By analyzing large datasets, AI models can learn to recognize the difference between suspicious activities and legitimate transactions, and they can help identify possible fraud risks to prevent financial crime—even catching trends that a human agent might miss.
Financial institutions are increasingly integrating AI solutions into new and existing workflows to improve decision-making, fraud prevention and risk management. AI-powered machine learning models trained on historical data may use pattern recognition to automatically catch and block possible fraudulent transactions from being executed. They also may require human agents to complete extra authentication steps to verify a suspicious transaction. AI technology can also use predictive analytics to estimate what types of future transactions a person might make, and it can recognize if a new type of transaction or transactional behavior is unusual.
In these ways, AI fintech can help protect individuals from financial losses resulting from various types of fraud, including phishing scams, identity theft, payment fraud, credit card fraud and other varieties of banking fraud.
AI fraud detection systems are not perfect, and some false positives may negatively impact the overall customer experience. But preventing fraudsters from committing financial crimes ranging from unauthorized charges to money laundering is paramount to ensuring the security of client accounts and maintaining regulatory compliance for financial institutions.
As advancements in AI technology continue, both AI-driven fraud prevention providers and leading financial institutions are banking on AI to become an even more valuable tool in preventing fraud attempts and mitigating fraud risks.