Data and AI Learning Group

Artificial Intelligence in Debt Collection. Can AI make it smarter and easier?

By Lisa Garcia posted Wed July 22, 2020 04:51 AM

  

Debt collections procedures in contact hubs remain difficult and ineffective. Inappropriate control of channels of information, low optimization of methods and not having a combined vision of communications affect the process. Also, businesses perceive this as costs given the point that they can´t lower their default rates.

Businesses need to rely on strong and innovative technologies. Such as automation, prediction, superior analytics and AI. These will support to transform current company models with accurate and not interfering intelligent policies. Focused on client loyalty to forecast and lower risk management. All of the above checking their debts to increase to other phases.

Regarding the size of outstanding debt, even a small percentage change in debt collection products can majorly affect the profitability of banks. With the Big Data change, machine learning and AI can be leveraged to promote recovery, while also discussing some of the other difficulties that lenders face.

Remarkable difficulties faced in debt recovery

  • Balancing performance with client experience


Clients today have high assistance expectations from banks. Even apparently, small efforts such as responsiveness on social media and personalized interaction can make a big influence on how customers understand the lender’s brand. These may even manage their commitment to it.

Thus, the lender must be able to give a consistently pleasant experience to the client across their campaign. In fact, however, a poor group practice often ends up leaving the client unhappy or angry, irrespective of how comfortable their origination or support activity has been. This can have a important influence on the brand knowledge and undermine the properties put into customer purchase.

  • One size matches all contact methods


While the volume of outstanding debts differs, not all debtors have the same outline, behavior or motive factors. But, lenders and obtaining agents often serve a cookie-cutter approach in their conversation: a stern and matter-of-fact tone, strongly-worded messaging. This one-size-fits-all strategy can negatively affect recovery because it may scare or anger clients even further.

  • Violation of collection methods


One out of each four US clients contacted by debt collectors advised that they felt threatened. Nearly 38% of them were reached more than 5 times a week by collectors, often at inopportune times. And three out of four clients reported that despite requests to stop calling, collectors proceeded to do so.

With management also not always clear on the number and regularity of collection agency calls and how collectors may use voicemail, emails and texts, the customers are left at the receiving end of abuse from collection agencies; but the brunt of their disappointment and anger is borne by the lender’s name.

Changes in debt collection with Artificial Intelligence and Machine Learning

With digitization, lenders — both regular banks and financial services businesses — have entrance to a lot of valuable client information. This data can be examined with the help of AI and ML-powered mechanisms to derive insights that open new opportunities to the optimization of collections, improved recovery and profitability, and client satisfaction. AI and ML find application in four main sections in debt recovery.

Prediction of client default

The Artificial Intelligence tool examines hundreds of parameters from FICO score and income to history and borrower’s performance through the journey. Understanding who is likely to default will alert, and thus forearm, the lender. And enable them to formulate pre-emptive plans for recovery. Edgeverve’s AI-powered FinXEdge Collect answer, for example, evaluates internal activity information (credit score, income, loan details, etc), outside factors (job data, GDP, weather, micro and macro-economic events) and behavioral influencers ( demographics, voice data and call notes) to predict defaults and suggest proactive outreach policies for high-risk clients.

Segmentation of borrower risk

This supports direct gathering efforts towards the clients who are not only at risk of defaulting but also most likely to meet their debt. Much of debt collection purpose is still manual with follow-up calls, emails and other information of reach out done at an exclusive level. Artificial Intelligence tools help improve process efficiencies, productivity and agreement through intelligent segmentation and prioritization of accounts and prime-time support for reach out.

Personalized interaction

The purpose here is to bring back the personal element into the debt recovery method and thereby, increase customer reply. Certain contact channels work best for some kinds of clients. Machine learning tools can support:

  • deliver tailored pieces of connection to different customer sections at the right time
  • offer extra personal insights of the clients to help steer these discussions
  • recognize which medium works best for which client segment
  • recommend the right tone and attitude to adopt for the best response
  • help medical collection agency with real-time cognitive automation
  • share analytical insights on the reaction to certain

Tailored settlement offers

In most standard collection practices, the choice on what improvement or settlement terms to propose to a client was mostly left to the ability and experience of the debt collectors, with loose guidelines in place. Machine learning tools can bring a way to this insanity. They leverage data to distinguish different client profiles: those who are in a temporary state of difficulty but who can and are likely to settle debts later versus those who think on defaulting or have no intention to settle. These tools can also support the right settlement terms to offer to each of these client types.

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