IBM has stated that we’re living in the ‘data economy’. Innovative organisations across the world are using data and AI-based tools to power their operations, relying on them to reveal key customer insights, signpost areas for improvement, and provide ongoing performance updates.
But banks and other financial institutions have been fairly slow on the uptake. Consider the following statistics:
This is in stark contrast to data analytics leaders like Amazon, Spotify, and Netflix, whose entire business model revolves around successfully using customer data to provide personalised, sought-after customer experiences. So how can banks and financial institutions flip the switch, unlocking greater value from their customer data and boosting their profits as a result?
This blog will examine how data and AI can help one particular financial business case: collections. It will examine the tools/systems that collections departments currently lack before exploring the 5 ways that data analytics and AI can increase collections success.
Data analytics can make your collections approach both more efficient and effective. However, it is not as simple as flicking a switch and seeing the results pour in. You have to first ensure that your team is equipped with the right tools for the job.
Unfortunately, as things stand, collections agencies lack the following must-have tools/systems.
Digital channels are not only more effective than traditional alternatives (like direct mail), but they also require far less time and resources to prepare. And there is another major benefit to sending digital outreach messages: they are easily measurable. There is no way to tell whether a past-due customer ever opened your mail.
You can, however, tell exactly when they opened your last email—and whether they clicked through to the repayment landing page. These data-driven insights will then highlight areas where you can potentially improve your strategy going forward.
If you want to put data to good use, then you need to make sure it is accurate, up-to-date, and easily accessible. In other words, you need a centralised data repository that automatically integrates data from a variety of sources.
Having access to all your data in one place means that it can be easily analysed. You have the full picture in front of you—everything from your customers’ preferred channels to the data indicators that demonstrate how successfully your various outreach strategies have performed.
It is no mean feat trying to draw solid conclusions from a raw dataset. This is why you need a dashboard that can visually demonstrate what your data is telling you. It will reveal critical insights and make them clear as day, meaning no key findings ever go unnoticed.
Many collections agencies still focus on the value of their total number of active claims. They place too much emphasis on the overall value of what they are owed instead of diving into how they can improve repayment rates, which customers need further assistance, and how they can enter into a two-way dialogue to improve the likelihood that they will be repaid in full.
Data analytics and AI help collections departments provide an improved, more tailored customer experience—which in turn drives a commensurate increase in customer loyalty, improves recovery rate as well as increases profits.
There are 5 ways in which data analytics and AI can help increase your collections revenue:
1. Segmentation for better targeting
Data analytics sheds light on each individual past-due customer: their behaviour, preferences, demographics, and payment history. This data can then be put to good use, with collections teams segmenting like-minded customers into groups according to their prediction of customers’ behaviour.
For instance, one segment might respond better to infrequent messages sent via email—while another segment would prefer frequent SMS messages. By grouping similar past-due customers together and serving them tailored dunning experiences, you will boost your collections success.
2. Identification of high-risk customers
When it comes to collections, not all customers are equal. Some might only owe your company €100 and have a great track record of timely repayments. Others, however, might owe your company €5,000+ and already be well past the date by which they were supposed to pay you back.
Unsurprisingly, high-risk customers require more time and attention from your team. Data analytics and AI allow you to instantly identify which customers are behind (or appear to be struggling with) their repayments. More importantly, you can even use historical data to predict which new customers might fall into a high-risk group and approach the customer to design a tailored instalment plan that suits their specific needs/context.
By entering into a dialogue with customers (and not simply imposing terms on them), it is far more probable that they will repay what they owe in full.
3. Reduces operational risk
Data analytics provides collections departments with everything they need to know to optimise their operations. It reveals customers’ behaviour and preferences, allows them to instantly assess their outreach performance, automatically identifies the best strategy to use, finds areas for improvement, and displays the impact of any changes.
This means that they can craft the ideal strategy at all times. No longer will you be relying on inadequate outreach processes—you will instead be working with the most effective strategies, most (if not all) of the time.
4. Improves the customer experience
Data is so powerful because it lifts the lid on individual customers. Instead of simply knowing their name and address, you can now dig into what makes each individual tick. Collections departments can identify ways in which customers would like to be communicated with (the channels, messaging, and tone of voice, for instance) before using these insights to guide their dunning approach.
This means an improved customer experience—which in turn translates into an increased recovery rate and revenue. Since only 27% of consumers feel like the banking/finance industry is customer-centric as things stand, there is clearly some work still to be done.
5. Automates low-priority interactions
AI-powered virtual agents (such as chatbots) are able to handle fairly low-complexity customer interaction, saving businesses up to 30% in customer support-related costs. They are linked up to your centralised data repository—meaning they have all must-know information and can effectively serve a wide range of customer requests.
This means that your highly skilled agents can spend their valuable time dealing with high-risk customers who would benefit from human-to-human interactions. You will be better placed to focus your human resources where they are needed the most (i.e. high-priority interactions).
Data has transformed countless industries, and credit and debt collections are next in line. Data allows collections departments to better understand and segment their customers—in turn, helping them provide more welcoming and personalised experiences. Outreach strategies can be put to the test, with AI-based algorithms identifying your most effective templates and automatically optimising them. As a result, collections departments will drive increased revenue while simultaneously becoming more efficient in the process.
Ready to embrace data-driven dunning? If so, book a demo with one of our experts to learn more about how receeve’s AI-powered collections management software works.
Design can make or break a product. The rise of the iPhone arguably had less to do with its features themselves and more to do with the design. Competitors could boast similar functionality—but the ...