Artificial Intelligence can make Consumer and Commercial debt collection more efficient, maximize ROI and improve customer experiences.
The debt-collections industry is not particularly immune to the all-pervasive disruptions that are happening across businesses ever since the onset of the current situation and this is much understandable.
Here are some of the thought processes, as we at CreditNiravana envisage, on how the debt collection industry is now panning out in the wake of this unprecedent global recession.
Lending has always been risky business, laden with delinquencies, defaulting and inefficiencies that come with it. But it is heading into even more choppy waters… Global consumer debt is estimated to be a whopping $ 120 trillion and the collection success rate in many geographies is alarmingly low. In the US alone, $900 billions of household-debt is considered delinquent.
Considering the sheer size of the outstanding debt, even a small percentage in improvement of the debt collection numbers can majorly impact the overall profitability of the lenders. With the onset of the big data revolution, machine learning and Artificial Intelligence is being used to improve recovery while also addressing most of the other challenges being faced by the Lenders.
Most Lenders reach out to customers only when they become delinquent. Without understanding the behavioural economics of each borrower, reaching out only when she/he becomesa defaulter will not help achieve positive results.
The conventional belief in the lending industry is that, once you ascertain the risk level of a customer during the underwriting stage, you have addressed the risk mitigation with respect to that customer. Most customers’ payment behavioural economics is dynamic, and unless you have a robust, scientific system to monitor the payment risk behaviour of the customers dynamically, your risk mitigation process is reactive and will not yield results.
Often, collection analytics departments rely on linear demographic segmentation for profiling the borrowers, and for prioritizing the collection process.
Relying on the borrower’s responsesverbatim, for debt collection results, or taking subsequent follow-up actions often won’t yield positive results.
Understandably, one’s response when under pressure is subjective and the ‘true intent’is often not reflected in the responses.
For this, you need to have a dynamic and robust NLP and NLG system to ascertain the real patterns and anomalies in the payment behavioral responses of the borrower, so as to ascertain the correct ‘intent’ of the borrower.
Lenders need to have a dynamic, real time Ai platform to ascertain contextual payment behavioral economics of the borrowers, for accurate and precise prioritized debt collection process.
While the size of outstanding debt varies, not all defaulters have the same profile, behaviour or motivation factors. Lenders & Collections Agencies however often follow a ‘cookie cutter’ approach in their communications: Stern, firmly worded messaging that has a matter-of-fact written all over them. Such ‘one size fits all’ approach can impact recoveries negatively since it may go on to intimidate or ever irritate the customers even more.
A recent CFPB survey suggested that out of every four American customer contacted by the debt collectors, at least one reported to have felt threatened.
Nearly 40% of them were contacted more than 4 times a week by collectors, often at inconvenient times, and out of four customers, at least three reported that in spite of repeated requests to stop calling them, the collectors continued to call with renewed vigour.
Customers today have high expectations when it comes to service from Lenders. Even efforts that are seemingly small, such as responsiveness on the social media and personalised communications do make a huge impact on how customers perceive their lender’s brand. It may even alter their loyalty towards the brand.
Thus, it is critical that the Lender be able to offer consistently delightful experiences to their customers across their transactional journey.
In reality, however, a poor collection experience often ends up leaving the customer unhappy or angry, irrespective of how pleasant their initial interactions or previous support experience has been. This could lead to a major impact on the brand’s perception and undermine all the investment made to acquire the customer.
With the advancement in Data science and Data engineering, a variety of large volume data,which are pertinent to debt collection process,can be analysed with the help of Ai and ML-powered tools to derive insights that open new doors to the optimization of collections, increased collection and profitability, as well as customer satisfaction.
Ai and ML find applications are of great importance in four main areas in debt collection.
The Ai platform dynamically analyses hundreds of parameters of structured and unstructured data, and ascertain well in advance as towho is likely to default, and thus forewarn the Lender. This shall allow Lenders to formulate pre-emptive strategies for recovery. CreditNirvana.ai, for instance, evaluates internal transaction data (loan details, income, location, payments etc), external factors (weather, job data, GDP, micro and macro-economic events) and behavioral influencers (voice data, video data and call notes) to predict delinquencies, and recommend personalized debt collection actions pre-emptively.
Accurate assessment of borrower’s ability and willingness to pay contextually and dynamically, is the key for prioritization and personalization of collection process. A whole lot of the debt collection efforts are still manual processes which include follow-up telecalls, emails and online forms executed at the individual level.
AI tools like CreditNirvana.ai help improve process efficiencies, productivity and compliance through intelligent behavioural segmentation and prioritization of accounts, along with personalized recommendations for reach-out.
The target now is to combibe a human element into the process of debt collections and thereby improve the responses of the customers. It has been noted that certain communications work best only for a certain type of customers.
Advanced NLP-NLG process and Ai conversational engine in CreditNirvana helps to:
Using the advanced Ai/ML process and digital tools, reaching out to the customers pre-emptively and personally through Ai conversational methods facilitates continuous customer engagement. This in turn results in enhanced positive response of the customers with respect to their payment behaviour.
Since the conversational process can be mutual(example WhatsApp, WeChat, Messenger etc.) and can happen at the customers’ preferred time, the overall impacts will be much more positive than an interaction at a typical call-centre.
The decision on what recovery or settlement terms to propose to a customer was left mostly to instinct and experience of the debt collectors, owing tonon-specific and linear guidelines in place.
Advanced ML/Ai techniques can leverage data to identify the contextual customer payment dynamics (both willingness and ability to pay) of the customer dynamically,on real-time basis, and can recommend the right settlement terms to propose to each of these customers.
If you are a Lender facing challenges in debt collection processand are wanting to leverage the power of varieties of internal and external data, so asto improve debt collection and recovery, as well as enhance customer experience, Creditnirvana.ai may be just right for you.
Find out more and request a meeting here https://www.creditnirvana.ai
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