How Real-Time Analytics Tackles Fraud Detection

Frauds are a major part of the huge amount of losses to the financial institutions that occur every year. We often see the news of the frauds that do away with the public money that is deposited in the banks. This leads to the people’s mistrust in the banking system as a whole. People being to question whether their hard-earned money is safe in the bank after all.?

With the improvements in technology and the significant increase in the amount of data, frauds are also getting more complicated and harder to detect. This problem is now being solved to a huge extent by harnessing the power of artificial Intelligence and machine learning which help in improving the real time analysis and detection of fraud. This is made possible due to the expansion in the amount of data that is being generated every day.

Uses:

Several notable banks have already begun to implement real time analytics for their safety and the security of their customers. This is done with the help of machine learning which helps for risk management, detection of frauds and to ensure compliance. This is a win-win situation for both the organisations and its customers as the customers get assurance of their security and at the same time this implementation distinguishes the organisation from their competitors, this shows that they are serious about the security of their customers. Artificial intelligence and Machine Learning can be of great help for fraud detection. ML works by learning from the existing cases of frauds that have occurred, the insights then obtained can be used to take steps that ensure that the same or similar frauds don’t repeat in the future. This also helps in real time by identifying which transactions have chances of being fraudulent and can prevent them from happening in the first place.

Fraud Detection:

As we have discussed above, the data that we generate is increasing exponentially. This can be a boon or a bane. A potential fraudster can make use of this large amount of data to hide themselves i.e. to disappear on completing of the fraudulent activities. This is largely possible due to the absence of real time fraud detection. This is where AI and ML comes into picture. They can be designed to learn on their own and to do real time detection of the frauds or to predict the chances of occurrences of these incidents and thus take necessary action.

In-Memory computing:

Machine learning makes possible the real time fraud detection by making use of in-memory computing. This means that the that data is stored in the RAM of the dedicated servers as compared to the traditional methods of storing it in the comparatively slower disk drives. This improves the efficiency of machine learning by reducing the data read and write overheads and reduced latencies. The machine learning system ingests the attributes of the transactions and then matches these to the pre-existing patterns by using qualities to a specific user. This helps in being able to do the real time analysis of the transactions.

Three hundred trillion – that is the approx. amount that has been lost in 2017 to fraudulent transactions. That is a very huge amount and it correctly shows the significance of the matter of fraud detection and prevention. A study had found that companies were losing 7% of their annual expenditure to fraud. Thus, it is very much important for a financial institution to implement fraud detection techniques based on real time analytics to help the institution become more productive, ensure the satisfaction of your customers and to make sure that you stand out from your competition.

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BI Consultant

DataFactZ is a professional services company that provides consulting and implementation expertise to solve the complex data issues facing many organizations in the modern business environment. As a highly specialized system and data integration company, we are uniquely focused on solving complex data issues in the data warehousing and business intelligence markets.

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