Network Science July 26, 2021
With the onset of the COVID-19 pandemic, every sector has felt the presence of the online and digital shift. The debt collection sector is no different. In fact, with the lessening of physical on-counter transactions and collections, more focus has shifted to automation and self-reliance. One can even statistically argue that the pandemic has led to an increase in borrowers or a sharp change in positive repayment chances. Working with a ‘better safe than sorry’ policy, many agencies now employ early predictive measures to safeguard their own and their customer’s interests. We now see how this can happen.
Personalization refers to the adoption of tailored methods for any activity in debt collection. This form of customization is beneficial for increasing engagement and response and has seen a surge in recent times.
It makes the borrower feel valuable, rather than a defaulter who is simply being poked for repayment. The following are all encompassed by debt-collection personalization:
1. Content of communication
2. Medium of communication
3. Tone of Reminders
4. Analytical Insights
5. Loan Repayment Proposals
6. Customer Profiling and Response Effectiveness
7. Stress Management
8. Dynamic/Sudden changes to customer repayment chances
It is imperative to understand that Machine Learning and Artificial Intelligence models are based on predictive intelligence. In simple words, it is using previous records and known outcomes to predict future outcomes. In such a scenario, there are many avenues where AI/ML can help debt collection:
1. Predictive Intelligence for Defaulter Identification: Based on several factors like credit scores, previous history/past track record, amount of loan, and salary-wage tradeoff, ML models can effectively pinpoint people who are likely to repay the loan and the ones who are not. This can be followed by regular check-ins, follow-ups, and support.
2. Emotional Intelligence for Willingness to Pay: Just like humans can easily understand the intent of a person to repay the loan, even Natural Language Processing Models can do the same. They convert the conversations to text and analyze the intent of the borrower. This helps in setting up the next steps and finding ways to accelerate the process if need be.
3. Automotive Intelligence for Speed and Efficiency: While human-to-human calling has its advantages, there is very little that a group of individuals can achieve in a limited amount of time. This is where automated voice calls and IVR technology can exponentially increase the number of people we reach out in a limited span of time. Queries asked by borrowers can also be redirected in a similar way.
4. Mitigative Intelligence for Optimal Recovery and Customer Satisfaction: We often see that a loan defaulter ends up losing collateral or valuable property, which is not in the best interest of any party. Using AI, agencies can sit with the party/parties involved, and plan an optimal path for the borrower for repayment and mitigation. This leaves the customer and loan provider with more than what they would have ended up with otherwise.
In summary, the ideal debt-collection mechanism is calm, composed, emotionally aware, predictive, and speedy. It has to be customized to the needs of the borrower while also making him feel valued. The voice on the other end needs to be logical, intelligent, and not waived by emotions of the extreme. It needs to suggest what is best for the borrower while keeping the effectiveness of repayment as the primary aim. The transition to such an ideal debt collector may be slow, but it is momentous. The future will definitely see the continuation of the surge in the digitization of debt collection practices.