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7 Best Practices for Preventing Fraud

Seven Best Practices for Keeping your Digital Banking Customers Safe from Fraudsters

Fraudulent activity within US financial institutions has been on the rise in recent years, particularly as more and more financial transactions take place online. Many financial institutions have reported an increase in fraud attempts, particularly those related to phishing and other social engineering techniques, as well as malware and account takeover attacks. The COVID-19 pandemic has also contributed to the increase in digital banking fraud, as more people have been banking online and using mobile devices to access their accounts.

Many digital banking platforms have not kept pace with this evolving threat and the growing sophistication of cyber criminals, and therefore, their customers’ operations are at risk.

What can banks and credit unions do to help prevent fraudsters from gaining access to their digital banking platforms and wreaking havoc on their customers’ and their operations? This post outlines seven of the best practices banks and credit unions should follow to protect their digital banking platforms.


Seven Best Practices for Preventing Digital Banking Fraud

1. Implement multi-factor authentication: This adds an extra layer of security by requiring users to provide multiple forms of identification, such as a password and a fingerprint or a one-time code sent to a phone, to access their accounts.

2. Monitor transactions for suspicious activity: Banks should use advanced algorithms to detect unusual patterns of activity, such as large transfers to unfamiliar accounts, and flag them for further review.

3. Educate customers about fraud: Banks should provide customers with information about common types of fraud and how to protect themselves, such as not sharing personal information or account details with anyone.

4. Encryption of sensitive data: Banks should encrypt sensitive data such as personal information and financial transactions to protect it from being intercepted by hackers.

5. Provide fraud alerts: Banks should provide customers with fraud alerts via email, SMS or push notifications to notify them of any suspicious activity on their account.

6. Implement a virtual private network (VPN) or secure socket layer (SSL) certificate: Banks should use a VPN or SSL certificate to secure communication between the customer's device and the bank or credit union’s servers.

7. Implement AI/ML based fraud detection: Banks can use AI/ML-based tools to detect unusual activity in real-time, which can help to quickly identify and prevent fraudulent transactions.

What is AI/ML based fraud detection?

AI/ML-based fraud detection is the use of artificial intelligence (AI) and machine learning (ML) technologies to detect and prevent fraudulent activities. Machine learning algorithms can be used to analyze data and identify patterns and anomalies that may indicate fraud.

There are several types of machine learning algorithms that can be used for fraud detection, including:

  • Utilize supervised learning: Banks can use supervised learning algorithms to train a model on a dataset of past fraudulent transactions. The model can then be used to identify patterns and anomalies in new transactions that may indicate fraud.
  • Utilize unsupervised learning: Banks can use unsupervised learning algorithms to identify patterns and anomalies in transaction data that may indicate fraud, without the need for a labeled dataset of past fraudulent transactions.
  • Utilize anomaly detection: Banks can use anomaly detection algorithms to identify transactions that deviate significantly from normal patterns. These algorithms can be used in conjunction with supervised and unsupervised learning.
  • Implement deep learning: Banks can use deep learning algorithms, such as neural networks, to analyze large amounts of data and identify complex patterns that may indicate fraud.
  • Combine AI/ML with rule-based systems: Banks can use a combination of rule-based systems and AI/ML-based systems to detect fraud. Rule-based systems can be used to flag transactions that meet certain pre-defined criteria, while AI/ML-based systems can be used to identify patterns and anomalies that may not be captured by the rules.
  • Continuously monitor and update models: Banks should continuously monitor the performance of their AI/ML models and update them as necessary to ensure they remain effective in detecting fraud.
  • Implementing explainable AI: Banks should implement Explainable AI models, to ensure that the decision made by the model are transparent and can be audited. It's important to note that AI/ML based fraud detection should be part of a comprehensive fraud detection and prevention strategy that includes multiple layers of security.

The iuvity Approach

iuviPROFILER helps digital banking and credit union customers fight fraud and follow best practices while also minimizing customer friction and reducing operational burden. iuvity’s fraud prevention solution is an AI engine that constantly analyzes user behavior and identifies anything unusual in our customers’ digital channels, through behavioral and biometric profiling of users. It works in the background and is transparent to customers, so it does not impede the user experience.

The service is powered by the iuvity Threat Intelligence Network (TIN): a fraud database developed by iuvity and enriched with global information that allows information to be shared anonymously. To learn more about iuviPROFILER, visit of request to speak with one of our security experts today.