A decade from now most of the population preferred to buy things and essentials from markets around them. but now everyone prefers to buy online as it’s more convenient with more options at your fingertips. This also opens up new ways for the criminal world to access your private data and wealth.
In the year 2019 alone 3 million cases of identity theft was report in which 25 percent of them result in loss of money. Within just a period of 92 days between 1st October 2019 to 1st December 2019 Bengaluru saw a whopping 21,041 cases for online frauds related to debit ,credit cards and net banking resulting in more than 17.3 million dollars (128 crore) of loss.
therefore, Machine Learning in almost all fields of business: from stock prediction to self driving cars and more. Fraud detection becomes possible. though, due the ability of Machine Learning and AI models to learn from historical fraud patterns and recognize them for future transactions. This technology has proven to be extremely efficient for detecting frauds.
How does Fraud Detection work?
For fraud detection the Machine Learning algorithms identifies certain characteristics of fraudulent transactions that differentiate them from the legitimate ones.
Whenever a user carries a transaction the Machine Learning model scans the user’s profile for information. If such patterns are discovered on the user, that used is blocked or sent for manual review.
The first step is the data input. works with huge amounts of data easily and the more data the ML model receives. the better it can learn and update its fraud detection skills.
Feature extraction is the next step where the fraudulent characteristics or behaviors differentiate from the legitimate ones. These features include identity, orders, customer’s location, organization, network and pick up payment method (but are limit to).
The exanimated features from the data depends on the complexity of the fraud detection system. Next, a Machine Learning training algorithm is launch. this algorithm consists of a set of rules.
it uses to determine fraud or legit transaction. The more data is provided for the training set, the better the training algorithm works.
At the end, the ML model is ready and it notifies and takes required actions suitable for the business as instructed in the algorithm by the organization.
Integration of supervised and unsupervised AI models
Organized crime schemes are sophisticated and quickly adapt. therefore both supervised and unsupervised AI models play important roles in fraud detection. It must construct comprehensive and ready for next generation fraud strategies.
A supervised model is most commonly use in all fields. This model is train on label data set transactions. Each transaction is label as fraud or legit. The model is train on massive amounts of label data to learn about the fraud and legitimate behavior patterns. For this model the amount of clean, feature distinguishing and relevant training is directly proportional to the model accuracy.
Unsupervised AI models on the other hand are design to identify suspicious behavior. In these models a self learning algorithm must be employed to surface patterns that are invisible to other forms of analytics.
Unsupervised models are able to identify outliers that were previously not detect. By building an optimal blend of supervised and unsupervised AI techniques.
a very effective and efficient model can be deployed that can detect previously unseen suspicious behaviors. however, it also can detect very subtle patterns that were previously observed for millions of transactions.
Adapting and improving in fraud detection
There is always room for improvement and accuracy can be further improve by expanding the dataset that is use to craft the predictive characteristics in the model.
Extensive research should be done using different modelling techniques on a variety of cases and training on massive amounts of data resulting in more critical predictions.
Given the speed and sophistication of organized frauds, behavioral profiles must be update with each transaction or activity. Along with those, using and upgrading of adapting techniques and increasing the sensitivity for detection.
by automatically adapting to recent confirm cases should the primary focus which will pinpoint fraudulent behavior and increase accuracy.
Conclusion
While we can’t reach the goal of 100% accuracy in fraud detection.
though, we can get close to the perfect model with each transaction and detection. With enough time and data, get very close to that goal. The nature of Machine Learning and Artificial Intelligence for these detection models allows for multiple algorithms to integrated together.
though as modules and their results can combine to increase the accuracy of the final result.
Written by: Akash Mitra
reviewed by: Shivani Yadav
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