With a history extending back to simple trade networks in ancient Assyria and Babylonia, the banking industry is one of the world's oldest, yet it has continuously remained at the edge of technological advancement.
FREMONT, CA: Artificial intelligence (AI) is becoming widely used in banking, especially risk management. Due to the recent global financial crisis, banks and financial authorities are paying much more attention than before to how risk is recognized, disclosed, and managed.
AI and ML may help financial organizations identify risk, make more informed credit decisions, and improve regulatory compliance.
Detection of transaction fraud in real-time
While fraudulent transactions account for a small percentage of financial transactions, they pose a significant challenge for the banking sector and any company that relies on digital payment activities. Additionally, COVID-19 has exacerbated this problem, as more individuals digitally pay for products and services, creating an ever-growing attack surface for fraudsters.
Banks have previously utilized fraud detection models (a) created for physical credit card transactions and (b) organized around rule-based systems based on lessons learned from previous occurrences.
Today, however, internet purchases do not require the actual presence of a credit card. Furthermore, rule-based systems have not proven to be sufficiently flexible to the fast-paced world of current digital commerce or the complex payment fraud methods created by criminals.
This is why many companies involved in e-commerce, particularly banks, have shifted away from rule-based systems and toward machine learning models. Machine learning enables AI systems to modify continually and even learn new rules as additional data is processed—providing an immediate advantage when confronted with the numerous dangers and types of fraud.
This challenge makes use of two distinct types of machine learning algorithms: supervised and unsupervised:
Supervised learning identifies fraudulent trends by analyzing previously annotated historical data (with instances of fraud activity manually tagged).
Unsupervised learning is used to analyze unlabeled information and discover correlations and variable connections that may be invisible to human investigators.
Both approaches are ultimately complementary since supervised techniques learn from previous fraudulent actions, while unsupervised techniques enable the identification of novel forms of fraud. And by integrating the two, banks may conduct a comprehensive analysis of transaction flows, discover subtle trends in a user's purchase experience, and correctly identify and flag fraudulent behavior in real-time without requiring human interaction.
This enables people to focus on more complex fraud situations and the effort required to handle issues arising from fraudulent transactions—and consequently yields improved outcomes for financial institutions and their customers.
