Swiss Banks Take Measures to Prevent Fraud

In an age where cybercrime is becoming increasingly sophisticated, Swiss banks are taking steps to protect their customers’ accounts from fraud attempts.

Internal and External Risks

According to Joël Winteregg, CEO of NetGuardians, attempting to defraud a bank is no longer a question of “if” but rather “when”. Winteregg identifies the greatest risks for Swiss banks as being both internal and external.

• Internal Risks: Fraud committed by employees is a significant concern. In Switzerland alone, there have been high-profile cases of employee theft, including a former wealth manager accused of stealing CHF3 million from customers’ accounts over two years.
• External Threats: Malware attacks, phishing, and social engineering are also major concerns. The latter involves using information gathered from social media profiles to impersonate officials and steal personal information.

Combatting Threats with Proactive Detection

To combat these threats, Swiss banks are employing proactive detection methods based on profiling and machine learning technologies. These solutions analyze a customer’s typical transactions, as well as other factors such as location, language, and screen resolution, to identify suspicious activity. By combining multiple variables, false hits can be reduced by over 80%.

Human Intervention is Still Necessary

However, human intervention is still necessary in exceptional cases where AI-based solutions may not be able to accurately detect fraud. “Augmented intelligence” combines the strengths of both technology and human decision-making.

Effective Utilization of AI-Based Fraud Prevention Solutions

For Swiss banks to effectively utilize AI-based fraud prevention solutions, they must consider data collection and storage. According to Winteregg, existing bank data can be used for machine learning algorithms, reducing the need for new data sources.

Future Developments in AI-Based Solutions

As the race against cybercriminals continues, advancements in AI-based solutions are expected. Future developments may include:

• Enriched machine learning algorithms using Markov chains
• Additional data sources from social networks
• Behavior- based biometrics

By staying ahead of the curve and incorporating these advancements into their fraud prevention strategies, Swiss banks can continue to protect their customers’ accounts from fraudulent activity.