It also costs consumers in higher credit card fees, citizens in higher taxes, businesses in higher insurance costs. It's in every organization's interest to take a proactive stance in uncovering and preventing fraud. Data Mining Defenses Uncovering fraud can be a tedious, labor-intensive process. Traditionally it has involved time-consuming investigative work, supported by simple computer functions such as flagging insurance claims that exceed a certain limit. One weapon that's proving effective is data mining, the process of selecting, exploring and modeling large amounts of data to reveal previously unrecognized patterns, associations and trends.
Data mining goes beyond computer monitoring to identify suspicious patterns of activity.
Such patterns might involve unusual data, such as a large number of accident claims from one person, or unexplained relationships, such as different companies with the same mailing address. If you have a population of 80 million transactions and less than 1 percent is fraudulent, it's a needle in a haystack. If proven cases show that fraud is more prevalent among men, then gender might be the first 'split' in the decision tree. And so on, including such data points as education, income and place of residence. The more information you have, the closer you can come to uncovering potentially fraudulent events.