and subject matter experts required to monitor and maintain their data systems.
Streaming applications can also integrate with existing tech stacks (including enterprise apps, BI tools, and microservices) to extend the capabilities of streaming data processors if desired. This means companies can implement streaming apps – and scale up or down – without replacing their entire tech stack.
4. Specific use case
Streaming applications are best used for fraud detection cases where immediacy is critical, like in fraudulent card activity or account takeover. Some use cases are less urgent and may not require the assistance of a streaming data application. Processes like closing on a house, for instance, are intentionally designed to move slowly to allow time for regulatory inspections. Tracking the individual components needed to build a house as they are shipped would be a better use for streaming applications.
Streaming applications allow organizations to identify fraud quickly to better prevent financial losses, damage to brand reputation, and customer distrust. What’s more, streaming applications can mitigate the need for companies to invest in niche (and costly) software solutions for fraud detection, freeing up budgets and teams.
Nstream delivers the fastest way to build streaming applications so companies can fully understand what’s happening in their business in true real-time and act quickly on suspicious, malicious, or abnormal activity.
Learn more about how streaming data applications can support common business use cases here.