AI, machine learning and robotic process automation (RPA) are reshaping debt collection and credit management. “AI and automation won’t replace people; it eliminates repetitive tasks, freeing teams to focus on higher-value activities,” career credit professional Ian Wood explains.
He was speaking at a recent seminar hosted by DebtSource Credit Management, Credit Business Network and Credit Professionals Institute. Wood shared his vision of how AI can transform credit management strategies, enhancing cash flow and profitable opportunities.
For example, capturing application forms can be automated, allowing humans to focus on evaluating loans and ensuring governance. He emphasised that AI and automation should complement human oversight, ensuring responsible operation.
He stressed that effective credit management begins at the point of origination, not when loans are already in arrears. “If you get it wrong from the start, it affects the entire lifecycle,” he warned, highlighting the need for quality data from day one, especially when dealing with SMEs where financial information may be limited. For small businesses, non-financial data such as reputation and social media presence can be crucial, as personal and business finances may intertwine.
RPA can automate mundane tasks like generating statements or gathering financial data, improving efficiency. “Why should credit controllers manually request and send statements when this can be automated?” Wood asked, pointing out that RPA allows customers to access statements via a self-service portal, saving time and reducing errors.
Using AI to analyse large datasets can detect trends or early warning signs of financial distress. “AI can flag deviations in cash flow from forecasts, such as drops in revenue or rising costs, alerting teams to potential problems,” Wood explained. The aim is to use AI for decision support while leaving room for meaningful human interaction. AI helps manage clients at different stages, from early collections to legal enforcement.
Wood also highlighted the importance of clearly communicating credit terms from the outset, reinforcing them digitally throughout the relationship. “An SMS costs only 12 cents—why not use it to remind clients of their terms, especially during onboarding?” he asked.
Emphasising the need to build strong client relationships from day one, he said: “Efficient onboarding sets the stage for trust and long-term partnerships. I believe most clients intend to pay, but AI and predictive analytics can help identify the small minority who might be bad-faith actors. AI helps avoid granting credit to clients unlikely to pay,” he noted.
AI can also drive consistency in decision-making, as automating processes ensures decisions are based on predefined rules, reducing human error.
Tailoring credit facilities to the specific needs of each business is another benefit of AI. Wood explained that different businesses require different credit structures—for instance, a retailer with seasonal stock needs a different facility than a steady, year-round business. “Understanding these nuances upfront can prevent defaults and reduce the need for legal enforcement,” he noted.
Knowing clients well is key to managing credit portfolios effectively. “If you understand your clients and their business cycles, you’re in a better position to prevent defaults,” he said. Regular monitoring and using RPA to capture and analyse up-to-date data allows businesses to make informed decisions quickly
“You can automate digital communications, such as payment reminders, with RPA to reduce the administrative burden,” he said. This approach also improves client experiences. Sending thank-you notes after receiving payment, for example, can strengthen relationships and maintain trust.
When it comes to collections, predictive analytics help differentiate clients with temporary issues from those with deeper, structural financial troubles. “By utilising reporting and predictive analytics, you can flag clients who need specific treatment based on their payment performance,” Wood explained.
It is important to constantly monitor clients’ financial health: “It’s vital to track your portfolio regularly, not just at origination. Regular reporting and predictive analytics let you spot risks early and adjust your strategy,” he said. Identifying early warning signs can allow businesses to reduce credit exposure or renegotiate terms before issues escalate.
Using AI, businesses can apply automated reporting and monitoring at the portfolio level to detect early signs of risk. “The key is to act early—whether by reducing exposure, adjusting credit facilities or meeting with clients to resolve issues before they lead to defaults,” he said. External data sources like credit bureaus and social media can further help monitor client health. He explained that credit triggers, such as legal troubles or social media complaints, can provide early warnings, allowing businesses to take preventive action before problems escalate.
However, Wood urged that credit providers must act swiftly when necessary. “At some point, you have to distinguish between a client and a debtor, or ultimately incur more losses. Act with sound reasoning and execute promptly to avoid prolonged financial losses,” he advised.
Wood also recommended selecting the right collections partner. “Work with firms that specialise in the full spectrum of early-stage collections to legal enforcement to recover funds effectively,” he said, ensuring a smooth process for addressing payment defaults.
Wood’s vision for integrating AI and automation into credit management processes aims to minimise risk, improve efficiency and strengthen client relationships. “By continuously monitoring clients and automating processes, businesses can stay ahead of defaults and enhance their overall credit management,” he concluded.
