Artificial Intelligence Machine Learning Algorithms

Artificial Intelligence Machine Learning Algorithms

Banks and other financial institutions have to deal with financial losses each year due to various fraudulent activities.



According to one survey conducting by a leading financial management company, banks and other financial institutions are able to recover less than 25% of the financial losses due to fraudulent activities. Also, the fraudulent activities aren’t just restricted to the financial institutions, but each year billions of dollars are lost to such frauds across all industries; especially for online businesses.

This number is high enough to demonstrate the importance of fraud prevention to minimize the fraudulent losses for banks and other financial institutions. Fortunately, the rise in digital technologies like artificial intelligence (AI) and machine learning (ML) can help in mitigating such frauds by detecting frauds in real-time and with much more preciseness.

To put it simply, fraud detection is the process of detecting suspicious activities that can potentially breach the sanctity of a business and lead to reputational or financial losses.
Interestingly, a survey of the fraudulent activities faced by banks and financial institutions found that there is a close pattern amongst frauds carried out across the globe.



Before the rise of digital technologies like AI and ML, many of the fraud detection measures involve complex analysis of structured information, which wasn’t just tedious activity but also less accurate as well. Much of the time consumed at those times was consumed by computers identifying the repetitive patterns over and over again, and to make matters worse, many times the analysis of the structured data returns false positive. The other way around was to use a rule-based approach, however, this again was a time-intensive approach which also requires a lot of human involvement to detect fraudulent activities.

Now, if we are to look closely at the definition of fraud, it’s a criminal activity and most of the time hit the digital businesses that run of technological tools. However, on the positive side of things, technology is also creating more powerful tools to detect and prevent fraud. Some of the more powerful technologies that are leading the battle against fraudulent activities include:

- Data Mining

The technology segments and classifies the data. Once the data is classified, it starts to look out for similar patterns pertaining to suspicious or fraudulent activities or behaviors.
- Artificial Intelligence and Machine Learning

Artificial Intelligence and machine-learning algorithms are one of the most powerful ways to automate pattern identification in massive complex datasets. Once the patterns are identified, these can be classified as a fraud for future reference.
- Neural networks

This is yet another advance technological method used to identify suspicious patterns from complex datasets and later used to stop fraudulent activities

With the increasing trend of Fintech, fraud detection has become pivotal in keeping up with the sanctity and security of financial and personal information of clients. To make matter worse, the fraudulent activities are also getting technologically complex, making it even more necessary to rely on technologically advanced fraud detection techniques to counter and mitigate such activities.



Another great challenge faced by banks and financial institutions is the growing volume of data to handle. With massive data being generated each day, relying on traditional fraud detection techniques is no more viable option for financial institutions.
With all these challenges upfront, financial institutions need to invest in data-driven technologies like artificial intelligence (AI) and machine learning (ML) to identify such fraud activities and be better prepared to contain such instances in the future.

Creating a fraud detection framework using Artificial Intelligence and Machine Learning
The key to creating a robust and reliable framework aimed against containing fraudulent activities is to understand that this will be a dynamic process, which will require consistent monitoring, improvement, identification, and implementation. The algorithms that you need to implement would require the data of the past instances to be able to learn and analyze the patterns and become apt in preventing future instances.

Another important consideration to remember when designing and implementing machine learning algorithms for fraud detection is that such AI-based models should have the capacity to differentiate between legitimate activities from the illegitimate ones. This is again possible by training the algorithm with the past data and them implementing it for future transactions. Fortunately, today there is a massive repository of all sorts of data available with banks and other financial institutions which could be used to train AI and ML-based algorithms.

Lastly, it is important to note that the fraud detection algorithms won’t be the same for all instances. Rather, these should be customized to meet the unique requirement for each business model. It’s important to train these algorithms with the real and local data sets to be able to make them more accurate in predicting and preventing future frauds.

Supervised learning vs unsupervised learning

Supervised learning models are one of the most common types of AI-based machine learning algorithms that are put to task to detect frauds. Such models are trained on local and real structured datasets of the respective organization for “fraudulent” and “legitimate” activities. While some advanced algorithms are able to automatically detect the signals for different classified data, most of them are needed to be trained before implementation. The viability and accuracy of classified learning models are highly dependent upon the richness and cleanness of the training data. One best example of supervised learning is AI image processing.

The other AI and ML model which is often put to detect frauds in banks and financial institutions is the unsupervised model. These models are designed to automatically detect and classify the “fraud” or “legitimate” activities from the data with minimal training required. The key to this approach is the need to structure the data in a way that these algorithms are able to learn about the data automatically. Since these are automated algorithms with the ability to detect complex patterns from massive datasets, these are more apt to identify and reveal subtle patterns that otherwise may not be visible in other kinds of analytics techniques.

Bottom Line

The growing trend of e-transactions across the globe has also led to an increase in cybersecurity concerns. Even though fraudulent activities has remained the norm for banks and financial institutions from the very beginning, the rise of technologically complex frauds has driven the need of even more sophisticated algorithms using the latest technology to be able to counter such instances and retain the sanctity and security of customers’ personal and financial information.

Artificial Intelligence and Machine Learning are undoubtedly two of the most potent and capable futuristic technologies that are being used worldwide to develop complex and automated algorithms to mitigate and counter fraud instances not just in financial institutions but also in all sorts of industries.


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