According to the latest reports by Artificial Intelligence Experts, cybercrime and financial fraud are presently costing the global economy 600 billion dollars. It’s equivalent to 0.8% of global GDP and calls for more robust security mechanisms than ever before. This is where Artificial Intelligence reaches to the rescue.
More than almost any other use-case category, AI Developers statistically say approximately 26% of the venture funding raised for AI in the banking sector is for fraud and cybersecurity applications. In this article, we explain the main approaches that AI banks can use to detect payment fraud, credit fraud, and onboard fraud. We also analyze the data that each method requires and how this data is used for fraud detection.
Learning Of Blog
- Overview
- Importance of AI in fraud detection
- How AI helps to detect fraud
- Challenges faced
- Wrapping it up
AI, coupled with the rich data sets available in the financial services sector, provides organizations with the means of defending their businesses and defeating criminals. Let’s have a look at how.
Overview
The financial services sector is already on the eve of a significant transformation, and AI is the driving force behind it. Innovative AI applications have already been identified across areas such as credit scores, regulatory compliance, customer experience, and portfolio management. Thanks to rapid technological advancements, tasks that once took employees hours to complete by a manual process can now be conducted in a matter of seconds. Now chatbots and customer-oriented AI projects are getting much hype. Banks also post press releases trumpeting their new and most fabulous chatbots.
- Prioritizing AI to reduce risk makes sense for three reasons. First, it is costly not to detect fraud and money laundering.
- Second, effective risk management systems make banking customers more relaxed and boost the credibility of the bank. Customers are more inclined to use banks with a track record of preventing cyber attacks, for example.
- Third, banks will make significant time and cost savings by using AI to automate absolute compliance, fraud detection, and cybersecurity activities. Today, this requires tremendous human effort, and existing rules-based anomaly detection systems leave much to be desired. You, too, can equip yourself by applying for an Artificial Intelligence Course Online with a Certificate.
Importance of AI in Fraud Detection
We know many applications of AI in numerous areas. Now let us see its influence in fraud detection.
Efficiency
The average transaction volume handled by banks per second is increasing year over year. Already if a machine learning model has been built, these models can withstand a huge number of real-time transactions. They are also quick to adapt and find new methods of fraud in a short time.
Lower Ownership Costs
With cloud computing becoming more affordable, your expense of running machine learning fraud detection models can be kept low. It can be scaled or reduced by the volume of transactions.
Greater Adaptability
Banks with offices in multiple geographies may need to tackle different types of fraud in each region.
Speed
In rules-based systems, people create ad hoc rules to determine which types of orders are to be accepted or rejected. This process is time-consuming and comprises manual interaction. As the speed of trade increases, it is essential to have a faster solution to detect fraud. Our merchants want quick results in the next microseconds. Only machine learning techniques allow us to do this with the kind of level of confidence required to accept or reject a transaction. Machine learning can evaluate a vast number of transactions in real-time. It continually analyzes and processes new data. Moreover, an advanced model such as neural networks autonomously updating its models to reflect the latest trends.
How AI Helps to Detect Fraud
Banks generate an enormous amount of data daily. These data are used to train machine learning models that, in turn, help flag a given transaction as fraudulent or not. Fraud detection in machine learning comes under the theme of anomaly detection (also known as outlier detection). This involves taking all of the data and creating a graph. There might be a few transactions out of a specified range. They are called anomalies or outliers. For example, a specific customer may make his ATM and online purchases within a specified geographic range. There are services available that can map IP addresses to a physical address. When a transaction occurs unexpectedly from a new IP address far away, it may be marked as an outlier, and the operation will not be permitted. Here’s a picture that reveals this. Transaction IDs, source IP, start time, destination port, number of bytes are used to classify transactions as attacks or not.
Challenges Faced
AI is revolutionary, but there are also some challenges to it, like
Less Data
This is also known as the issue of the cold start. If there is not enough data, the accuracy of the models built by machine learning algorithms might below. Smaller banks could, therefore, have trouble implementing such methods.
Lack of Clarity
Sometimes it may be difficult to explain why certain transactions have been classified as fraudulent. In some scenarios, reversing the engineering of such cases may not be possible.
It Does Not know which Algorithm to Choose
The same machine-learning algorithm might have different levels of success rates for different banks. Knowing which algorithm to use could be a challenge. In these instances, it is recommended to work with an established machine learning solution provider.
Wrapping it up
As technology signs of progress and criminal activities become more complicated, banks and financial institutions are using AI’s power to protect their companies and enhance the customer experience. Applying AI to detect fraud enables financial firms to identify real transactions versus fraudulent transactions in real-time and with greater accuracy. While organizations may not be in a rush to move to advanced data analytics immediately, they should begin to examine their existing data, identify data requirements, and develop the expertise needed to start by employing Certified Artificial Intelligence Developers as soon as possible.