Democratizing Data Can Help Fraud Prevention Evolve

Fraud prevention’s most capable asset at present is analytics. By detecting patterns through data science, there’s a great chance that banks can find anomalies consistent with fraudulent transactions. However, the problem with data science is its complexity. Namely, the proportion of information that requires analysis often exceeds the number of scientists and analysts who can actually process the data. Data science itself is a difficult field for a person to develop experience, requiring extensive knowledge in mathematics, statistics and business. In order to address this problem, reducing the necessity of scientists becomes crucial. That’s why it’s crucial for businesses to democratize their fraud data through automation.

 

Crunching and visualizing the numbers

Data democratization is the process in which a company reduces the required activities of processing a given data set while increasing its accessibility to people. By cutting down on the amount of work that a person must do to handle or identify specific figures, there is an increased chance he or she can get results out of it without needing to crunch numbers that require extra skills. This lowers the acumen required to complete analysis on most anything, especially fraud.

 

The way this works is through automating specific analytical processes. Stephen Wolfram of Wolfram Alpha described this in an essay for Big Think as translating the data into a natural language that people can understand. It means identifying consistent patterns to determine a sort of vocabulary that people can understand.

 

In fraud prevention, this takes the form of payment information. Various bits of data, from the payee’s location to the items they purchase, come into play during the transaction process. A data scientist would have to pore through hundreds of these at any given moment and make judgement calls when a possible discrepancy appears. When data gets democratized, analysts will only get a heads up when the machine itself identifies a possible anomaly in payment patterns. This can save time and money in overhead, but more importantly, it can catch fraudulent activity before it completes.

 

Taking insights from the machine

Data democratization comes with a major shift in data science, according to KDnuggets. Businesses expect more from their data now, and data scientists making major insights no longer cut it. More importantly, machine learning is advancing the capabilities of automated analytics to a level where a computing platform can identify patterns and deviations on its own with enough training and insight. With this additional support, data on preventing fraud becomes more accessible to everyone.