Digitisation Boost to Debt Collection Agencies

As delinquencies increase, lending institutions worldwide are increasingly and proactively switching to digital debt collection techniques to boost their recovery rates cost-effectively.

FREMONT, CA: Almost every industry in the world has been affected by contemporary technologies, and the banking sector is no exception. Now, technology is making its way into the debt-collecting industry. Lending institutions all around the world are aggressively converting to digital debt collection strategies as delinquencies rise to maximise their recovery rates while minimizing costs.

These modern technologies, like Artificial Intelligence (AI), Machine Learning (ML), Data Analytics, Blockchain, etc., enable lenders to act wisely and recover their debts without negatively affecting the customer's (borrower's) experience.

Digital debt collection platforms that use AI and ML employ a variety of ways to increase efficiency; a select few gather important data from numerous sources and channels. This data could include details about the borrower's profile, income, preferences, credit and financial history, and payment history to generate smart micro-segments and increase the effectiveness of the lender's traditional collection techniques. A few other platforms collect data on client payments and engagement and employ very efficient digitalised collection procedures to encourage customers to pay their debts. By using this vital information, lenders can get a comprehensive understanding of the borrowers' repayment options and chances of successful debt repayment. Their intelligence also enables them to tailor their debt collection tactics to each borrower. Further, using the right communication strategy, each borrower is engaged in a way that suits their preferred timing, channel, and style.

The strength of these contemporary technologies also enables lenders to predictably evaluate the danger of non-payment or default.  This is accomplished by identifying certain notable trends in the data, which alarms the lenders and forces them to decide what to do next. This aids them in avoiding the lapse in payment on their accounts. Lending institutions may provide unique repayment choices, discount plans, etc. for a select number of the identified riskier situations to recover the funds and cancel such accounts.  In addition, AI-based algorithms enable lenders to improve collections and increase repayments by identifying borrowers with the ability to pay. Using digital collection methods has helped creditors and debt collectors recover more money by an average of 65 per cent.

It's interesting to note that more borrowers are starting to favour a digital, non-intrusive method of debt payback. Along with allowing clients to make flexible payments, these channels also provide them with a simple self-service alternative that allows them to take care of their debt on their terms, at their convenience, and without having to speak with a collection agent. For the convenience of borrowers, many lending institutions have implemented AI-based bots to guide them through the payback process. Users appreciate personalised, interesting, and in their preferred language, technology-enabled experiences. The rates of debt recovery are positively impacted by this enhanced client experience.

The approach lenders must take to achieve more efficient collections has been made clear by research into the customer experience of credit delinquency. Customers essentially stated that their contact preferences and reactions are determined by personal factors that have little to do with the risk classifications and contact guidelines established by lenders. While a smaller segment of customers continues to be more receptive to conventional contact techniques, the majority of customers prefer to be contacted and act through digital means.

Issuers must first develop a sensitive multichannel interaction strategy to address the varied preferences of their clients. The strategy needs integrated infrastructure, technology, automation, machine learning, and analytics capabilities, as well as a well-planned implementation. Delivering customised communications to the appropriate clients through the appropriate channels and in the appropriate order is the goal. Implementing a truly multichannel approach will be expensive, however, it will be worth it for issuers in terms of happy consumers and more productive and efficient recoveries.

Digital collection techniques give the lender the ability to support a system that gathers 360-degree data on clients. Increased data collection not only improves the lender's comprehension of borrower behaviour but also fosters an insight-led strategy in which lenders can use historical data to foresee future changes and properly plan for them. Lenders can benefit from advanced bespoke analytics and single-view borrower dashboards by actively managing their portfolios, reducing their risk exposure, and promoting a positive cash flow.‍

Therefore, to leverage the potential for collections, issuers must first develop a sensitive multichannel contact strategy to take into account the varied interests of their consumers. The strategy needs integrated infrastructure, technology, automation, machine learning, and analytics capabilities, as well as a well-planned implementation. Delivering customised communications to the appropriate clients through the appropriate channels and in the appropriate order is the goal. Implementing a truly multichannel approach will be expensive, but it will be worth it for issuers in terms of happy consumers and more productive and efficient recoveries.

Institutional lenders are expanding into newer customer segments like the new to credit and extending their services to various remote and underserved areas, those where the penetration of formal credit has previously been low, as digitalisation and AI/ML technologies drive efficiencies in debt collections. This accelerates the rate of economic growth in emerging economies and helps them fulfil their greater objective of financial inclusion.