Big data analytics can assist credit risk managers, debt collectors

Perhaps the most important practice in making sound business decisions in the contemporary economy is the cultivation and analysis of data. The term "big data" was coined in recent years, and is meant to serve as a reflection of the massive compilation of industry information that has become readily available - and consistently archived - by organizations looking to improve their chances of success.

Big data can provide valuable insights for a plethora of different verticals, including debt collection and credit risk management. Organizations within these particular industries can position themselves to achieve sustainable growth by applying the use of data analytics.

Credit risk managers can better assess potential borrowers with big data
Industry expert Sebastien Clouet wrote an article for Bobsguide in which he suggested that the utilization of big data and analytics can inform important business decisions for credit risk managers. By delivering visibility and increased levels of understanding regarding loan applicants, he said it can become easier to complete risk assessments and evaluate the potential revenue returns associated with each borrower.

Clouet noted that advanced data can offer more detailed reports on payment backgrounds than were previously available to credit risk managers, opening up new doors through which successful decision-making can be facilitated. Combining data with real-time analysis is a powerful move for industry members, shedding light on constantly updated variables, according to the author.

Big data can offer unprecedented financial strength within the risk management market, Clouet said, and can lead to more accurate measurement of prospective borrowers.

Simplifying debt collection through data analytics
Forbes contributor Tom Groenfeldt reported on an unnamed lending organization's success in employing a data analytics firm, Mu Sigma, to better evaluate and predict company trends. He offered an explanation regarding big data's current prominence - it often shows that traditional collection processes are not always the most cost-effective, a revelation that sometimes leads to significant cuts in expenses.

Groenfeldt remarked that Mu Sigma consulted with its client to develop a probability model that measures the likelihood of collection based on payment histories. The model also provided estimation of the potential impact of every action within the organization, which served to provide a foundation for maximizing success.

The author interviewed Mu Sigma's head of values and strategy, Tom Pohlman​, who admitted that the biggest problem the consultancy firm faces is inconsistent data. Continuously provided and accurate data is paramount to achieving analytical success, but Pohlman suggested that information is so often replicated and repurposed that it can sometimes be invalid. The importance of accuracy in big data analytics cannot be understated, which is why Mu Sigma - and presumably other data consultants - has worked to develop new algorithms and procedures on a regular basis.

Big data has arrived as an imperative analytical tool in the business world, and it is vital that companies of all sectors use it to determine the effectiveness of their various products, services, marketing campaigns and other expenses. Debt collectors and credit risk managers can use carefully analyzed data to make strong, informed decisions and position their organizations for success.