Advancements in AI have taken the world by storm, with reports suggesting that the AI market size is expected to grow to $1,345.2 billion by 2030. The staggering rise in the industry surely has implications, especially in debt collection.
Source: Freepik
With the advent of AI in debt collection, companies worldwide have been integrating their machine-learning capabilities and automation strategies to help boost their Collection rapidly. Although AI has helped companies shift from legacy systems to modern-day technology, some still don’t realize the importance of AI and automation in debt collection. In the field of debt recovery, AI not only provides faster recovery rates but has enabled multiple departments to perform at higher potentials and provide better results, which include predictive AI models, automated communication, data-driven decision-making, RegTech, enhanced resource allocation, effective debt recovery solutions, and much more.Data-Driven Decision Making (DDDM)
Although companies and businesses used data-driven Decision-Making (DDDM) to streamline their collections before AI, the ongoing advancements of AI in debt collection have taken DDDM to the next level. DDDM refers to using data analytics, stats consumer behavior and much more to perform various actions and decisions to enhance companies’ debt recovery solutions. These insights can be used to optimize the strategies currently being used by the companies and analyze multiple metrics involved in the debt collection process that help maximize recoveries, further solidifying the role of AI in debt collections. Some of these metrics include:- Recovery Rate- The amount of debt accounts that have been collected compared to the ones that haven’t.
- Cost per Collection – The average cost linked with recovering a single debt.
- Delinquency Rate – The number of accounts receivable that are past due.
- Return On Investment – the profit accumulated after the debt has been recovered.
- Regulation Compliance Rate – The rate at which the entity complies with the regulations for debt collection.
RegTech in Debt Collection
Regulation compliance has been on the rise in recent years due to its importance and the consequences of violations. That being said, AI has brought along an innovative advancement in debt collection, referred to as RegTech, that can help companies boost their collections if adequately used. At its core, RegTech automates regulation compliance for users to help them adhere to the regulations set in place through AI in debt collection. RegTech uses automated compliance monitoring to enable businesses to report and document debt recovery procedures without violating regulations and laws. Such AI debt recovery solutions allow companies to keep track of their regulation compliance, boost their recoveries constantly, and help create a user-friendly debt recovery experience for customers by using AI in debt collection.
Source: Freepik
Predictive Debt Models
A few years back, predicting debt before it even happens seemed almost fantasy-like. With so many factors at play, it seemed virtually impossible for businesses to determine whether a debt may occur. Yet, AI in debt collection has bridged this gap through predictive debt models. Predictive debt models use data analytics and advanced automation to forecast multiple metrics based on user behavior, credit history, payment processing, and much more. AI in debt collection, in turn, has allowed companies to better optimize their debt recovery solutions and mitigate the risk of bad debt in response to the models generated. Such predictive debt models include:- Churn Prediction Models- These models evaluate the probability of customers terminating their association with a company without paying the unpaid invoices.
- Risk Scoring- Such models are used to score through debt collection automation users based on their previous debt history, payment behavior, credit assessment, and much more.
- Customer Segmentation Models- For these models, AI in debt collection goes through the user’s history and segments them into groups based on relatively similar behavior.
- Probability Of Payment Models- Debt collection automation has allowed companies to calculate the probability that customers will comply with debt recovery solutions and clear their unpaid invoices on time.
AI Chat Bots
In a standard debt collection process, companies communicate with customers through debt collection agents who use multi-channel communications to carry out the process. However, the benefits of conversational AI for debt collection have introduced a new wave of AI chatbots that are being adopted by businesses worldwide. With reports suggesting that 69% of customers prefer AI chatbots due to their availability 24/7, AI in debt collection has provided customers with personalized interactions, self-service options, along with proactive debt recovery solutions that not only eliminate waiting times but also allow companies to save resources and achieve better collection rates through their AI debt recovery solutions.Custom Tailored solutions
Over 60% of business owners state that AI will help foster better customer relations. AI’s ability in debt collection to provide custom-tailored solutions is a major driving force behind such positive relations.
Source: Freepik
The role of AI in debt collections is crucial as it can process vast amounts of data in short periods to segment and profile customers. Then, using personalization communication strategies, improving debt collection with AI can deliver the best possible solutions for users to help them clear their debts faster. By implementing proactive debt recovery solutions and custom-tailored solutions, customers are more incentivized to clear their debts early and form long-term relations with companies by using artificial intelligence for debt collection.
