Guidelines to Employ Machine Learning Algorithms for Fighting Fraud
In this article, check out some guidelines to employ machine learning algorithms for fraud prevention.
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Fraud Prevention isn’t everyone’s cup of tea. By the time financial institutions catch up with the latest criminal tactic, fraudsters come up with a new one to take its place. Because of this obligation to constantly upgrade against scammers, it is always an ongoing challenge for financial institutes to stay neck and neck with criminals.
At the same time, the finance sector is spending considerable budget, time, and effort to develop or adopt more advanced technologies for fraud prevention. However, one thing they may be lacking is the technology that could adapt and change as hastily as fraud tactics.
Organizations traditionally rely on rule-based algorithms to deter fraud. Rule employs if-else logic, which can be thorough at detecting known patterns of fraud. Even though rules, if combined with advanced approaches, remain important fraud-prevention tools, they are limited to known patterns. They couldn’t adapt to unknown fraud patterns and schemes and are not effective at identifying sophisticated fraud techniques.
Machine Learning (ML) algorithms, learning and adapting to every bit of data processed, can make an impact in fighting fraud. An optimally designed AI system is not only adept at new changes, but it also uncovers new patterns without over-adaption, which could result in too many false positives.
That is why more and more industries are adapting to machine learning and artificial intelligence for detecting and preventing fraud. According to a survey, 80% of the fraud prevention specialists employing AI-based solutions believe that AI is effective against fraudsters.
However, the problem that persists is figuring out which ML algorithms are efficient in detecting unknown fraud patterns. Are supervised learning or unsupervised learning algorithms more effective?
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Which ML Algorithms Should You Employ for Fraud Detection?
Simply put, machine learning automates the process of extracting known and unknown patterns from data. Meanwhile, it also recognizes that fetched data patterns and applies them to unknown or real-world data. The system also learns and adapts as new patterns and outcomes are presented to it via a feedback loop.
Supervised or Unsupervised ML Models
Learning and adaption differ in supervised and unsupervised learning models. In supervised learning, ML models try to learn from the known data patterns, also known as labeled data. To train a supervised ML model, both fraudulent and non-fraudulent data records are presented to the algorithm, and the data is labeled.
On the other hand, unsupervised ML algorithms work differently. Unlabeled data is presented to the model, which learns the data structure on its own. This helps in the detection of unknown patterns from the data.
5 Essentials for Robust ML Models
So how does it work? Which components are essential to implement a robust ML system for fraud detection? For applying ML to fraud prevention, the following components are required:
- Data: Be it AI or ML, quality data is fundamental for building an anti-fraud system. The amount of data available today is large and is considered the new currency of the 21st century thanks to the formula that more data is equal to added accuracy in fraud detection when it comes to data-driven AI-models. However, the major challenge the firms face is having an AI platform that can scale up with an increase in data and complexity.
- Profusion: No single ML algorithm either supervised or unsupervised could work best for fraud detection alone. You to need to employ different algorithms or methods and test them using different data samples to achieve success.
- Integration: Only 50% of the AI/ML models developed make it to market, resulting in loss of effort and hard work. Having data in Hadoop means your model can be applied in Hadoop only. On the other hand, if your data is streaming in real-time systems, an ML algorithm that could be embedded with those systems is required. That's why it is essential to develop portable integration for your model such as APIs.
- Ongoing Monitoring: Ongoing monitoring is essential and it is what makes ML models more efficient than simple rule-based algorithms. A good ongoing monitoring program could register and track the ongoing efficacy of ML models.
- Experimentation: Fraudsters are clever and technology changes quickly. So, building and deploying ML models for fraud detection is not enough. Having a platform where AI scientists could continuously test and enhance the ML model based on new techniques and data is necessary.
Handling Fraud, Enhancing Customer Experience, and More
Detecting fraudsters while delivering delicate customer experience is a difficult art. An organization with a system that accurately predicts and deters fraud while having cumbersome authentication measures is apt to lose customers.
Other than fraud prevention, there are a number of ways artificial intelligence can transform the banking sector. Seamless customer experience, mobile banking, risk management, and cost reduction are some ways AI is contributing.
Tactics of bad actors are becoming increasingly sophisticated as they constantly adapt new methods to exploit the financial system. Fraudulent transactions, while in small proportion, can have far-reaching consequences and could result in million-dollar losses. With the advances in AI, businesses can employ systems that learn, adapt, and uncover emerging patterns for fraud prevention.
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