Fraud Detection Via Artificial Intelligence: How AI Gives Wings To Banking Sector?

In this modern world, our lives are increasingly depending and entirely revolving around technology. Among the various types of technologies available in the market, Artificial intelligence (AI) is one of the top-most technologies which came into existence. There are  wide range of AI applications that blows the mind of many people up to a huge extent. Artificial intelligence can play an essential role in fraud detection in the field of finance & banking industry.  

As most of the users now use banking online, therefore there is a rise in the risk of being duped by fraudsters than before. According to the latest estimation done by Artificial Intelligence Experts, cybercrime & financial frauds are costing the global economy around 600 billion dollars currently. Thus, Artificial Intelligence reaches the rescue. Nowadays, the AI Developers say approx. 26% of the venture funding increased for AI in the banking sector is for fraud applications. In this blog, we will explain the main techniques that AI banks may use to detect payment fraud, credit fraud, or onboard fraud.  

Overview- Need for transformation 

In this modern era, most of the financial services sector is already on the verge of a significant transformation, wherein Artificial intelligence is the main driving force behind it. The improvised AI apps have already been identified in the areas that include credit scores, regulatory compliance, customer experience, as well as portfolio management. With the rapid technological advancements, all the tasks can now be conducted in a matter of seconds. Moreover, chatbots & customer-oriented AI projects are coming into the limelight.  

What is the importance of AI in Fraud Detection? 

There are many AI applications in various areas, wherein it also comes with significant influence in fraud detection. Now let us discuss the importance of Artificial intelligence in Fraud Detection. 

1. AI has incredible Efficiency 

You must be already aware that the average transaction volume managed by banks per second is regularly increasing. If a machine learning model has been built already, then these models can withstand a high number of real-time transactions. They are also efficient and quick to adapt & thus find the latest methods of fraud in a minimum possible time. 

2. It helps to lower ownership costs 

As cloud computing is now becoming more affordable, your expense of running ML fraud detection models may be kept low. It can be reduced by the volume of transactions. 

3. AI offers greater Adaptability 

There are many banks with offices in ample geographies. In this case, you may need to tackle various types of fraud in every region. 

4. Speedy and Efficient 

It has been observed that in the rules-based systems, people may create ad hoc rules in order to determine which types of orders need to be accepted or rejected. This process seems quite time-consuming & comprises manual interaction. But contrarily, it is essential to have a faster solution in order to detect fraud. Merchants  need quick results in seconds. Only Machine Learning (MI) techniques allow doing this.  

Get a clear idea about how AI helps to detect fraud

Primarily all banks generate a large amount of data on a daily basis. These types of data are basically used to train MI models that help flag a given transaction as fake or not. Fraud detection in MI comes under the category of anomaly detection. It basically involves taking all of the data & then creating a graph. But there can be few transactions out of a specified range. They are referred to as anomalies.  

Challenges faced by the AI 

Though Artificial Intelligence (AI) is radical, but still, it may also encounter some challenges.  

1. Insufficient Data 

It is also called the issue of the cold start. In case there will not be sufficient data, the accuracy of the models built by MI algorithms might change below. Most of the Smaller banks could have trouble in implementing such kinds of methods. 

2. Problem occurred due to lack of clarity 

Sometimes, the situation may arise that it becomes challenging to explain why certain transactions have been considered fraudulent. In some cases, reversing the engineering of such type of situations may become impossible.  

3. Confusion while choosing the correct algorithm 

It has been observed that the machine-learning algorithm might offer different levels of success for other banks. Therefore, knowing which algorithm to use may be difficult. It is always recommended to work with an established MI solution provider. 

Final Words: 

As criminal activities are on the rise,  therefore, the banks & financial sectors are making use of AI’s power to protect their companies from various types of frauds. Thus, they tend to enhance the customer experience with the help of AI. By applying AI to detect fraud basically enables the financial firms to deeply identify real transactions vs. fraudulent transactions in real-time along with a high level of accuracy. As old rule-based algorithms fade into the past, adoption of the new top-notch methods that are Machine Learning algorithms for fraud detection is the new safe. It will surely bring more excellent value to your businesses and thus provide a high level of satisfaction to your customers.

Liza Kosh is a senior content developer and blogger who loves to share her views on diverse topics. She is currently associated with Seasia Infotech, an artificial intelligence company. She holds great knowledge and experience in technical and creative writing.