AI to Detect & Prevent Financial Fraud



The banking industry is undergoing a transition from traditional paper-based system to digital payments. The problem with this new system is that it's not foolproof and can be hacked easily. Banks are constantly looking for ways to prevent fraud and detect fraudulent activity on their systems. This is where artificial intelligence comes into play.


One way they're doing this is by using machine learning techniques, which are computers that have been trained to recognize patterns or understand language. For example, if you give a machine learning algorithm a bunch of data sets of known photos and images, it can be used to recognize new images. This phenomenon is what allows AI to be powerful and beneficial to the industry.


Machine learning algorithms are quickly becoming an indispensable tool for banks looking to prevent fraud and detect it if it occurs. The number of cases of digital banking fraud is expected to increase in the coming years and banks are looking to prevent as much of it as they can with AI.


How Might AI Help Stop Digital Banking Fraud?


Banks are always on the lookout for new ways to prevent fraud in their money transfer system. One way is by hiring more people to help screen questionable transfers. However, this can become expensive and time-consuming. The other way is to use AI to screen transfers. This can be done in a couple of ways:


1. Through the use of "Bayesian Belief Networks" that analyze a network of actors involved in a transaction and the variables that affect the likelihood of that actor committing fraud.


2. By using "Natural Language Processing" that analyze the words in a transaction and calculate the probability that the transaction is fraudulent.


Backing up the first method is the use of "Bayesian Networks" that use Bayes' rule to determine the probability that a transaction is fraudulent.


Backing up the second method is the use of "Markov Models" that analyze similar past fraud patterns to come up with a more accurate prediction of whether a transaction is fraudulent or not. The second method involves using "fuzzy logic" that analyzes variable meanings of words in a message and the probability that those words are being used randomly by a human rather than with intent.


Another emerging method involves using "Neural Networks" which are made up of neurons that communicate with each other and recognize patterns in data. The more patterns that are recognized, the stronger the connection between the patterns and the higher accuracy of a prediction.


DataVisor is a California based AI start-up that provides fraud detection solutions. It develops big data solutions that predict attack vectors among various users and accounts. It also provides security, analytics, and infrastructure solutions for predictive threat management.


Teradata is another firm that uses AI to detect and prevent fraud. The company’s solution is used by U.S. Bank to predict threats and deeply personalize the banking experience for its customers. Other companies providing AI-based solutions for fraud detection and prevention include Polish company Nethone, Indian start-up ADVARISK, French-based Shift Technology, etc.


Anti-fraud surveillance


Anti-fraud surveillance focuses on looking for suspicious behavior that could indicate a potential fraud attack. One potential behavior is people making many transactions of small value. If you're sending small amounts of money to lots of people, then you may be attempting to send the money to someone using a "pen pal" method or a similar scheme.

Another potential behavior is a large number of transactions from a single IP address. Whenever a person opens up a new account, the first thing they usually do is send a large amount of money to test the system.


AI companies like "SentinelOne" are developing solutions to help prevent fraud. The company provides endpoint protection using AI and machine learning through its Autonomous Al Platform.


Anomaly Detection