5 Key Machine Learning Models For Fraud Prevention

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Business frauds have occurred in every sector of every industry for decades and have attracted enough attention from the management. Scammers have always found loopholes and taken advantage of the innocence and/or carelessness of business owners. This has continued even as we have entered the digital era.
Traditional frauds have now been replaced by cybercrimes and digital scams. Just like businesses, fraudsters have started making use of advanced technology and tools for deceiving businesses and stealing a substantial amount of money. This calls for improved fraud detection methods that are capable of countering the strategies employed by fraudsters. Detecting and preventing fraudulent activities has only become more complicated with time, making it necessary for organizations in adopting automated fraud detection predictive models. Fortunately, we live in an age of AI and machine learning. These technologies allow users to make sense of data, scan different processes, and learn patterns for making valuable predictions and analysis. Businesses have been using machine learning for fraud detection across the board to recognize the irregularities in doubtful transactions, notifying the users immediately for taking preventing measures. E-commerce and FinTech are two of the most affected areas when it comes to frauds and scams (especially digital). Right from payment frauds to credit card scams, organizations belonging to these sectors face maximum threats and are in need of a robust fraud detection service.
Here are five of the most important machine learning models that can be used by organizations to stay secure:

1. Making Use Of Supervised And Unsupervised AI Models
Over time, the nature of fraud has become increasingly dynamic, erratic, and unpredictable. Using a single standard fraud detection technique for all cases will never help you prevent every threat that comes your way. For this, you require an ideal combination of supervised and unsupervised AI models woven together in a cohesive way.

A supervised model is a commonly used machine learning model that makes use of properly tagged transactions. Here, every transaction is tagged as “fraud” or “non-fraud” and the machine learning system is fed with details about the nature of tagged transactions.

This allows the system to understand the pattern of different transactions and identify all subsequent transactions that are likely to be fraudulent in nature.

On the other hand, an unsupervised model is used for identifying deviant behavior in cases where the tagged transaction data is thin or non-existent. Here, a self-learning mechanism is installed to identify patterns in the data that are overtly invisible to the analytics.

2. Use Of Behavioural Analytics
As the name suggests, behavioral analytics involves anticipating the behavior of a transaction across all possible aspects. Here, the information of the transaction is tracked in specific profiles that are updated with every transaction taking place in an organization. These profiles represent the behaviors of every individual, merchant, or device involved.

A strong fraud detection service will combine a range of analytic models and profiles which contain all necessary details for understanding evolving patterns in a transaction in real-time. Any deviation in these patterns would alert the organization of a potential threat.

3. Differentiating Between Generic And Specialized Behaviour
There are generalists and specialists in several professional fields, including law, medicine, construction, etc. Similarly, behavioral analytics for fraud detection can be carried out on the basis of generic and specialized behavior.

In the case of fraud detection machine learning, the AI technology relies on raw data and predictive characteristics acting as inputs to a model producing a score. These characteristics represent specific patterns and relationships within transactional data discovered with machine learning.

By using this model, data scientists who are well-versed with the domain knowledge improve this discovery process by analyzing the weights, portions, and combination of predictive characteristics of fraud detection.

4. Expanding Large Datasets
The depth and breadth of data are often considered to be more impactful to a machine learning model performance as compared to the smartness of an algorithm. According to this theory, it is possible to improve the predictive accuracy of a system by expanding datasets used for creating the predictive characteristics used in a machine learning model.

Just like a physician sees thousands of patients during their training as it allows them to diagnose patients with increased expertise, a fraud detection model will benefit from the experience gained by ingesting as many examples as possible into the system. These examples can contain both legitimate and fraudulent transactions, allowing the machine to do the thinking!

5. Adaptive Analytics And Self-learning AI
To ensure a steady improvement of performance, a fraud detection service should make use of adaptive technologies that are designed for sharpening machine learning responses, especially on marginal decisions.
Here, transactions that are very close to investigative triggers are taken into consideration – the ones that are either just above or just below the cut-off. Accuracy of fraud detection is most necessary on these margins as this is a region where there is a thin line between a false positive event and a false negative event. The use of adaptive analytics highlights this distinction with a thorough knowledge of the threat that an organization is likely to face.

The Final Word
Business frauds are increasingly getting serious with time and it is important for organizations to start taking fraud detection seriously. A plethora of e-commerce ventures and financial lending organizations face the issue of serious frauds that are difficult to tackle.

The digitization of services is one of the key reasons behind the frequent act of siphoning off of money. Having said that, equally competent (if not more powerful) measures are required to counter these frauds and prevent them before things get out of control.

Advanced machine learning technology allows organizations to make sense of the data fed into the system, identify patterns, analyze transactional behaviors, and notify the management about a potential threat. This makes it possible for organizations to take all possible precautionary measures against fraudulent activities and keeping the valuable treasury safe from all kinds of threats.

 

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