Every year, billions of dollars are lost to fraud, doing unquantifiable damage to businesses, banks, and consumers across the nation. The unfortunate truth is that fraud is only getting more creative, deceptive, and cunning as time goes on. However, AI and machine learning have developed several effective defenses against fraud, safeguarding your financial accounts at all moments of the day. In this article, Daniel Calugar describes what AI is doing to protect your accounts from fraud.
AUTOMATIC FRAUD DETECTION
It’s important not to interrupt or interfere with daily transactions, but, at the same time, we need to be confident in the security of those transactions. Machine learning systems can familiarize themselves with a user’s history, preparing fraud detection systems to identify significant deviations from that history. In doing so, potentially fraudulent transactions can be flagged for further assessment by a fraud analyst or prevented entirely.
Further, using supervised and unsupervised data models, AI can teach itself how to recognize emerging patterns in fraud attempts. This allows fraud detection systems to better identify shifts in fraudsters’ tactics and lock down those attempts before analysts can catch up, all while minimizing false positives. These tactics include more complex abuse attacks such as refer-a-friend abuse, promotion abuse, or seller collusion.
RISK MANAGEMENT STRATEGIES
Machine learning technologies are being used to analyze fraud risk management strategies in financial institutions to centralize potential risks and present mitigation recommendations. Data analysis offered by these algorithms provides specific insights from personal data to bolster risk management techniques.
Data Regulation Compliance
Without rigorous management, it’s easy to drift away from standard protocols in data management. By leveraging artificial intelligence, financial institutions can be pushed to comply with current regulations and maintain data quality.
STREAMLINING PROCESSES FOR FRAUD ANALYSTS
Using artificial intelligence, you can instantly process a clearer view of transactional history for quicker and more reliable decision-making. Risk scores and anomalous transactions can be viewed in real-time, significantly reducing the hours necessary for analysts to make the same assessments. Without collecting and interpreting vast amounts of data, fraud analysts are more prepared than ever to put transactions in context and validate decisions in setting threshold scores.
PHISHING SCAM PREVENTION
While not strictly related to financial institutions, several email services, such as Google’s Gmail, use machine learning to predict, identify, and prevent phishing attempts for their users. A phishing attempt prompts a user to open a link which then presents a login screen. This login screen often mimics a website with which the user is familiar.
If that user takes the bait and enters their login credentials into the false site, that information is then harvested to make fraudulent transactions on that account. Phishing attempts target all types of accounts, including financial accounts. However, with the AI employed by email providers, like Gmail, phishing attempts are either sent directly to a designated spam folder or are flagged by prominent, visible warnings to alert otherwise unsuspecting users.