In recent years, the emergence of machine learning and AI technologies has transformed various industries by automating repetitive tasks and improving customer experiences. The finance sector has particularly benefited from AI and machine learning, and in this blog we’ll explore how these technologies are helping companies align collections processes with business strategies, improve profitability, maximise performance and increase customer retention.
Debt management technology: turning collections into a profit centre
Despite the notable steps forward yielded by technology in finance, the collections space finds itself multiple stages behind other key business functions, with many companies often failing to prioritise the value of debt management systems in reducing losses, aligning strategies and increasing profitability in the long term.
Consequently, antiquated collections practices are still commonplace, even amongst the largest operators in the finance sector. The use of spreadsheets, phone calls and letters remain key components of businesses’ dunning strategies. But in a data-driven, increasingly connected lending environment, these labour-intensive approaches often fall short in yielding significant returns. They also drain business resources and drastically affect the cost-benefit analyses of recoveries, meaning low-value debt is often written off or sold to external debt collections agencies (DCAs) due to the weight of resources required for retrieval.
In response to this widespread reliance on outdated collections processes, AI and machine learning offer advanced tools that improve the accuracy of recoveries, addressing the challenge of modern debt management tools being undervalued as potential profit centres for businesses.
The outreach methods delivered by debt management software and supported by AI also vastly improve resource allocation. Collections teams can now tailor their messaging and reach customers using modern CRM tools within debt management software, allowing for increased strategic focus and better analysis of large amounts of customer data.
By providing collections teams with improved AI-led data insights, targeted messaging, and modern communication tools, the rates of debt recovery can be significantly increased. This, in turn, results in increased liquidity and greater long-term viability for lenders, especially during times of economic uncertainty.
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Optimising business strategies through AI and machine learning
AI and machine learning technologies can analyse historical data to identify patterns and trends, enabling collections professionals to make more accurate predictions about future behaviour and payment patterns. This can help to inform debt management strategies and improve recovery rates. In turn, businesses can inform their sales, marketing and outreach strategies with a more high-level view of their customers’ spending and repayment patterns.
This disparity between investments in customer acquisition, where businesses pour resources into sales, marketing, and technology, and the relative neglect of modern debt management solutions, lays bare a shortsighted prioritisation of acquiring customers at the expense of effectively managing their debts.
Consider these key marketing and debt management-related statistics:
- From 2021-2024, digital ad spend in the U.S. is predicted to grow from US$211bn to $317bn, an increase of almost 50%, while in the U.K. it will rise from almost US$32bn to more than US$42bn (+33%).
- In a survey conducted by The Ascent, only 26% of respondents reported that their credit card issuer offered a modern, user-friendly mobile app for managing their accounts, indicating a lack of investment in modern debt management solutions.
- The US debt collection industry is estimated to be worth over $13 billion, yet many debt collection agencies still rely on outdated methods and technology, such as robocalls and snail mail, instead of investing in modern debt management solutions that prioritise consumer experience and communication.
Further, AI and machine learning are improving predictive modelling in debt management. These technologies underpin the receive platform, facilitating analysis of historical data to identify patterns and trends, enabling collections professionals to make more accurate predictions about future behaviour and payment patterns. This can help to inform debt management strategies and improve recovery rates. In turn, businesses can inform their sales, marketing and outreach strategies with a more high-level view of their customers’ spending and repayment patterns.
Increased brand loyalty: a smarter approach to improving customer experiences
As outlined, by adopting a data-driven approach to collections, businesses are gaining valuable customer insights to drive more tailored messaging, increasing engagement and yielding high rates of return. When coupled with self-service payment tools, this approach compels customers to engage with your messaging and empowers debtors to action repayments autonomously.
With UK polls suggesting that almost 90% of customers will not answer calls from unknown numbers, the need for dunning strategies based on modern communications methods is clear.
Further, by adopting smarter segmentation strategies and leveraging data for up-to-date customer views, at-risk borrowers can be identified earlier in the debt journey, letting you adjust your strategies to reduce defaulting and cut risk at scale. This both safeguards your profitability and allows you to modify your lending practices to respond to shifting market conditions quickly.
Empowering your workforce with AI technologies
While AI-powered debt management is transforming business strategies and enabling true scalability, it’s also making everyday processes quicker and more efficient for collections professionals. With receeve’s AI-driven solution, your team can now simplify workflows, aggregate data, run reports, trigger communication en masse and analyse their performance with ease, from anywhere – all from a single platform. This leads to easier-to-implement collections strategies, reduced user error, simple process standardisation and, ultimately, a better-equipped collections team.
For businesses looking to effectively manage a growing collections operation, the constant need for more employees can be mitigated by incorporating AI-driven technologies. This allows existing staff members to enhance their processes through rapid adoption. Additionally, new team members can be easily onboarded and empowered to implement best-practice approaches right from the beginning, enabling your business to establish an efficient collections function immediately. This can be especially beneficial for businesses that aim to integrate additional software, thanks to the seamless integration facilitated by APIs.
An additional advantage of adopting a debt management solution built on future-proof architecture is the ease of implementation. By outsourcing the maintenance and integration of your solution, costly and labour-intensive development and upkeep expenses are cut, since the software vendors are responsible for management of the system. This has the added benefit of enabling cross-national collections practices (in the case of cloud-native tools) and consistency across different regions, since you’re provided with a single point of orchestration and unified dashboard from any location.
AI and machine learning are providing richer data insights in debt management. These technologies can analyse vast amounts of data to identify trends, patterns, and correlations that would be impossible for humans to detect. Factor this in with simple-to-use interfaces, up-to-the-minute reporting, and assured interoperability within your existing tech stack, and your collections teams are truly empowered to improve their performance from the outset.
The steps to better debt management
Viewing debt collection as a secondary task has long been commonplace for businesses. But by factoring collections tools, processes and results into their overall business strategy, companies can yield better customer experiences, improve rates of retention, reduce employee turnover and better finetune their risk function at scale.
If you want to streamline your collections processes and align them with your overall business objectives to enhance your recovery rates, improve efficiency, and retain more customers, get in touch with us today. Our team can provide you with tailored insights and practical case studies of how AI-driven collections approaches built with the receeve platform have assisted organisations in transforming their debt management processes using a truly scalable solution.