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How AI is Modernizing the Collections Process

Debt levels are rising and borrowers are increasingly unable to pay off the debts. The Covid-19 pandemic has not helped and there is a high risk of delinquency when it comes to types of credit, from business loans to mortgages. Traditional strategies are no longer enough to collect debts and improve receivables.


During the last decade or so, AI and machine learning are disrupting debt collection. Companies are using advanced analytics, machine learning and behavioral science to fully automate their debt collection strategies. According to stats, the share of AI in FinTech alone is expected to reach about $35.4 billion in value by 2025.


Historical debt collection


Historically, debt collection has been reactive. Lenders try to recoup their losses after a borrower becomes delinquent. The risk models that are in use now don’t allow for early delinquency warnings as they are based on a limited set of data. They do not rely on numerical logic to develop a solution.


According to essay experts for an online assignment help, one of the main blockers to improving the efficiency of collections is using obsolete processes. Methods used for the collection are often intrusive and create a negative impact. Even though using emails and SMSs instead of phone calls to collect debts may be more aligned to reaching debtors, there is still a need to customize the process.


Debt collection has to go beyond asking customers to repay overdue installments and suggest a way out of the crisis. This is where AI and machine learning can come into play.


Timely warning for delinquency


AI and ML technologies can analyze a great quantity of data from many different sources. It is possible to process call time, the value of certain accounts, collection rates, call effectiveness and much more.


Machine learning is now enabling the lenders to easily identify at-risk borrowers before they reach a point where they are unable to make payments. Machine learning accuracy constantly improves through retaining as new information comes to light and reveals new insights about delinquency risks.


Machine learning can recognize patterns that provide financial institutions a robust way of evaluating risks. This goes beyond the usual credit scores and other rough indicators. It can collate new data and update the metrics in real-time when conditions change, such as during a pandemic. This is not possible when using the risk analysis based on traditional methods.


Focus on at-risk clients


With a timely warning system of delinquency, financial institutions are able to work on the clients who might fall behind on payments. A timely indication and accurate analysis enables them to prevent their accounts from becoming delinquent. With timely analysis of issues, the debt collection department can adjust their method of collection as per what data puts forward.


For instance, they can single out potential defaulters who take time to respond the messages and also use predictive modeling to decide about the next course of action. For example, they may decide to offer various payment related offers or some type of rebate to the potential defaulter to settle an account before it goes into the collection cycle. There’s a big chance of settlement as it encourages a borrower to make a move.


Create nuanced borrower profiles


Traditional risk models assign borrowers into categories based on broad market sectors but AI and machine learning are allowing the creation of better borrower profiles. They make it possible to highlight nuances within a particular economic sector. For example, in the current pandemic, certain businesses, such as retail stores and restaurants, find online shopping or delivery or take-away as more viable than others.


Economic restrictions and different locations where the virus is more severe also cause different effects in various industries and their related sectors. Considering these and numerous several other key factors, it is possible to understand more about borrowers.


Through AI and ML, financial institutions are able to build more detailed customer profiles. They are able to recognize the borrowers who are more likely to approach the issue positively and try to settle the loans and which borrowers need an extended effort, such as modifying their payment terms or restructuring their loans.


With so much corporate and household debt, even small improvements in categorizing borrowers can generate decent returns. As AI keeps updating its algorithms and customer profiles move towards being more nuanced, lenders become better able to evaluate borrowers based on targeted or pre-laid-out characteristics instead of categorizing them on a broad traditional analysis.


Natural language processing (NLP) is a new technology that means lenders can ask a question using normal language and get a response they understand. One of NLP's uses in businesses is to allow lenders to refine their methods of categorizing borrowers. They can even determine what language to use when they communicate with specific account subsets.


Optimize strategies for better customer engagement


Direct calls on phones or structured emails are the old methods that lenders used to settle the loan issues with the customers. Lenders today can use an automated, omnichannel communication process. They can email, send text messages, use social media or mobile apps. There are many ways for lenders to reach out but they need to know the right method to put into action, when to get in touch with them and the type of approach that would help to resolve the issue more effectively.


The best debt collection software leveraging the power of AI and machine learning can recognize and present the best channel by which to reach the borrower and the best time of the day for communications to be sent. This increases the likelihood of a response and improves collection rates. AI can even analyze borrower call audio to give actionable insights on the way various scripts impact borrower responses and enable lenders to come up with increasingly nuanced, detailed scripts.


Debt collection strategies can now be structured on the social, demographic and economic data that corresponds to each debt account. By taking into considertation the age, profession, salary and social profiling of a customer, lenders can establish and the chances of them paying their debt and also use this data to tweak their approach to them.


AI debt collection software is able to create voices like humans and create a more personalized experience for debtors. Settling debts today is becoming easier and less painful than a collection process consisting of multiple phone calls at inappropriate times of the day.


AI is eradicating the need for guesswork and human biases in the collection process. It is automating the process in a logical way and at the same time enabling the development of a more customer-centric approach.


The bottom line


AI offers multiple opportunities to help drive improvements in collections with a more informed and customized approach. Of the firms already using AI, 40 percent are using it for collections. Lenders and borrowers alike are seeing the benefits of AI and machine learning in modernizing the debt collection process.


More ability to interact with and understand borrowers helps to reduce losses. Early warning of delinquency means lenders can effectively focus on at-risk clients. More proactive customer outreach is helping borrowers to better manage their debt to avoid facing a financial trouble and avoid debt collection. Credit and collection organizations that embrace AI and machine learning will reap many benefits when they modernize their collection processes.

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Comment by Kevin Griffin on June 6, 2021 at 8:24am

Thanks for the interesting article. I would like to add that debt collection in the current conditions is becoming an ineffective and costly process. A lawyer in an organization works with a large volume of documents; he can process no more than 120 applications per month. Moreover, he must act promptly: the limitation period for debts is only three years. But for some of the debtors it is necessary to draw up documents anew for collection through the court. After the trial, it is also required to prepare documents for enforcement proceedings or appeal. In addition, after a while, some legal entities and individuals again have debts, and in these cases all documents have to be re-examined. Artificial intelligence automates the entire cycle of work with a debtor - from automatic robotic dialing, calculating penalties, state fees, determining jurisdiction, court details and forming an electronic document flow for claims and claims to sending documents to the court online using an electronic digital signature (EDS), forming a judicial decisions for a magistrate and a writ of execution, a request to the Federal Tax Service and banks for bailiffs. I gathered this information from posts on Facebook, in which the authors talk about the automation of various workflows. You can find quite a lot of such posts there and I noticed that in most cases they had almost 40 thousand likes! I am sure that in order to achieve such indicators, their owners repeatedly buy facebook likes in order to quickly increase their number.

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