Applying automation to operationalize debt collections in crisis scenarios

Charlson Varghese,

Senior Director

Published: June 2, 2020

By this time, we all realize the unprecedented impact that the current health crisis is having on the economy. While globally banks are grappling with the various ways in which they can disburse relief offers to their retail and commercial banking customers per directives of their respective central banks, it is equally important to turn our thoughts to how banks could improve the receivables or debt collections process during this time.

Going forward, we are likely to observe large scale unemployment and financial hardship for customers. This coupled with other factors such as regulatory and legal parameters will increase the outstanding loan portfolio of banks. Already, governments of European countries are restricting legal enforcements and claims by banks during this time. Moreover, new legislation that restricts banks from auctioning mortgage backed properties during the period of emergency have also been released. This will lead to a negative impact on the debt collection process of financial institutions.

However, all is not doom and gloom for banks. In the US, the $1,200 CARES Act payments that the US Congress approved in response to the COVID-19 crisis will be credited to the bank accounts of eligible Americans which can be used by the banks to setoff outstanding loan components or fees.

Use a combination of modeling and AI/data analytics capabilities to tackle the collections conundrum

Regardless of the scenario, how can banks face collections challenges in a crisis? Banks do have the option to shrug off such situations and call them one-time occurrences while continuing with their current collection processes. The other option is to recognize these as crisis management opportunities and proactively include them as part of their collections strategy. Since we expect an increase in overdue loans and non-performing assets in the short to medium term, it makes sense for banks to consider effective and efficient decision support options underpinned by digital automation and AI/data analytics capabilities. These can include:

AI/Data Driven Personalized Customer Collection Experience

AI can leverage data from both traditional and digital sources to provide a complete contact and treatment strategy to create a customer-centric experience for collections service personnel. Data from various individual events from customers; lending lifecycle, demographics and bank policies such as business and compliance rules, customer profiles, arrears and payment history, instances of hardship, risk trigger events etc. can be used to generate next-best-actions (NBAs).  These NBAs support timely and preferred dunning options on channels of choice, make outbound calls, monitor and act on situations such as broken promises, litigation, and settlement.

Probability of Delinquency – Proactive Default Risk Mitigation

To arrive at the probability of delinquency, data of occurrences of manmade and economic events is supplemented with similar data from natural disaster events. This data is used to calculate an event risk score, which when coupled with individual customer events calculates to a risk rating for each customer, thereby calculating the probability of delinquency for each customer by using adaptive model predictors such as customer metadata and risk ratings.

The probability of delinquency helps banks monitor and mitigate risks associated with customer default in an unprecedented situation, and the effectiveness of the collections process can be greatly improved even with increasing workload in the future because of the adaptive nature of the model.

Predictive Automated Outbound Dialing

Existing outbound solutions execute a one-size-fits-all strategy and do not promote a personalized customer experience since every customer experiences the same standard process. Using data and predictive models, solutions can be devised for crafting intelligent decisions, with built in configurable compliance rules, to predict the best time to contact customers. The solution can be extended to provide for interactive voice response (IVR) chatbots that deliver payment reminders and fulfill payments while being fully integrated with a bank’s CRM solutions. Automated voice chatbots are efficient and will reduce the time spent by collections agent on calls, thereby reducing cost and improving efficiency. Finally, these solutions can support scale and are configurable with preconfigured cloud components that are built for change.

Where do banks go from here?

In a crisis scenario, if banks consider going down the beaten path, a growth in the collections workforce can be expected. However, handling such a volume of cases can be daunting for collections teams. As such, financial institutions should accelerate the process of digitization and the use of AI and analytics tools to ease the burden on staff, improve customer experience, and reduce cost while contributing positively to the balance sheet

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