AI can transform debt collection with a more personalised approach

Andrew Warren

Current trading conditions means it’s the right time for companies to adopt a smart contact strategy, writes Andrew Warren.

He argues that opportunities exist right across the collections lifecycle

Customers are in dire need of support from their banks and a tailored approach to debt collection, and banks are expected to act in a customer-centric way, with proper and early consideration of conduct risks.

However, debt collection strategies can be complex, inefficient and outdated. In today’s digital-first world, customers expect speed and demand flexibility, accessibility and choice. Yet, the banking and financial services sector has largely failed to evolve to meet these expectations.

The emphasis now needs to be on boosting customer experience, streamlining operations and cutting costs and banks are starting to realise the importance of improving the effectiveness of their collections operations. There is also pressure from regulators, and how debt collections are being approached is being closely monitored.

While there is rarely a shortage of data available to debt collectors, it is not always easy to find the information they need

Data analysis and AI technology has already significantly improved customer experience and helped deliver efficient advances in various sectors, for example with the use of chatbots or helping to create more personalised customer communication with greater access to and analysis of customer data.

Now this approach needs to be applied to debt collections, maximising the potential data and AI can have on the whole debt lifecycle and enabling personalised experiences for customers.

Debt collection has been a big industry challenge for years. While there is rarely a shortage of data available to debt collectors, it is not always easy to find the information they need. Finding the right means or most accurate and up-to-date contact information, for example, continues to prove challenging.

Additionally, without access to the most comprehensive data on customers, banks cannot accurately analyse and forecast losses, segment by segment, in real time. And because the data they do have is generally siloed, it is not being used effectively. The impact? Sub-optimal recoveries and higher costs.

However, technology can help to overcome these challenges by sifting through what is available more efficiently and by providing more comprehensive datasets, rather than limiting the information they have access to.

Empathy and debt collection might not be a notion that easily goes hand in hand. However, debt collectors are in fact tasked with helping and supporting their customers, and an empathy-driven approach can greatly improve this experience. And this doesn’t only benefit the customer. For debt collectors, an empathy-first engagement can deliver higher NPS scores (10-20bps increase), an increase in recovery (5-15%), a productivity increase (5-20%), and real-time call QA adherence (50-100%).

When debt collectors understand their customers’ problems, and are able to empathise, it enables them to think proactively about a solution. When we understand the challenge the debtor is facing, debt collectors can assist them in finding the right solutions.

Understanding data is the key to identifying trends and anomalies to help adopt this kind of approach. With the use of data analysis and AI technology, banks can provide new opportunities for hyper-personalisation across the whole debt lifecycle.

Customers today expect a personalised, digital experience from banks. Phone calls and letters are fast becoming outdated

Collating data through the use of AI can help debt collectors with messaging, timings and tone to help improve collections rates and improve customer experience.

The impacts can be transformational, especially in terms of cost savings, greater agility, and more empathy for an enhanced customer experience.

Customers today expect a personalised, digital experience from banks. Phone calls and letters are fast becoming outdated means of contact – and unreliable.

But with a smart contact strategy, where organisations use smart, connected, and automated technology to align operations with the changing dynamic of customer interaction, debt collectors can reach the right priority contact, at the right time, through the right channel.

For example, by harnessing machine learning to help focus banks’ attention on contacting debtors that are more likely to settle outstanding debt or use analytics to identify vulnerable customers early on.

By using data and AI and designing algorithms with predictive models, debt collectors will be able to revisit their whole engagement strategy for collections. Instead of following fixed, inflexible processes, where every customer is treated the same, they can become much more dynamic, insight-driven and future-ready.

The pandemic has resulted in a huge rise in debt levels across businesses. Banks and debt collectors simply cannot work to this scale with the existing, legacy systems many still have in place.

The need to automate and revolutionise the industry and current approach taken to debt collections has never been more apparent.

This is where data analysis and AI technology comes in, though this does not have to mean an expensive, disruptive systems transformation. With an augmented AI-powered approach, banks and debt collectors can recreate customer experiences, and reimagine business operations, that support customers and drive better, faster resolutions.

Andrew Warren is Head of Banking & Financial Services, UK&I, Cognizant


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