What Small Businesses Can Learn from Big Banks About Real-Time Fraud Prevention

An Interview with Karthik Reddy Alavalapati for SiteProNews

Fraud is no longer just a problem for big banks. As online payments become the norm, sophisticated, fast-moving scams are increasingly targeting small businesses, and outdated fraud tools just can’t keep up. In this interview, Karthik Reddy Alavalapati, a senior software engineer and technology strategist at a leading U.S. bank, shares practical insights on how real-time machine learning is changing the game in fraud detection—and what small business owners and webmasters can learn from it.

With nearly 20 years of experience helping Fortune 100 companies modernize their systems, Karthik explains how small businesses can apply the same principles. By leveraging behavioral data, automating detection, and enhancing compliance, we can build safer and more innovative payment systems. Whether you run an e-commerce store, manage a payments platform, or simply want to protect your customers and revenue, this conversation offers an inside look at how to combine security and speed without sacrificing user experience.

Question: Thinking back, was there a single “aha” moment when you saw how real‑time ML could stop fraud and build trust during a payment transaction?

Response: Absolutely. There was a key moment when I saw the impact of real-time machine learning in fraud prevention. Traditional systems missed a series of suspicious transactions, but once we implemented real-time ML, it instantly flagged unusual activity and stopped fraud before it happened. I recall the ML system instantly flagging an unusual transaction, allowing us to stop the fraud before it was completed. That experience made it clear to me how real-time ML not only prevents losses but also builds customer trust by proactively protecting every transaction.

Question: In plain terms, how does a live payment request get analyzed by ML? And how is that different from older, rule‑based systems?

Response: In simple terms, when a payment request comes in, a machine learning system quickly examines a wide range of details—such as the transaction amount, location, device information, and the user’s past behavior (spending, payments etc). The system compares these factors to patterns it has learned from vast amounts of previous data, allowing it to spot anything unusual or potentially risky in real time.

This approach is quite different from traditional rule-based systems, which rely on a fixed set of criteria—like blocking transactions over a certain amount or from specific locations. While rules can catch some types of fraud, they’re often rigid and are not dynamic in nature. However, machine learning models continuously update and refine their understanding as new data comes in, being effective at detecting complex and evolving fraud attempts.

Question: Your career has seen legacy systems and cloud‑native transformations—what’s one common pitfall when upgrading systems for real‑time fraud detection, and how did you solve it?

Response: A common pitfall when upgrading legacy systems for real-time fraud detection is integrating new technology without disrupting existing operations. We addressed this by gradually introducing cloud-native components and using APIs to connect with legacy systems, ensuring a smooth transition and minimal downtime. Collaboration across teams was essential to quickly resolve challenges and maintain systems stability.

Question: Regulatory compliance and data governance are within your area of expertise. How have these shaped your approach to designing real-time fraud-prevention models?

Response: Regulatory compliance and data governance have significantly influenced how we design modern ML systems. From the beginning,ensuring all data handling aligns with relevant regulations set such as NPI, PCI DSS etc, indicates prioritizing privacy, security, and transparency throughout the lifecycle. This includes implementing robust data access controls, encryption, and audit trails, as well as ensuring that models are explainable and their decisions can be traced.

Working closely with compliance and legal teams is also a key and best practice, as it helps anticipate regulatory changes and adapt processes proactively.

Question: Balancing tight fraud controls with a frictionless checkout experience can be tricky for e-commerce sites. What have you learned from building systems that respect both?

Response: Balancing effective fraud controls with a seamless checkout is a continual challenge in e-commerce. What I’ve learned is that leveraging advanced machine learning and adaptive authentication lets us accurately identify and address high-risk transactions without adding unnecessary friction for most customers. By regularly updating fraud detection tools, monitoring real-time behavior, and collaborating across teams, we can maintain strong security while keeping the checkout process fast and user-friendly.

Question: For those early in IT who want to move into real‑time ML for fraud, what skills, mindset, or experiences from your journey would you recommend focusing on?

Response: For anyone starting out in IT and interested in real-time machine learning for fraud prevention, I recommend building a solid foundation in both data science and software engineering. Understanding core concepts in statistics, algorithms, and data modeling is essential, as is gaining hands-on experience with programming languages like Python or Java.

Beyond technical skills, cultivating a mindset of curiosity and continuous learning is vital. As new trends evolve, it is important to learn new tools, and research is crucial. I also found it valuable to develop strong problem-solving abilities and an analytical approach to find complex anomalies in the data.

Finally, working on projects involving large datasets, real-time processing, or financial systems. Collaborating with cross-functional teams will also help understand the broader business context and customer impact, which is just as important as the technical side.

The post What Small Businesses Can Learn from Big Banks About Real-Time Fraud Prevention appeared first on SiteProNews.

Source: https://www.sitepronews.com/2025/07/02/what-small-businesses-can-learn-from-big-banks-about-real-time-fraud-prevention/