How big data and machine learning revolutionize the Fraud Detection & Prevention Market

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Published: 06th February 2017
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Internet has penetrated into every sector and vertical, making them vulnerable to organized and automated crimes. Especially the Banking and Financial sectors with increased use of Internet banking and online transactions, monitoring the security is becoming a challenge making it exposed to fraud. According to Consumer Sentinel Network, the credit card fraud amounted to $5.55 billion worldwide and is on the rise. This pressures the organizations to devise solutions to prevent fraud, at the same time to provide positive customer experience. Especially, the banking and financial industries, which are highly customer-centric, need to fight against fraud strategically and not disturb the user banking experience.

To develop a more accurate and less interfering fraud detection system, organizations are investing to design flawless data analytics technology and algorithms to detect and combat fraud. The key is to gain a holistic view of customers with the help of Big Data. The magnificence of big data is that it presents a new realm of possibilities for organizations to run differently. By collecting huge chunks of data from mobile devices to social networking sites that are being generated every minute, normal activity can be distinguished from fraudulent activity, which increases the accuracy of fraud detection systems.

A powerful and evolving development in fraud detection system that uses big data is machine learning. In brief, machine learning is configuring agile systems to learn from one another to uncover the patterns hidden in data and making it to learn itself to deliver better insights. It helps to identify fraud in real time and to quickly detect the frauds in future. These machine-learning algorithms along with text mining are used to monitor financial transactions for fraud, in order to reduce the number of false positives thrown by current surveillance systems. This makes fraud detection more accurate and relevant and reduces the costs of manual inspection.

But there are few things to consider before using big data to develop fraud detection systems. The organizations need to make sure that they are dealing with high quality data for proper data analysis. Another challenge is they need to realize the boundaries for using customer data and respect their privacy. Also, it is disruptive for an organization to implement big data systems. They need to make sure there is a proper collaboration between IT and business units where the teams agree upon clear goals by adopting destination-driven thinking.

As technology advances, new strategies for optimizing fraud detection are being developed. Financial organizations should inform their team members about latest trends and developments. They need to partner with experts to stay ahead of technology curve.
By using huge amounts of data from various sources like social media, customer databases, point of sale and external sources from data vendors, big data along with machine learning allows organizations to greatly enhance the quickness of fraud detection. The analytical data from these sources is forming a new collective fraud database, which can be used to develop better models for fraud detection.

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