This is a guest post from Yaakov Smith.
Big data is changing the way the financial world handles client interaction. No matter what sector data analytics are employed in (IT, marketing, sales etc.), its implications are leading to a new wave of Business Intelligence (BI).
Any company that uses analytics on a daily basis will understand the ability of big data to transform customer relations and optimize management processes. In particular, big data solutions are enabling debt management software to make autonomous decisions on client handling procedures.
Scaling the Severity of Debt Management Violations
Until now, many debt collectors have relied on a one-size-fits-all method of debt management. Most Individual Voluntary Agreements (IVAs) and Debt Management Plans (DMPs) are managed using the same parameters, regardless of the severity of each case. In this habitual chain of events, clients who fall slightly behind on payments face the same sanctions as those who regularly avoid them. The lack of gradation often leads to increased hostility between collector and debtor, resulting in further infringements.
With the correct implementation of big data, debt management companies can create bespoke strategies for individual clients. Debt management software is reaching a level where it can successfully determine the right cause of action on a case-by-case basis, through automated analytics. In a broader sense, this has turned debt management software into a Customer Relationship Management (CRM) program.
For banks and debt collectors, these advancements in data usage have enabled them to create bespoke business solutions for everyone on their books. Traditionally, debt management has been viewed as a callous business. But, those in the field have a vital role to play in the overall health of a country’s economy. A balance needs to be struck between meticulous efficiency and customer satisfaction and, so far, debt management automation has proven to be the most effective solution.
Leveraging Data to Secure Customer Loyalty
Any debt management strategy must work two-fold. Firstly, it must ensure efficient collection of overdue payments. Secondly, it needs to meet the customer’s own expectations. A debt management solution that keeps both parties happy will ultimately reduce friction and delays. Not only does data analysis optimize your company’s workflow; it also reduces the percentage of overall credit loss.
In the majority of cases, both the debt management company and their clients benefit from a more customized approach. The increase in transparency makes it easier for employees to meet business objectives and for debtors to understand the requirements of their debt management plan. Turnaround time and cost also fall dramatically, as debt management software automates the processes it would normally take a small team days to complete.
By providing tangible benefits for everyone involved, big data has markedly improved an area consistently plagued by inconsistency. Analytics have not only accelerated the debt collection process, but enabled management teams to tailor it to the specific needs of their customers. In the past, their expectations have not been taken into account, often lumped into one, uniform category.
With the introduction of BI into debt management software, we could finally be on the brink of a holistic debt management solution. In this event, the relationship between collector and debtor no longer becomes strained and both can operate from a mutually advantageous position. Soon, big data will be incorporated into all management systems and customer relations will cease to be a solely subjective field.
Author Bio: Yaakov Smith has twenty years of experience with developing, configuring and writing software. He is the owner and founding manager of bespoke business solutions company Logican Solutions Ltd, which offers its clients complete productivity software solutions for debt, property portfolio and claims management.
While working on forecasting (understand “time series analysis”) I found several interesting and state of the art articles from Rob J. Hyndman. He is the co-author, with George Athanasopoulos of Forecasting: Principles and Practice. This is an excellent concise and comprehensive text explaining concepts behind forecasting, common algorithms and how to implement them in R (for a business… Continue reading... | 2 Comments
“You don’t need to predict the future. Just choose a future — a good future, a useful future — and make the kind of prediction that will alter human emotions and reactions in such a way that the future you predicted will be brought about. Better to make a good future than predict a bad one.”
Isaac Asimov, Prelude to Foundation
This is a guest post from Ethan Millar.
Big data is the latest competitive advantage for businesses. Data are now woven into every industry and function across the global economy. The use of Big data will become the basis of competition and growth for businesses by enhancing the productivity and creating significant value for global economy with waste reduction and increased quality of products and services.
This infographic is proposed by Sandipan Pal.
Finally, the day has arrived.
After years of teaming up for group studies, setting goals and planning to fare better than your competition, you have achieved it.
Topped your grades and now waiting to make a mark in your field.
But, how do you make a mark?
Easier said than done, isn’t it?
Today’s guest post is written by Mohammad Farooq
If there’s one thing that businesses across all industries have in common today, it’s in their increased adoption of data to shape business decisions. Below is a demonstration of how key industries use analytics tools and the benefits these tools have in solving challenges of data capture and use to shape growth.
The Data Science Handbook gathers 25 interviews of Data Scientists. Interviews are well done, most questions depending on the previous answer. This gives a nice feeling of discussion between the interviewer and the Data Scientist. On the content side, it provides interesting insights about the job of Data Scientist. The book is however biased towards pioneers in the field spending 14h… Continue reading... | 10 Comments