Fraud losses are at an all-time high. According to PwC's Global Economic Crime and Fraud Survey 2022, fraud losses are at the highest level in twenty years of research. The Association of Certified Fraud Examiners (ACFE) estimates that organizations "lose five percent of revenue to fraud each year and the average loss per case is $1,783,000" in Occupational Fraud: Report to the Nations 2022.
These findings suggest that a traditional approach of using fraud analytics software with only (rigid) business rules or machine learning is not sufficient to protect organizations from ever-evolving fraudulent behavior.
Instead, combining business rules (preferably flexible) with machine learning and graph analytics technology is the best approach for implementing more advanced fraud analytics software and achieving exceptional results and functionality.
These three key components work together to substantially improve fraud detection and investigation and enable an enterprise-wide approach to fraud prevention and detection.
Traditional data analysis techniques revolve around systems that analyze data in tabular format based on rigid if-then rules. Unfortunately, data sets are typically siloed and highly complex to navigate, especially in large enterprises.
Graph analytics technology can significantly improve the effectiveness of rule-based systems and ML in detecting fraud by identifying and analyzing the relationships between data elements. This provides fraud teams with new capabilities for identifying hidden patterns and connections.
Fraud analytics software with graph technology is handy for connecting and finding patterns across siloed data. The effectiveness of fraud detection and investigation is improved when an organization’s data is analyzed instead of only one or two main silos.
For example, with a graph-based system, networks associated with organized crime groups can be automatically detected and analyzed. When a new loan application “” that organized crime group, it is quickly identified as suspicious.
Graph technology can significantly reduce fraud losses from organized crime groups that cost millions of dollars annually to banks, insurance companies, and other enterprises. Gartner's prediction that graph technology will be used in “80% of data and analytics innovations by 2025” is a logical outcome since the benefits of graph analytics for organizations extend far beyond only detecting organized crime.
Along with graph technology, machine learning (ML) and artificial intelligence (AI) supercharge fraud analytics software by predicting suspicious or fraudulent events or objects.
AI and ML not only supercharge fraud detection software but also aid in data preparation and analysis for other applications. AI can automatically extract and organize data, including addresses, phone numbers, and organizations, making it a valuable tool for fraud detection software. Gartner predicts that by 2025, context-driven analytics and AI models will replace 60% of traditional data models, emphasizing the growing importance of AI and ML in fraud detection software.
Even if there’s a massive volume of siloed data to be monitored, AI and ML can recommend risk rules based on existing business data while giving fraud analysts the ability to understand the logic behind the suggested rules.
ML systems learn from experience, which means that as additional information becomes available, it can use the new data to make smarter decisions and improve predictions over time in high-risk situations.
It’s important to note that training ML with high-quality contextual data that expresses the value from relationships - as done with graph technology - provides dramatically better results.
Advanced fraud analytics software that uses AI and ML helps enhance human intelligence by finding facts and patterns that are difficult or impossible to interpret and capture manually. As a result, fraud analysts can reduce manual review time and take steps to improve how they detect fraud to reduce operational costs within their organizations.
If your organization requires transparency of the logic behind fraud decisions, you may want to avoid AI and ML systems that operate as a “black box”. Instead, you may want to select fraud analytics software that gives your organization full transparency.
Rule-based fraud detection systems are crucial to any fraud prevention solution. These systems rely on predetermined rules and conditions to identify suspicious activity, making them an essential foundation for fraud detection.
A rule-based fraud prevention solution can be based on various factors, such as the context of the transaction, people or events; the value of the events (e.g., transaction, claim, etc.); and the relationship between the parties involved. This approach can provide valuable insights into fraudulent behavior and is a key element in any comprehensive fraud prevention solution.
For example, a rule-based system may flag an insurance claim filed within two days of a new policy being issued as possibly suspicious. Or it may label a transaction as potentially fraudulent if it involves a high dollar amount and is being conducted between parties with or without a previous relationship.
Traditional rules are limited in effectiveness as they are often very rigid, making it difficult to adapt to the constantly changing methods and techniques of fraudsters. Modern fraud analytics systems that have more flexible rules engines can enable an anti-fraud team to be far more agile such that they can more quickly identify and take appropriate action on emerging fraud patterns.
In addition to these rules, a rule-based fraud analytics software system may also incorporate graph analysis, which involves analyzing the connections and relationships between different entities in order to identify patterns or anomalies that may indicate fraud.
The key to unlocking the full potential of advanced fraud analytics software is combining flexible rule-based fraud detection with machine learning and graph technology. An anti-fraud system that combines these three components is able to effectively identify and predict fraudulent activity, simultaneously reducing the percentage of false positives down to single digits.
However, it's important to keep in mind that the combination of these components isn't the only factor to consider when choosing the best fraud analytics software for your organization. It's also crucial to make sure that these components are highly agile so that you can easily add new data sources, quickly test new hypotheses, and efficiently fine-tune rules and machine learning models.
DataWalk enables you to connect all your siloed data and quickly uncover suspicious patterns and networks, and to dramatically increase the speed and accuracy of fraud detection by leveraging the power of highly flexible rules, AI/ML, and graph technology. Request a live demo to learn more about DataWalk’s fraud analytics software solution.