Anomaly detection in fraud analytics plays a pivotal role in identifying unusual patterns that deviate from expected behavior, thereby enabling financial institutions to preemptively address potential threats and maintain the integrity of their systems.
Anomaly detection deals with the problem of finding patterns in data that depart significantly from the expected behavior. Being able to detect such anomalies is crucial in domains such as fraud detection for credit cards and bank transactions, insurance claims, and money laundering.
Anomaly detection is the ability to identify rare items or observations that don’t conform to normal or common patterns found in data. These outliers are important within financial data because they can indicate potential risks, control failures, or business opportunities.
Moreover, they usually represent critical areas that need further examination or scrutiny. Consumers, institutions, and businesses lose large amounts of sum annually due to hackers’ never-ending cyber onslaughts. However, investing in and retrieving stolen funds costs financial institutions billions more.
As attacks become more sophisticated, money-handling institutions must incorporate strong fraud-prevention techniques into their plans to safeguard their clients and themselves from excessive expenses. Institutions need to detect and determine anomalies by analyzing critical accounting and broader financial data and relationships within the ledgers or accounts or other datasets.
Anomaly detection, fraud detection, and outlier detection are the terms commonly heard in the A.I. world.
Being able to detect such anomalies promptly allows one to take action and restrict or limit the negative consequences of such anomalies. For example, anomalies in financial transactions can indicate credit card theft or money laundering operations, which can be stopped if acted upon swiftly.
Anomaly Detection in Fraud Analytics
Anomaly detection is used in different fields, such as health insurance and retail, and several areas such as fraud detection, performance optimization, and data quality improvement.
While having different terms and suggesting different images to mind, they all reduce to the same mathematical problem, which is in simple terms, the process of detecting an entry among many entries, which does not seem to belong there.
For example, credit/debit card fraud detection, as a use case of anomaly detection, is the process of checking whether the incoming transaction request fits well with the user’s previous profile and behavior or not.
For example, Mr. J is a hard-working man who works at a factory near NY. Every day he buys a cup of coffee from a local cafe, goes to work, buys lunch, and on his way home, he sometimes shops for groceries. He pays bills with his card and occasionally spends money on leisure, restaurants, cinema, etc.
One day, a transaction request is sent to Joe’s bank account to pay for a $30 payment at a pizza hut near Austin, TX. Not knowing whether this was Joe on a vacation or his card has gone missing, does this look like an anomalous transaction?
What if someone starts paying $10 bills with Joe’s account on a “Card-holder-not-present” basis, e.g. online payment? The banking institute would want to stop these transactions and verify them with Joe, by SMS or Email.
There is no definite and certain answer to an anomaly detection problem, the answers are probabilistic and always depend on the perspective from which we are looking at the data.
Anomaly detection techniques in different fields, such as health insurance and retail, and several areas such as fraud detection, performance optimization, and data quality improvement.
Final Thoughts
Anomaly detection stands as a critical tool in safeguarding financial, healthcare, and retail industries from potential threats and inefficiencies. At its core, it identifies patterns that deviate significantly from the norm, allowing institutions to preemptively address risks, control failures, and even discern business opportunities. With the ever-evolving sophistication of cyberattacks and fraudulent activities, the urgency for robust anomaly detection systems has never been greater.
Leveraging this technology, institutions can promptly respond to red flags, such as irregular transactions, thereby minimizing detrimental impacts and ensuring the security of both their assets and those of their clients. Despite the varied terminologies across sectors, the essence of anomaly detection remains consistent: identifying that which doesn’t fit the mold, thereby ensuring streamlined operations and bolstered security.
