Scope of Big Data in Payday Loan Industry

It is not astonishing to know that the capability of Big Data is being explored by the credit industry. The market of the insecure loan industry is rapidly expanding, some of the firms are wishful that the households will wind up with more access to credit. At the same time, the lenders will be able to build their portfolios by reaching deeper down the credit spectrum. In our previously published article, we have discussed the basics of payday loan and the position in the advertisement industry. There are several publications on the internet discussing the digital transformation of payday loan industry.

How the Internet and Big Data Industry Are Penetrating

Most of the financial stores in Ottawa, as an example city, do not seem to be physically close enough to residents, and that could be the reason why people who would want to seek online service. Payday loans Ottawa, ON is readily available via mobile applications. So the need to walk to the money lending store and wait in line is decreasing. According to the 2011 census, 883,391 people lived in the city of Ottawa, and the population of Ottawa district has about 1,142,700 inhabitants (2004 data). This agglomeration is Canada’s fourth-largest city. Although many people are employed and highly educated, Ottawa citizens can sometimes find themselves in a cash shortage, often happening all of a sudden. Within this feature of getting those quick finance injections in Ottawa, in most cases, there is an option called instant borrowing. This is a significant benefit for all of the Ottawa residents, because there is no need to wait in line, and everything is quickly done on the internet.

With this background, the penetration of data sciences becoming as easy as with the other industries. The digitized marketplaces of payday loan and credit card companies have a bird’s eye view of what the consumers are doing. Many of the companies are becoming the big adopters of big data technology, such as Hadoop and machine learning algorithms. The adaption is giving the computational heft required for data mining at a massive scale.
Initially these companies selected the Hadoop platform with MapR Technologies to store data and run big data processing. The industry is getting quick analysis to prevent fraud, thus helping the customer acquisition, and recommendation. Detecting fraudulent transactions is arguably the biggest use case for big data in any financial service. Big data tools are helping to find out their loyal customers among the 883,391 peoples living in the city of Ottawa and possibly predict the nature of future customers.

The Big Data Approach to Assess the Possibility of Paying Back

The striking feature of online credit is the high-income group customers who are seeking loans with a different intention – to keep the saved money aside for their expenditures. Payday loan and the other forms of borrowing no longer limited to the poorer segment. With the increasing number of gizmos and life-style products, the majority of the citizens are unable to meet their purchasing wishes with a standard earning. The shift in the paradigm also shifting the paradigm of the finance industry.

Benefits for the lenders : It goes without saying that the lending companies are the biggest beneficiaries of using big data. It this had not been the case, then they would not have invested in big data. Big data tools allowing them to learn the patterns which were previously invisible. For instance, big data analysis has shown that different cities have different spending patterns. So, our example of the city of Ottawa will have difference with New York or London. The tools also have also identified different personas which can help them to increase the number of positive outcomes. As pointed above, big data analytic methods are decreasing the amount of money that is lost due to fraud.

Benefits for the customers : When it comes to customers, they also benefit from the convergence of the lending industry and big data. They are also benefiting from the increased security provided by big data, and free of cost. Their lives become safer and more comfortable. No lender wants to invest behind who is likely to fail in paying back.

Conclusion

It is a fact that lending is always risky and the future unlikely to be predicted. However, the lending industry runs to meet the need of the customers. Most of the lenders will have their interest rates spelled out transparently because the law tells them to do it, so no hidden fees are even remotely possible. The governments have a different approach to make payday loans safe to consumers. While the technology industry is approaching in a different way which is logically valid. We can see a convergence of big data and the payday loan industry as just a better way for credit to take advantage of unsuspecting consumers.