Optimizing Debt Recovery in Consumer Lending: Strategies for Tracking and Managing Promise to Pay
Debt recovery in consumer lending is a complex process that involves tracking and managing the Promise to Pay made by customers who have become overdue on their payments. This is especially relevant in the initial stages of debt collection when the primary goal is to secure a commitment from the customer to repay their debts.
Tracking and Managing Promise to Pay
The Importance of Protocols and Structure
Promise to Pay is a key metric in debt recovery and is particularly important in the initial stages of collections. To ensure the most effective results, it is essential to establish clear protocols for how Promise to Pay is tracked and for how calls with customers are structured. Junior collections agents can handle the initial collections calls, while more experienced agents should be responsible for handling cases where the Promise to Pay has been broken by the customer.
Collection Call Scripts
Finding the Right Balance Between Consistency and Flexibility
Collection call scripts can be a valuable tool for collections agents, providing a structured approach to communicating with customers and securing a Promise to Pay. When developing call scripts, it is important to find the right balance between consistency and flexibility, allowing agents to tailor their message to the specific circumstances of each
The Risks of Freestyling:
Why Deviating from Call Scripts Can be Detrimental
However, freestyling or deviating from established call scripts can be detrimental to the collections process. It can lead to inconsistency, mistakes, and difficulty in evaluating agent performance and identifying areas for improvement. To ensure the most effective and consistent results, it is important for collections teams to use well-crafted call scripts and to train agents on how to use them effectively. This can help to optimize the collections process and improve the chances of securing a Promise to Pay from overdue customers.
Dealing with Broken Promise to Pay
Options for Restructuring Loans and Splitting Payments
When a customer breaks their Promise to Pay, the collections process becomes more complicated. One potential solution is to restructure the loan, although this can create accounting issues if the loan must be provisioned at 100% in certain jurisdictions. An alternative approach is to suggest that the customer split their payment into smaller, weekly installments within the original repayment schedule. This can help to improve the overall performance of the loan from a portfolio management perspective, and may also improve the customer's perception of the lender as being more understanding and supportive.
Optimizing the Promise to Pay Process
Using the Champion-Challenger Approach
One effective strategy for optimizing the Promise to Pay process and improving segmentation is to use the champion-challenger approach. This involves testing different variations of a process or system, such as different call scripts or communication channels, and comparing their performance to determine which is the most effective.
Data Collection and Analysis
Improving Segmentation and Decision-Making
For example, in the context of debt recovery in consumer lending, a collections team may try using different call scripts with a sample group of customers to see which one results in the highest rate of Promise to Pay. This data can then be used to inform the team's decision on which script to use with all customers moving forward. Similarly, the team may test different communication channels, such as email, phone, or text message, to determine which is most effective in securing a Promise to Pay from overdue customers.
Optimizing Decision-Making with a Rule Engine
Using Behavioral Data to Identify Patterns and Suggest the Best Course of Action
Using the champion-challenger approach allows the team to continuously optimize their process and improve segmentation by identifying the most effective strategies and tactics for different types of customers. It also helps to ensure that the team is making data-driven decisions rather than relying on intuition or assumptions.
Maximizing the Effectiveness of Debt Recovery in Consumer Lending
Effective tracking and management of Promise to Pay also requires thorough documentation and data collection, including information about which call scripts were used, the terms agreed upon, and whether promises were kept. This data can be used to improve segmentation, evaluate performance, and train behavioral models that can help to optimize decision-making in the future. A rule engine can also be used to help optimize the handling of customers who have broken their Promise to Pay by identifying patterns in their behavior and suggesting the most appropriate course of action.