Machine Learning And Fraud Prevention

As early as the beginning of the Millennium computer software has been used to detect fraud. However, a brave new world is coming to the financial trade. It’s called artificial intelligence or machine learning and the software will revolutionize the way banking institutions detect and deal with fraud.

Everyone knows that fraud is a significant problem in banking and financial services. It has been so for a long time. However, today the effort of banks and other financial institutions to identify and prevent fraud now depends on a centralized method of regulations known as the Anti-Money Laundering (AML) database.

AML identifies individuals who participate in financial transactions that are on sanctions lists or individuals or businesses who have been flagged as criminals or people of high risk.

How AML Works

So let’s assume that the nation of Cuba is on the sanction lists and actor Cuba Gooding Jr. wants to open a checking account at a bank. Immediately, due to his name, the new account will be flagged as fraudulent.

As you can see, detecting true fraud is a very complex and time-consuming task and can result in false positives, which causes a whole lot of problems for the person falsely identified as well as for the financial institution that did the false identification.

READ ALSO:  A Way Out Available for Real and Automated Decisions: Artificial Intelligence

This is where machine learning or artificial intelligence comes in. Machine learning can prevent this unfortunate false positive identification and banks and other financial institutions save hundreds of millions of dollars in work necessary to fix the issue as well as resulting fines.

How Machine Learning Can Prevent False Positives

The problem for banks and other financial institutions is that fraudulent transactions have more attributes than legitimate transactions. Machine learning allows the software of a computer to create algorithms based on historical transaction data as well as information from authentic customer transactions. The algorithms then detect patterns and trends that are too complex for a human fraud analyst or some other type of automated technique to detect.

Four different models are used that assist the cognitive automation to create the appropriate algorithm for a specific task. For example:

  1. Logistic regression is a statistical model that looks at a retailer’s good transactions and compares them to its chargebacks. The result is the creation of an algorithm that can forecast if a new transaction is likely to become a chargeback.
  2. Decision tree is a model that uses rules to perform classifications.
  3. Random Forest is a model that uses multiple decision trees. It prevents errors that can occur if only one decision tree is used.
  4. Neural network is a model that attempts to simulate how the human brain learns and how it sees patterns.
READ ALSO:  ABCs of a Realistic African Diaspora Engagement

Why Machine Learning Is The Best Way To Manage Fraud

Analyzing large data sets has become a common way to detect fraud. Software that employs machine learning is the only method to adequately analyze the multitude of data. The ability to analyze so much data, to see deep into it, and to make specific predictions for large volumes of transactions is why machine learning is a primary method of detecting and preventing fraud.

The process results in faster determinations, allows for a more efficient approach when using larger datasets and provides algorithms to do all of the work.


by Robert K Janis