Detecting and Visualizing Fraud Using Knowledge Graphs
With the rate of fraud on the rise, companies need to be on the lookout and continuously invent new measures to detect and prevent fraud efficiently. The challenge with fraud detection measures is that fraudsters are constantly becoming innovative and figuring out new steps to bypass the existing standards. One of the types of fraud on the rise is financial fraud, and it is one of the most difficult to detect because it takes different approaches.
Despite the challenges, there are new and better measures to detect fraud, and one of them is using knowledge graphs. The advantage of these graphs is that they can detect even the most salient fraud committed by different stakeholders, employees included.
Why Knowledge Graphs?
Using knowledge graphs enables you to compare different concepts, i.e. creating a relationship between various entities, making it ideal for comparing different scenarios to detect anomalies. For instance, the company can use the graph to detect the relationship between things, attributes and different data that can point to any suspicious activities or unmatching data from database such as Nebula Graph database.
After detecting the anomaly, the organization can proceed with the same graph or other investigative tools. The graphs are also advantageous because they enable the company to create a database containing different data entities that can be used to determine or test the relationships. The company can use different algorithms from the available data to detect relationships that point to fraud.
The Steps in using a Knowledge Graph for Fraud Detection
There are different steps in using knowledge graphs.
- Data modeling
The data modeling process defines the entities you need to examine and evaluate and then enters the data into the systems for different columns. After defining the entities to relate to, you need to use algorithms such as the Louvain algorithms and different search algorithms to identify the patterns that can detect any suspicions.
Some data you can use to detect fraud are account numbers, customer IDs, and social security numbers. Once you detect fraud in these numbers, you can apply the same algorithm to the rest of the available data to detect fraud in the pool. This is the one advantage of a knowledge graph since the sequence can be applied to the remaining data to detect fraudulent activities.
- Defining suspicious patterns
The main benefit of knowledge is identifying fraud patterns that allow employees or the system to apply the same logic to the rest of the data. You can build the rules, procedures and policies from the pattern to detect fraud. The pattern can reveal issues such as the same ID, addresses, social security numbers, names and other attributes. It can reveal multiple accounts under the same name and many more similarities. Therefore, the IT expert should use these revelations to build algorithms and rules to be applied to the rest of the database to detect fraudulent activities.
- Running the algorithms
Once you have suspicious activity, such as multiple accounts for the same name, and the rules, you need to run the identified algorithms. The algorithms can be in different forms, such as graph queries, to help you identify suspicious behaviors in the system and database. You can also rely on standard algorithms such as those provided by Nebula Graph database. The database already has preset algorithms that will help identify other fraudulent transactions you may not be aware of.
- Data visualization
After running the algorithm’s results will reveal all the identified suspicious activities. The process involves turning the identified sequences and events into reports and readable formats. The visualization helps you spot patterns in large amounts of data, indicating fraudulent activities.
Visualization helps in fraud investigations, especially when dealing with large amounts of data. From the pattern, the company can detect the subsequent and similar patterns revealed by the visualization patterns and reports. Instead of manual analysis, you can rely on automated visualization patterns to easily identify fraud transaction patterns.
Benefits of Knowledge Graphs
Companies should increasingly implement knowledge graphs due to their numerous benefits to the organization. These benefits can help the company with current and future fraud detection needs.
- Saving time and costs
Detecting fraud can be cumbersome, primarily if you rely on human efforts hence the need to use automation. Once you identify the entities to define and investigate the relationships, the system runs independently to give the desired results. Therefore, it can detect what human experts cannot see, saving the company the costs and time wasted on fraud prevention and detection.
- Future Fraud prevention
The data visualization and detection enables you to detect the fraud pattern, anomalies and relationships in real-time. With this data, the company can enforce stronger systems to prevent fraudulent information from the database. It can help the company enforce stricter rules and data that can be applied for future instances and fraud detection activities.
- Data unification
It enables the company to combine both structured and unstructured data entities. This increases the volume of data available for analysis and visualization. You can create different rich data sources than most knowledge systems enabling you to identify different types of fraud from both structured and unstructured systems. From the data, the company can summarize the relationship to be used to analyze both structured and unstructured fraudulent activities.
Conclusion
Knowledge graphs are ideal for fraud detection and prevention since they enable you to analyze the relationship between different entities to detect any anomalies and suspicious activities. From the identified activities, you can create algorithms to apply to all the data to identify all the suspicions.
