In the technology-driven business world, data security and integrity have never been more important. This is particularly true in sectors commonly targeted for fraud, including banking, financial services, and healthcare. Security threats are ever present for enterprises in these fields, making fraud detection immensely important.
If fraud occurs often enough—or at a large enough scale—the consequences can be disastrous. Fraud analytics are an essential part of these organizations' fight against fraudulent claims and similar schemes.
What is fraud analytics?
The term fraud analytics refers to all the core data analytics and data science methods, tools, and best practices that are used to stop and mitigate financial fraud.
Fusing data science and automation
Given the sheer amount of data supporting the thousands or millions of transactions that enterprise-scale institutions process on a regular basis, it's impossible to imagine sifting through this information without leading-edge analytics tools. Even the most seasoned, knowledgeable data experts would only be able to detect a fraction of the critical patterns—and anomalies—within these transactions through manual data exploration.
To be clear, the skills of experienced data analysts and data scientists are crucial to interpret the outliers, trends, and other patterns that fraud analysis tools uncover within big data sets of genuine and fraudulent transactions. But human analysis alone can't handle the sheer data volume. A modern analytics program ensures that crucial steps like data ingestion; exploratory data analysis (EDA); data cleansing; extract, transform, and load (ETL)—or extract, load, and transform (ELT)—processes; and data integration happen as fast as possible.
The most advanced analytics tools used to detect major types of fraudulent activity automate most of their processes. This is only achievable at scale through artificial intelligence (AI)—often specifically through machine learning (ML) tools. All major ML training methods can be leveraged to prepare optimal analytics solutions for fraud detection.
Supervised learning, for example, is beneficial for engineering traditional banking, insurance, and healthcare fraud analytics systems, all of which rely heavily on large volumes of historical data. By contrast, newer financial methods, such as open banking, won't have amassed as much data from the past, so unsupervised learning makes more sense for fraud detection in those contexts.
Fighting present and future fraud
Applying the possibilities of comprehensive analysis to a task such as fraud prevention makes sense in a world where cyberattack methods are becoming more sophisticated—sometimes faster than cybersecurity experts can keep up with.
Using analytics in this context allows for a meticulous, granular approach that can ultimately help to prevent fraud in real time. Fraud analytics can also be used strategically, as a tool for proactive risk analysis—determining where fraud is most likely to occur and directing detection and prevention resources there. Predictive analytics provides the ideal mechanism for assessing circumstances that indicate a high chance of future fraud, whereas prescriptive analytics helps fraud detection teams determine the best courses of action to mitigate or prevent fraud.
Overcoming limits of traditional fraud prevention
To better understand the value of today's fraud analytics, it's worth looking at classic fraud detection practices—and the limitations they had before analytics tools could support them.
Rules-based fraud prevention
This method involves establishing rules or policies that fraud teams consider clear signs of bogus activity. Once rule-breaking is detected, rules-based fraud prevention mechanisms are triggered to either halt pending actions or prevent future actions, like enacting a temporary hold or cancelation on somebody's credit or debit card.
In a vacuum, rules-based fraud prevention seems reasonable, especially in the example above. The problem is that the "rules" are static: Someone shopping at a brand-new store in an atypical location might simply be on vacation. But classic rules-based fraud systems might flag the transaction and put the card on hold, inconveniencing the shopper until they can verify the purchase.
Anomaly detection
Like their rules-based counterpart, classic anomaly detection methods, while valuable, have notable flaws. The main issue is the volume and velocity of big data—especially when coming from a broad variety of disparate sources. Traditional anomaly detection systems have a hard time handling the scale of these transactions and their associated variables, which reduces accuracy. Therefore, false positives could crop up or anomalies related to fraud might go undetected—or both.
Key analytics benefits for fraud detection and prevention
Adding the dimension of leading-edge modern analytics solutions to rules-based fraud prevention and anomaly detection helps focus these techniques.
Greater scope and scalability
AI/ML algorithms used in contemporary fraud analytics examine every data point surrounding a transaction and compare it to historical data in minutes or even seconds. This adds context and nuance that can ascertain the difference between a new type of transaction or a problematic anomaly.
At the same time, automated analytics help fraud detection and prevention systems focus strictly on the most relevant data. This reduces the chance that they'll be overwhelmed by any irrelevant "noise" in large data sets.
Better compatibility with new methods
Fraud analytics doesn't just strengthen the foundation provided by traditional financial crime-fighting methods.
When used in conjunction with an advanced data analytics platform, enterprises can more effectively leverage the most up-to-date fraud detection and prevention tools and their associated data sources. These include biometrics, spend velocity, geolocation data, autofill tools, behavioral analysis, and more. Ultimately, this means organizations will identify patterns and anomalies that might not have been discoverable through more traditional means.
Protection for the bottom line
Vulnerabilities and data breaches don't always cost enterprises a great deal of time, money, and resources—if they're detected and rectified fast enough. But any fraud that goes beyond a few aberrant, infrequent incidents will be financially costly—as it's typically a crime committed systemically, over time, for maximum illicit profit.
Using analytics to track and mitigate fraud offers several long-term benefits to enterprises' bottom lines. Most obviously, it helps reduce the frequency of fraud and the sunk costs that come with it. But fraud analytics also allow organizations to measure the overall performance of various anti-fraud initiatives and identify vulnerable areas of the enterprise.
Increased customer loyalty
The majority of fraud affects enterprise customers one way or another. Often it's a direct effect, as with identity theft—or its newer variant, synthetic identity theft, which combines parts of real identities with fake information to defraud banks and many other organizations. But scams like fraudulent insurance claims, when occurring in large enough numbers, also eventually hurt customers by causing premiums to rise.
Implementing fraud analytics and other leading anti-fraud practices and technologies—and publicizing these efforts—lets customers know you take these risks seriously and are doing everything possible to prevent them. This is obviously important for financial and healthcare organizations that customers entrust with their most sensitive information, but it's no less valuable in retail: One need only look at the aftermath of the 2013 Target data breach—a 46% profit decline and hundreds of millions lost over several years—to clearly see how damaging a lack of transparency can be.
Will multichannel communications about analytics-based fraud prevention initiatives guarantee customer loyalty? That's impossible to say for sure. But in an era where most consumers are well aware of how financially gutting things like identity theft are—$52 billion in 2021, affecting 42 million U.S. adults—openly using fraud detection analytics and related tools does help customers understand you're on their side.
Manage fraud analytics in the cloud
The cloud is understood as an ideal deployment arena for analytics workloads due to its elasticity and scalability, storage space, and potential for performance improvement, among other factors.
To safely run analytics on data connected to fraud—as well as information sensitive enough that fraudsters might want it—enterprises should protect their cloud infrastructure with cutting-edge cloud-native security features. Critical examples include cloud access security brokers (CASBs), next-generation firewalls (NGFWs), and end-to-end encryption for data in transit and at rest.
Aggregating all this data into a secure single source of truth for fraud prevention requires a comprehensively equipped analytics platform with cloud-native compatibility like Teradata VantageCloud. The solution's analytics capabilities include advanced in-database analytics, AI/ML features, complex data modeling, and more.
To learn more about VantageCloud, connect with us today.