Although technology has made banking more convenient for customers, it has also opened up new avenues for fraud. Financial fraud statistics show that account fraud, credit card fraud, insurance fraud, scams, and other fraudulent acts cause millions of dollars in damages to institutions and consumers every year.
Financial fraud detection is essential for minimizing risk for institutions. Scammers can easily drain individual accounts or run up tens of thousands of dollars on credit cards. Worse yet, organized crime rings can execute elaborate schemes and steal millions of dollars.
Big data fraud detection is a cutting-edge way to use consumer trends to detect and prevent suspicious activity. Even subtle differences in a consumer’s purchases or credit activity can be automatically analyzed and flagged as potential fraud. Using data analytics to detect fraud requires expert knowledge and computer resources, but is easier than ever, due to improvements in programming languages and server technology.
How Does Data Mining Work?
Data mining is the science of automatically detecting patterns in a given set of data. It requires significant amounts of computing power and careful data management using advanced technology like data lakes and cloud computing. Any data analysis program requires complex programming languages, but data mining that is robust enough to support subsequent machine learning must be carefully coded to prevent errors in pattern detection.
Data mining for fraud prevention relies on pattern analysis to find outliers or suspicious trends. In financial services and many other industries, one of the best sources of data is big data. This data contains information like customer zip codes, travel patterns, income levels, age, and other demographic factors that influence customers’ financial decisions and purchases.
All of these datasets and accompanying machine learning processes are subject to continuous review, testing, and feedback from humans. When a system flags a false positive, the person who investigates that false positive can then teach the system why it was incorrect. Experts in financial fraud detection apply this new knowledge and understanding to future fraud data analysis as well.
Accuracy of Fraud Prevention
Suspicious large transactions or blatant fraud can be detected without data mining. For example, if a customer uses their credit card at a store, and then an hour later appears to use their credit card at a store on the other side of the country, the provider can freeze that account.
However, data mining makes it possible to detect other, more subtle signs of fraud with high levels of accuracy. Customer information can be analyzed to predict general trends and spot fraudulent transactions before a customer even knows that their card or account has been compromised.
Improvements in technology and machine learning processes have resulted in fewer transactions being flagged as fraudulent.
Consumers make online purchases, travel, move, and use a wider variety of accounts than ever before. Mobile apps have made it easy for consumers to manage their finances on the go, and the data collected from these apps make it easier for financial institutions to maintain accurate data.
Data mining goes hand-in-hand with machine learning, which is a type of artificial intelligence that can recognize previously-learned patterns and search for them in datasets. Data mining is the process of determining the rules and patterns, while machine learning is teaching a computer how to interpret and understand those rules.
Machine learning is a complex process that has taken years to develop to the level where it can handle complex consumer trends and information. The intersection of big data and artificial intelligence now allows financial institutions to use big data fraud prevention methods that were impossible in previous decades.
There are multiple forms of machine learning, each with slightly different learning capabilities. Supervised learning uses labeled data from historical datasets with known outcomes, like fraudulent or genuine past transactions. Unsupervised learning uses unlabeled data without known outcomes but can still draw inferences and recognize patterns.
Semi-supervised learning uses a blend of both methods for a unique cutting-edge learning method. All three types of learning are essential to AI and must be refined with new programs and processes on an ongoing basis.
Fighting Fraudulent Accounts
Banks try to make opening new accounts as easy as possible for new customers, and they try to minimize restrictions on new account usage. However, this also opens up avenues for fraud, especially if scammers use false identities to open the accounts. Stolen social security numbers and fake ID cards go a long way at the bank.
Fraudulent account takeovers are also an issue in bank fraud cases. These takeovers occur when a fraudster has an existing account transferred into their control using stolen customer information and a few changes to contact details.
Fighting these accounts is all the more important since data breaches are still prevalent, and they can expose millions of customers’ account details. Plus, phone and email scams allow criminals to obtain private data like personal bank account numbers easily. Even the coronavirus outbreak has inspired a fresh wave of email scams that trick users into installing malware or sending billing details to a scammer.
Detecting fraudulent transactions from fake or compromised accounts requires an expert eye for data mining and machine learning systems that can handle massive amounts of data. Financial institutions must invest heavily in the detection of all kinds of financial fraud, whether the root of the fraud was a phishing email or a stolen credit card.
Preventing Credit Card Fraud
Credit card information is easy for criminals to steal and use for fraudulent purchases. Financial institutions periodically reissue credit cards and allow customers to freeze their accounts manually, but these safeguards are rarely enough to prevent fraud.
Another potential source of credit card fraud is when account owners claim an income level that is higher than what they actually make, enabling them to open a larger line of credit than they deserve. This happens with both new and current customers, so financial institutions need to be proactive in monitoring all accounts for signs of fraud.
Data mining allows companies to compare customers’ activity with comparable peers and receive accurate financial fraud detection warnings. Although every customer has unique spending habits, there are important trends related to mode, method, and amount of each transaction.
Fraud analysis programs must work to detect fraudulent transactions in a matter of seconds so that the card provider can stop the transaction and contact the customer for follow-up. This requires large amounts of computing power but is an essential investment for both customer experience and loss prevention.
Running data analysis, tweaking machine learning algorithms, and addressing false positives in fraud detection requires careful consideration of the thousands of variables affecting patients and providers. Read More
Detecting Insurance Fraud
Insurance fraud includes all sorts of medical, automobile, and property insurance coverage that can be manipulated for financial gain. Financial institutions that offer insurance services must be proactive in implementing new systems to detect this fraud.
Examples include inflated claims or lying about the cause of damage in property insurance claims. As with other types of fraud detection, insurance fraud can be detected with big data mining and AI.
Taking Down Organized Crime
Unfortunately, financial fraud is rarely a solo crime. By operating in groups, criminals can execute complex operations designed to defraud multiple institutions at once for long periods.
Data mining and machine learning can be designed to detect and draw connections between broad anomalous trends, instead of just finding single outlier accounts or transactions. As cybercriminals become more sophisticated, the ability to automatically detect this fraud may be able to save financial institutions from millions of dollars in losses.
Data can also be used to detect money laundering. Although money laundering is not necessarily a direct cause of loss for financial institutions, it may potentially indicate other illegal activities in big fraud cases. By detecting and reporting this activity, financial institutions protect themselves from future harm.
To tackle crime in real-time without affecting genuine customers, financial institutions must be able to identify and lock down compromised accounts. Minimizing false positives requires ongoing analysis and revisions to machine learning processes, including improvements to data mining practices.
Investing in security fraud detection with data mining is a key component of overall risk management and best practices for security. By investing time and resources into financial fraud prevention with big data, financial institutions can minimize losses and maximize profits.
Syntelli Solutions has experience in managing data mining for fraud detection for financial institutions and a proven track record of positive outcomes. We provide a variety of services in data management, machine learning, artificial intelligence, cloud computing, and other technology fields related to data. Contact our team to learn more about the customized solutions we provide.
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