The significance of Data in the Debt Recovery Process

Debt Collection Industry is one of the fastest-growing industries in recent times as it branches out to several verticals like auto finance, FinTech, Healthcare, etc. As much as it has earned its share of ire from the masses, it has equally proven to be reliable and sustainable for every stratum of society for their different needs in the market, big or small, conventional or basic. What makes it grow rapidly is the sheer concept of supply and demand.

With the consumer debt increasing every year, this industry goes through a paradigm shift in business which is a smart integration of technology with the least amount of human intervention. The Automation of collection operations is the latest onset this industry is witnessing and it is here to stay or a considerable amount of time. By 2028, it is expected to have a decline of around 8% in jobs owing to the automation involved. Fintech or Auto loan companies have already begun their groundwork and the commencement to which is the collection of relevant data. While you sip your coffee, the industry is thriving on your cookies quite literally.

With the automation taking over the legacy system of debt recovery, the parallel work of data collection and mining is in full swing. With experts, time and again emphasizing the importance of collecting data, very less people know the kind of wisdom and value it will bring to the industry and boost customer-centric attitude. Artificial Intelligence and Machine Learning fuel upon data for predicting consumer behavior and forecasting payments.

Below are the key facts that make data collection an inevitable task in the debt collection process:

  • By training the algorithms to mine the data collected, analytics can predict which customer will most likely resume paying after missing a couple of installments, and who will probably default.  
  • The marketing cost of a debt collection agency and revenue spent in contacting the right consumer is reduced significantly due to the historical data collected for targeting in the future.
  • Recognition and cause of delinquency in accounts or risk profiling is easier and more doable with regressive data mining collected over a considerable time frame. Acting proactively on such accounts helps in faster and timely debt recovery. 
  • Segmenting accounts on the basis of past defaulters, lazy payers, and fraud detection is much accurate with the analytical data produced from historic and conventional data prediction.
  • With data insights, iterating the customer of their portfolio, the next payment due and date along with a feasible mode becomes an easy job, thus making consumers aware and delivering a better experience all throughout their debt journey. It helps them realize the impact their payments make on their future investments.
  • Cost-cutting is an exponential part of data collection practice. Focusing on ‘easy targets’ than wasting cost and resources on difficult portfolios makes the collection process easy.

This is the time to start building up on your ancient and new data to fuel up your machine learning mechanisms and build on happy clients worldwide with a profitable debt recovery pathway.