Why segmented pre-collection activity is important for lending businesses.
What is pre-collection
Pre-collection is the proactive and strategic approach to managing unpaid debt that begins before the account becomes delinquent. The goal of pre-collection is to avoid having accounts enter the collections process by reminding the borrower, or by working with the borrower to establish a payment plan or other arrangement before the account becomes delinquent.
By contacting accounts before they become delinquent, the lending business can improve cash flow and reduce costs connected to the collections process.
Why segmented pre-collection activity is important
There are a number of reasons why the segmented pre-collection activity is important for lending businesses. By segmenting accounts into manageable chunks, businesses can more effectively target their collection efforts.
A lender can use segmentation to prioritize accounts that are more likely to become delinquent. This allows the lender to focus its resources on accounts that are at a higher risk of not being repaid, which can improve recovery rates.
Segmentation can help businesses to tailor their collection efforts to the specific needs of each borrower, which can improve the chances of successful collections.
Most importantly, not all customers need to be reminded. Contacting all customers can often annoy the ones that were going to pay on time anyway. Segmenting can help you to identify those that need to be contacted, saving time and money in the process.
Ways to do pre-collection
There are many ways to do pre-collection, but some of the most common methods include:
- sending emails
- sending instant messages (WhatsApp, Facebook Messenger, Telegram) or SMSs
- making automated phone calls using IVR or voice bots
There are alternatively other more costly and old-school ways:
- Making phone calls
- Visiting in person
- Working with outside agencies
- Hiring a debt collection law firm
How to do pre-collection segmentation
There are a number of ways to segment accounts for pre-collection. The most common method is to segment by account balance, with higher-risk accounts being those with higher balances.
Other ways to segment accounts include payment history, credit score, demographic information, or by type of debt.
Once accounts have been segmented, the next step is to decide how to contact each group.
How to do it technically?
The technical implementation can be done by daily extracting data from your core system. The data useful for segmentation can be :
- maximum days past due
- average days past due
- remaining balance
- monthly repayment
- historical contact types and which lead to success
Predictive scoring
Potentially, a company can build a machine learning model with the target of the probability of forgetting repayment. The dependent variable can be a binary flag “did the customer repay on time?”. The target variable should focus on short-term repayment probability, so you need to consider only a small number of days, e.g. 10. This dataset can be used to train a machine learning model that will predict the probability of timely repayment.
After the model is trained, it can be used on a daily basis to score all the accounts and select a subset based on both segmentation logic and a score of timely repayment.
Making it all work
The process of calculation can either be done as an ETL in a data warehouse or as a batch processing task using a decision engine.
A decision engine will make it possible to select the best contact method for each customer, based on the customer’s characteristics and the model output.
