How Does Big Data Help Banks Prevent Fraud?

TL;DR

Big data and predictive analytics help financial institutions detect anomalous behavior and identify risk before fraudulent transactions cause damage. Unlike threat-specific perimeter and endpoint controls, analytics can remain effective as attacks evolve by consolidating information from users, applications, networks, and payment flows into a shared source for proactive detection and response.

Transcript

Big data and analytics, how do they tie into financial institutions' fight against fraud? Hi, I'm Tom Field, vice president of editorial with Information Security Media Group. I'm pleased to be speaking today with Terry Austin. He's the president and CEO of Guardian Analytics. Terry, thanks for joining me. Thank you, Tom. So we discussed this a bit... Read More

Key Insights

• Perimeter and endpoint protections are insufficient by themselves because they often need to change whenever attackers introduce a new threat. Big data analytics supplements these controls by using information generated across networks, applications, and interactions with end users.
• Predictive analytics is designed to anticipate where threats and attacks may arise, allowing institutions to identify suspicious activity before it causes harm. This proactive approach contrasts with investigations that begin only after fraudulent activity or a security breach has occurred.
• Behavioral anomaly detection is independent of any single threat type. It can identify risk whether an attack is driven by a person, automation, malware, or another form of penetration, making the defensive approach more resilient as fraud techniques change.
• A consolidated source of truth is essential for institutions with outsourced technology, limited internal fraud resources, and fragmented information. Centralized data, appropriate analytical tools, and specialized expertise help these organizations coordinate detection and response across their financial services.
• Denial-of-service attacks can serve as distractions rather than being the fraudsters' primary objective. While an institution focuses its response on disrupted services, attackers may exploit the diverted attention to enter through another route and siphon money.
• Anti-forensics techniques make reactive investigation more difficult because attackers may cover their tracks or misdirect security teams after committing fraud. Anticipating suspicious behavior before an attack is completed puts defenders in a stronger position than reconstructing events afterward.
• Guardian Analytics expanded its fraud prevention coverage from online banking into mobile banking and ACH payments. Its ACH system examines the full transaction flow, while a planned wire system was intended to broaden coverage across financial services payment networks.
• Aggregated information from approximately 250 financial institutions gives Guardian Analytics a broad base of fraud data and expertise. The resulting platform supports fraud defense while also helping customers plan later service offerings and organizational growth with greater confidence in their protections.

Questions & Answers

Q: How does big data help banks prevent fraud?

Big data helps banks prevent fraud by combining information produced across networks, applications, payment systems, and interactions with users. Predictive analytics can examine that information for behavioral anomalies and emerging risks before damage occurs. Because this method focuses on unusual activity rather than a single attack signature, it can respond to human-driven, automated, malware-based, and other evolving threats.

Q: Why are perimeter security controls insufficient against evolving fraud?

Perimeter and endpoint protections are insufficient on their own because they generally must be updated whenever a new threat appears. Attackers and fraudsters are organized, technologically advanced, and constantly changing their methods. Predictive analytics adds resilience by detecting abnormal behavior and risk independently of the specific technique used, allowing institutions to anticipate attacks instead of relying entirely on threat-specific defenses.

Q: What fraud challenges do financial institutions face?

Financial institutions face a constantly changing threat environment while often relying on technology stacks purchased from external providers. Many do not have large internal IT, fraud, or security teams, and their relevant information may be fragmented. These conditions make it difficult to develop sufficient expertise and coordinate defenses against account takeover, wire fraud, ACH fraud, and attacks on online banking services.

Q: What is a consolidated source of truth for fraud detection?

A consolidated source of truth is a unified collection of information that supports fraud analysis and response across otherwise fragmented systems. Financial institutions need this shared view, along with specialized expertise and analytical tools, to understand changing threats. It is particularly important when institutions outsource technology and have limited internal resources for monitoring online banking, ACH payments, wires, and account takeover attempts.

Q: How are denial-of-service attacks used to support banking fraud?

Denial-of-service attacks can be used to divert an institution's attention while a separate fraud attempt is underway. Security and operational teams may concentrate on restoring disrupted services, giving attackers an opportunity to enter elsewhere and siphon money. The tactic illustrates why banks must look beyond the most visible incident and use predictive analysis to identify concurrent anomalies and concealed financial activity.

Q: Why does anti-forensics make reactive fraud detection difficult?

Anti-forensics makes reactive detection difficult because fraudsters may conceal evidence, cover their tracks, or deliberately misdirect security teams after an attack. Investigators can therefore struggle to determine what happened once funds have already been taken. Detecting anomalous behavior and risk in advance puts institutions in a better position to prevent harm before attackers can obscure the event and frustrate the investigation.

Q: Which payment channels can fraud analytics monitor?

The Guardian Analytics offerings discussed include fraud prevention for online banking, a mobile banking solution, and an ACH payment system that examines the entire transaction flow. A wire system was also expected to be introduced shortly. Together, these products reflect an effort to extend big data and predictive analytics across a broader range of payment networks used by financial institutions.

Q: How does shared fraud data benefit financial institutions?

Shared fraud data gives institutions access to a broader, consolidated base of information and analytical expertise than many could create independently. Guardian Analytics reported that approximately 250 financial institutions had standardized on and adopted its platforms and solutions. The combined data helps customers defend against fraud attacks and provides a trusted analytical foundation for planning future services and organizational growth.

Summary & Key Takeaways

• Financial institutions face a constantly changing threat environment populated by organized, technologically advanced attackers. Traditional perimeter and endpoint protections remain necessary, but they must change whenever new threats emerge. Big data and predictive analytics provide a more resilient approach by identifying anomalies and risks without depending on one specific attack type or technique.

• Many financial institutions buy much of their technology stack, operate with limited internal security resources, and keep information across fragmented systems. Effective fraud defense therefore requires a consolidated source of truth, specialized expertise, and tools that can analyze and respond to threats across online banking, mobile banking, ACH transactions, wires, and other payment networks.

• Fraudsters increasingly combine automated and manual methods, use denial-of-service attacks to distract defenders, and employ anti-forensics to conceal their activity. Guardian Analytics responds with predictive platforms covering online banking, mobile banking, ACH payment flows, and an upcoming wire system, supported by aggregated data from approximately 250 participating financial institutions.