Transaction Fraud

Stop Illegitimate Transactions with 360 Degree Visibility Across all Systems

In some business segments, fraud has reached the highest levels on record, affecting more organizations than ever. But now, innovative new fraud analytics technologies are helping businesses to quickly identify high risk transactions and behaviors so they can act to mitigate or prevent the losses from fraud and other financial crimes.

One challenge of managing transaction fraud is having the visibility into all stages and elements of a given transaction across disparate and disconnected systems. Gurucul Fraud Analytics can flag any process related control failures due to inconsistent / abnormal transactions across disconnected processes or systems such as core banking and SWIFT. This enables banks to potentially prevent and block significant financial frauds.

Gurucul Miner, a natural language-based search engine, provides a simple but powerful tool for analysts and auditors to gain 360° identity-centric visibility across all systems. It also pivots on any of the data elements such as account number, type of transactions, amounts and so on for any further investigation or periodic risk assessment.

“Today, fighting fraud has moved front and center to become a core business issue… Think of it as the biggest competitor you didn’t know you had.”

Didier Lavion, Principal, PwC US

Data Sources

Fraud Analytics ingests data from whatever sources are available, online and offline, in whatever format the data is in to detect transaction fraud. The more data coming from more data sources, the better for making correlations and finding true insights. Example sources include transactions from point-of-sale devices, customer databases, biometric data, employee work logs, identity and access management systems, payment transfer records, device metadata, and so on.

Data Linkage

The data coming in from so many disparate sources is normalized such that data can be linked and associated to a specific identity. That identity could be a person, such as a cashier, a customer service representative, a customer, and so on, or the identity could be an entity, such as a point of sale device or a desktop computer. This data linkage feature allows for the creation of a baseline of behavior or activity for each identity so that new activities can be compared to the baseline to look for anomalies that are indicative of transaction fraud. 

Big Data

Gurucul has an architecture that can scale to millions, even billions of data points over time. A big data system supports large and varied structured and unstructured datasets and enables data analytics to uncover information including hidden patterns, unknown correlations, and trends.

Machine Learning

The Fraud Analytics engine uses machine learning rather than rules and policies to assess the data. Machine learning is an application of artificial intelligence that provides the system the ability to automatically learn and improve from the data feeds without being explicitly programmed. The process of learning uses sophisticated algorithms to look for patterns in data and to make better decisions in the future based on the data collections that are processed. The primary aim is to allow the computer to learn automatically without human intervention or assistance, and to adjust actions accordingly. The more data the system takes in, the more effective the learning models become — thus, the need for more and varied data sources.

Risk Scoring and Prioritization

When the analytics engine finds convincing evidence of suspected transaction fraud, the system must present its prioritized findings in a way that alerts to the highest risk activities. This is done in the form of a risk score. This score then enables decision-making concerning what action(s) to take to mitigate the activity; for example, suspending a transaction, or prompting for additional identity verification.

API Integration

The purpose of Fraud Analytics is to uncover signs of high risk of transaction fraud. The output is a unified risk score with pointers to the underlying validation data. The next step is to make a decision on what action, if any to take, and to orchestrate that action. Thus, integration to other systems through APIs is necessary, for example, to terminate a fraudulent transaction in process by dropping the person’s online connection.

Gurucul Fraud Analytics generates a 360° view of critical transactions and activities and everything happening around and relative to them. When this happens in real time, as things are happening, it provides the opportunity to not only accurately detect fraudulent transactions, but to actually prevent them.

For more details on our Transaction Fraud solution, request a demo of Gurucul Fraud Analytics.
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