How Machine Learning Can Help With Fraud Prevention

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@anand-sivastavaAnand Srivastava

Owner of TipsBlogger.com

Technology, without a doubt, has eased up a lot of issues for us, including the likes of fraud prevention. But before we start talking about the technological inputs pertaining to the same, it is necessary to understand why fraud prevention is actually required by businesses and why this genre of functionality is wide-spread and extremely popular. Firstly, financial firms are probably the most affected ones as fraudsters are actually interested in siphoning off money more than anything else. Secondly, fraudulent activities aren’t restricted to one vertical and it is a challenge for the firms to develop newer strategies for combating evolving threats.
The Role of Machine Learning
Every technology isn’t the same and not many can keep up with the evolution of fraudulent activities. The usual techniques include the usage of rule-centric algorithms followed by the inclusion of the if-else and other relevant logics for keeping the frauds at may. However, these technologies are static and cannot adopt with the evolving fraud tactics. These techniques are only restricted to known patterns and it becomes difficult to ascertain the unknown patterns and techniques. This is where Machine Learning pitches in as the associated ML algorithms have the capability to adapt and learn according to the nature of threats. With the processed data being used as the underlining principle, Machine Learning is probably the only efficient piece of technology that can adapt adequately without coming up with a lot of false positives.
Which Algorithms are to be employed?
It goes without saying that ML algorithms are great a preventing frauds. However, if you are looking to learn blogging and Machine Learning is your preferred specialization, it is necessary delve deeper into the existing set of algorithms. 
Fraud Detection Algorithm
It is necessary to understand that machine learning concepts make use of automation— a tool that is capable of extracting unknown and known data patterns. Moreover, even if the data changes in form, ML fraud detection has the ability to identify the fetched patterns and make inferences, accordingly. The best thing about ML is that the algorithms are self-learning which automatically adapt for newer outcomes via feedback loops. 
Supervised and Unsupervised Models
Supervised ML models make use of labelled data for concluding the nature of requests. However, fraud detection in that case requires both the existing records of fraudulent and non-fraudulent data sets and this is how labelling takes place. However, unsupervised algorithms learn the basis of data structures themselves which makes them equipped when it comes to detecting unknown patterns. 
How to Implement a Robust ML Model?
In order to remain immune against frauds, it is necessary to create a robust ML model that uses a host of tools for the best possible outcomes. The first aspect has to be the Data stores which are necessary in regard to any AI-centric application. Leveraging data analytics and Big Data inputs is therefore necessary to create robust model which is capable of detecting different patterns of diverse complexities.
Profusion is yet another approach, that helps create efficient ML models. What stands out is the ability of different algorithms to assess different kind of threats which fares better as compared to a standalone unsupervised or supervised model.
Another approach which is necessary for creating a functional ML model is integration. There are quite a few organizations that blog about the prospective ML or even AI models but only a handful actually makes it to the market. This is where it is necessary to make room for portable integrations involving APIs. Models with data on Hadoop are only compatible on Hadoop whereas the real-time data streaming ones require select system embedding. 
While these are some of the techniques that help create a robust ML models, companies providing similar services can also opt for other techniques like on-going monitoring for keeping fraudsters at bay. Moreover, the last option is experimentation which actually involves a trial and error approach. This option urges AI and ML scientists to continuously test the ML models besides enhancing the credibility of the same on the basis of threats, existing data sets, and even integration requirements, if any. 
Fraud Handling and Prevention
Many organizations are currently strengthening their online identity in order to keep the fraudsters out of sight. However, the most convenient approach in this regard has to be the one involving ML algorithms. Due to the self –learning capability of this tool, it becomes easier to predict and thwart fraudulent activities while making room for seamless authentication measures. However, the benefits are best experienced by the entire banking sector where artificial intelligence has a massive role to play, in regard to transforming the security norms, mobile banking standards, risk management systems, and certainly cost reduction.
Inference
It is a known fact that fraudsters are constantly on the lookout to exploit the existing financial systems for unscrupulous profits. However, smaller breaches can have catastrophic consequences and this is where firms require intelligent systems which can learn, uncover, adapt according to the nature of threats. 
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