Artificial Intelligence in Banking Fraud

Artificial Intelligence in Banking Fraud

📅 Date: 20th of February 2025

🕙 10:00 AM London Time

🌍 Free Webinar

We're excited to offer this free webinar as part of GCI’s commitment to keeping compliance and financial crime professionals worldwide informed on the latest trends. Our responsibility is to bring top industry experts to share their knowledge, empowering you with the insights needed to thrive in a rapidly changing landscape. Join us for this incredible opportunity to learn from the best—at no cost to you!

AI is playing a pivotal role in transforming fraud detection and prevention within the banking sector. By leveraging advanced machine learning (ML) and artificial intelligence (AI) technologies, financial institutions are able to detect fraudulent activities with greater accuracy, speed, and efficiency while simultaneously reducing risk. The impact of AI is also reshaping the practices of both commercial organisations and criminal entities. Here's an exploration of how AI is transforming fraud detection, reducing risks, and altering commercial and criminal behaviours

Our esteemed guest speakers, industry experts, will share their insights on:

Improved Fraud Detection with AI

• Driven fraud detection systems use machine learning models to analyse transactional data in real-time.
• These systems identify patterns and behaviours that deviate from the norm, flagging potential fraud.
• Machine learning allows systems to continuously adapt and refine their detection abilities based on past fraud incidents.
• AI can detect anomalies, such as simultaneous card usage in different regions or unusual spending patterns.
• Behavioural analytics enable the system to understand normal customer activity and flag deviations for further review.
• Real-time transaction monitoring prevents fraud before it occurs, with the option to automatically block or approve suspicious transactions.

Reducing Risk with Predictive Analytics

• AI allows banks to assess the risk of transactions and customer profiles before engagement.
• Machine learning models predict the likelihood of fraud based on historical patterns, user behavior, and risk indicators.
• AI can assign risk scores to transactions, flagging high-risk activities for review.
• Customer risk profiles are continuously updated using data such as spending behavior, location, and device usage.
• AI reduces false positives by becoming more accurate with exposure to data, improving customer experience and reducing operational costs.

Reshaping Commercial Practices

• AI automates compliance with anti-money laundering (AML) and Know Your Customer (KYC) regulations.
• AI systems cross-reference client data with regulatory databases to identify compliance issues faster and more accurately.
• Machine learning algorithms detect suspicious activities, such as large or complex financial transfers, that may indicate money laundering.
• AI streamlines the KYC process by verifying customer data and ensuring compliance with evolving regulations.
• Fraud detection tools powered by AI are available as a service, democratising access for smaller banks and fintech companies.
• AI improves customer trust and satisfaction by reducing fraud and minimising the number of false positives.

Reshaping Criminal Practices

• Cybercriminals are using AI to create more sophisticated fraud schemes, such as AI-powered phishing attacks and deepfake technology.
• AI is used to create synthetic identities, combining real and fake information to bypass fraud detection systems.
• Deepfake technology allows fraudsters to manipulate audio and video, tricking individuals into revealing sensitive information.
• AI enables the automation of attacks, such as scraping websites for credit card information or exploiting vulnerabilities in banking systems.
• Criminals are using AI to make scams more efficient, increasing the need for banks to incorporate AI into their defence strategies.

The Future of AI in Banking Fraud Prevention

• The integration of quantum computing with AI could dramatically enhance fraud detection capabilities.
• AI-powered biometric authentication (e.g., facial recognition, fingerprint scanning) will further enhance security, making it harder for fraudsters to impersonate legitimate users.
• AI will enable more effective cross-border fraud detection by incorporating data from international sources and analysing global fraud trends.
• Future advancements in AI will improve fraud detection, making systems more proactive in preventing fraud before it happens.

🔗 Reserve your spot today!