Banks are outsmarting the nefarious scammers the old-fashioned way by combining automation with customer contact to address any scammer that comes their way.
Banks, credit unions, and the likes hold millions of dollars in assets and consequently have large targets behind them. Bank robbers don’t bother with guns and masks anymore. They have simply updated their processes. They have now resorted to digital tools and resources to impersonate customers and hack accounts.
Fraudsters are updating their ways, and it is challenging for banks and credit unions to be on par with the various tactics. But, there is one thing at our disposal: technology. Various robust options are now available to pull out potential frauds and secure customer accounts. Algorithms, AI, and biometrics have successfully saved millions of dollars, all the while making accessing funds convenient for the account holders.
Why People-Centric Systems are a problem:
Protecting data was easier before cloud applications. A firewall of sorts was enough to secure data from most threats. Things have changed now.
Companies have started migrating critical workloads and storage to the cloud, and the protection offered from the data center diminishes with the perimeter vanishing. Security measures had to update with millions of people accessing data from the cloud. The system has to keep the data from external threats and ensure the people accessing it are the ones they pose to be. People-centric systems hence pose a new challenge for financial institutions.
Call center staff must be provided proper education; they need to properly verify a person’s identity before diving into any sensitive account data. In addition, financial institutions are adopting new technology to pin problematic transactions or unauthorized access to accounts.
The Bond of Algorithms and AI
Automated fraud detection systems were dependent on algorithms earlier to make out potential problems. For example, unusual transactions or spending behavior triggered an alert and asked the customer to verify the account activity.
But the algorithm cannot adapt itself. Suppose a customer flies to a foreign country monthly and confirms all their purchases as legitimate; the algorithm will go on flagging those transactions until it has been modified to account for this customer behavior. Here is where artificial intelligence, or machine learning, comes in.
Instead of waiting on the algorithm to adjust, AI can alter the model with new data. With time, the system learns to predict based on purchase history. For instance, a large purchase internationally can easily pass through the system without any triggers if it matches the customer’s past purchase behavior.
There is yet too much to explore regarding the potential of machine learning and artificial intelligence to counter security threats.