The internet has made it easier than ever to spend money, make money, and manage money with online banking apps. However, the convenience of online financial transactions creates a hidden cost. Criminals now have more methods than ever for defrauding financial institutions and their hard-working customers. Criminal hackers have taken advantage of exploits in technology to increase financial risks, finding new ways of defrauding others daily. For instance, transactions that appear to be legal might actually conceal illegal activities. Insurance fraud, credit card fraud, and tax fraud are only a few of the many kinds of fraud that criminals use to steal from others. Experts say that in 2019, there was more than $5 trillion lost to fraud globally. $5 trillion is nearly as much money as the entire GDP of Japan, the third-largest economy in the world!
Fortunately, as avenues for fraud are growing, fraud detection techniques are expanding too. The detection of financial fraud continues to be the key to minimizing any possible risk for both financial institutions and individuals. One of the most powerful tools in the belt of those who would catch fraudsters is data analysis. Data analysis uses computers and complex algorithms to comb large amounts of financial data for patterns. These patterns can help investigators detect fraud as soon as it happens.
Everything Is Data
Every time a person or a business makes a financial transaction, various pieces of information are collected. For example, when a patient goes to the hospital, dozens of new data points are stored in hospital databases, insurance databases, and billing databases. Savvy companies can then purchase access to this data to learn general trends regarding who is using which kinds of healthcare, when, and how often. This data helps the company learn who to market their products to, for instance. This science is called data mining.
Every banking customer, large or small, follows a sort of routine. Whether it is stopping for coffee on their way to work or paying out wages to thousands of employees every other Friday, financial entities tend to follow predictable patterns of behavior. Deviations from these behaviors are flagged in real-time using data analysis. For instance, if all of a given customer’s credit card purchases throughout the week are in one city, an odd purchase across the country on Tuesday will stand out as an aberration. The customer can now verify whether or not they knew about the purchase.
Similarly, the fraudsters themselves tend to exploit certain loopholes or repeat the same scams over and over again. These fraudulent patterns are also inevitably detected by big data. For example, the U.S. Securities and Exchange Commission uses a “Robocop” program to examine millions of trades each day and compare them to patterns of typical activity. Human inspectors can then personally investigate the accounts now flagged as potentially suspicious.
The Power of Artificial Intelligence
A key component of fraud detection through data mining is machine learning. Machine learning uses artificial intelligence to detect patterns that a human would never notice because they are simply too subtle or complex. Data miners then use a variety of sampling techniques to help computers focus on the right types of fraud. By feeding these powerful computers vast amounts of data, trends, and examples of fraud, these machines can start to teach themselves new techniques for noticing fraudulent behavior. Machine learning represents the cutting edge of fraud detection that is only possible through mass data analysis.
Defeating Fraud in Today’s World
A multi-trillion dollar problem demands the best solutions that money can buy. Financial institutions and computer scientists are rising to this challenge by using data mining, data analysis, and machine learning to process millions of individual transactions in real-time. Data analysis is the ultimate tool that will allow financial institutions to keep up with the ever-changing nature of financial fraud.