Financial fraud consumes an estimated 6% of the worldwide gross domestic product. In 2019, that was more than $5 trillion in fraud. Even with artificial intelligence and machine learning, the amount of fraudulent activity increases every year. It’s an arms race, with fraudsters defeating one technology or technique and data analysts and security professionals upping the ante with a tougher system to break.
Learn how data analysis is helping with fraud protection and why it’s important.
How Data Collection Works
Every time a financial transaction is performed, a multitude of pieces of data are collected. For example, a person who’s in an auto accident and gets injured will have to supply a lot of data. They’ll give their name, date of birth, license number, license plate number, make and model, location, passenger name, address, and more. This information is entered into databases and stored by the involved parties.
Oversampling Helps With the Imbalanced Data Problem
Imbalanced data refers to a problem with a classification system that doesn’t represent each class equally. Oversampling is one data analysis technique that overcomes imbalanced data so that machines can do a better job of identifying fraud. There are several oversampling techniques used by data analysts. A popular one is the synthetic minority oversampling technique. It uses synthetic observations or artificial observations of the minority class. In this case, the minority class is fraudulent transactions.
Undersampling for Achieving Better Balance in Fraud Identification
Another way to overcome the problem of imbalanced data is to use the data analysis technique of undersampling. This is like the opposite of oversampling. Undersampling simply uses fewer samples from the dominant class. It removes some of the data points from large clusters. A problem with undersampling is that it causes a loss of precision. Most data analysts use a combined approach of oversampling and undersampling.
Types of Fraud Data Analysis Can Defeat
Fraud can occur in any economic activity or industry. Data analysts have been focusing on three particular areas of fraud that are complicated and pervasive. These are insurance, credit card, and value-added tax fraud. A new way of looking for fraud in these complex areas is with a graph database. It ranks the connections to be as valuable as individual data points.
How Graph Data Analysis Beats Complex Fraud Rings
In insurance fraud, two people often team up to create an auto accident and defraud the insurance company or companies. A relational database isn’t likely to make connections between the involved parties. A graph database can. That’s because insurance fraud rings usually have 10 people or less who play different roles in the fraud. In one crash, Mr. A and Ms. X may be the drivers, and Ms. B and Mr. Y may be the passengers. In the next one perpetrated by the ring, they may swap who’s driving, with Ms. B driving with Mr. A as a passenger. A graph database makes these connections.
To learn more about data visualization and real-time analytics from Live Earth, check out our Financial Data Insights Solution to learn more.
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