How Banks Can Detect and Prevent Scams

Robert Harris

Scams are surging in the digital banking age. Easy access to personally identifiable information, social media, and faster payment services from rails to person-to-person tools only make it easier for fraudsters to trick consumers into a variety of schemes.

To combat the threat of scams and fraud, banks must take a multi-faceted approach that includes preventative and detective measures.

Educate Customers About Scam Risks

Educating customers about the risks of scams and how to protect themselves is one of the most effective ways to prevent them. Banks should provide customers with clear and concise information about the most common types of scams, including phishing, smishing, romance, and investment scams, and offer tips for avoiding them.

Just as banks should educate their customers about scams, they should also provide regular training and awareness programs to their staff to ensure they are equipped to identify and prevent fraud. Still, more is needed. Banks have to invest in scam prevention technology such as machine learning fraud detection software, two-factor authentication, and biometric authentication to identify and prevent contagion before it occurs.

Implementing machine learning is essential for adequately equipping fraud teams to detect and prevent fraud and scams.

5 Ways Machine Learning Helps Detect and Prevent Scams

Machine learning plays a critical role in helping banks detect and prevent scams. Here are just some of the benefits banks gain with machine learning:

  1. Anomaly Detection: Machine learning algorithms can analyze transaction data and identify unusual patterns or anomalies that may indicate fraudulent activity. Banks can quickly identify and flag suspicious transactions using machine learning to detect anomalies for further investigation.
  2. Behavioral Analytics: By analyzing customer behavior over time, machine learning algorithms can learn what is normal behavior for a given customer and identify deviations from that behavior that may indicate fraud.
  3. Natural Language Processing: AI can analyze text-based data, such as emails or chat transcripts, to identify fraudulent messages. Natural language processing techniques allow machine learning algorithms to identify patterns and keywords that may indicate fraud, allowing banks to flag and investigate those messages.
  4. Image Analysis: Machine learning algorithms can also analyze images, such as scanned checks or ID cards, to identify fraudulent documents. By using image analysis techniques, machine learning algorithms can identify signs of forgery or tampering that may not be visible to the human eye.
  5. Decision Support: Machine learning algorithms can provide decision support to human analysts, helping them identify patterns and anomalies that may indicate fraudulent activity. By using machine learning algorithms to augment human decision-making, banks can improve fraud detection and prevention accuracy and efficiency.

What Banks Should Look for in a Machine Learning Solution to Prevent and Detect Scams

With scams rising, banks must invest in machine-learning solutions to detect and prevent authorized payment fraud. But not all machine learning fraud and scam prevention solutions are the same. Choosing the right solution can be daunting.

Here are some key factors banks should consider when selecting a machine-learning solution to prevent and detect scams:

  • Accuracy: Accuracy is critical when evaluating a machine learning solution. Banks must look for solutions with a proven track record of accurately detecting and preventing scams.
  • Flexibility: Machine learning solutions must be flexible enough to handle various data types and formats. Banks should look for solutions that can analyze multiple data types, including structured, semi-structured, and unstructured data.
  • Speed: In today’s fast-paced world, banks need machine learning solutions to analyze data in real time. Banks should look for solutions that can quickly process data and provide insights within seconds.
  • Explainability: Banks need to understand how the solution works and why it flagged a particular transaction as fraudulent. The system must be transparent enough to allow banks to easily explain their actions to regulators.
  • Cost: Machine learning solutions can be expensive. Often, you get what you pay for, and banks need to consider the total cost of ownership. Still, banks need to evaluate the cost of the solution and ensure that it is cost-effective for their business.
  • Scalability: As the volume of data grows, banks need machine-learning solutions that can scale to meet their needs. Banks should look for solutions that can scale up or down based on their business requirements.
  • Integration: Any new solution must integrate seamlessly with the bank’s existing systems and processes. Banks should look for solutions that can easily integrate with their core banking systems and other fraud prevention solutions.

The scams epidemic shows no sign of slowing down. With liability shifting to banks to cover scam losses in some regions, now is the time to study how machine learning can protect both banks and consumers from the scams threat.