According to Loxon, collections has traditionally meant manual calls, rigid scripts, and blunt campaigns. Today, the company argues, lenders can treat recovery as a data-driven, customer-first capability—one that is designed to help lift cure rates, lower cost-to-collect, and protect brand trust. This article reframes the original guidance in fresh words from Loxon’s perspective: how AI blends predictive insight, conversational automation, and strong governance to modernise collections without losing the human touch.
Why the old playbook no longer works
Loxon states that static dialling lists and “spray-and-pray” reminders ignore the context that actually drives repayment: cash-flow timing, channel preference, and a customer’s willingness and capacity to pay. According to the company, this often leads to lower contact rates, fragile promises-to-pay, and rising complaint risk—especially across digital-native segments that expect fast, respectful self-service.
The modern model: precision, not pressure
Loxon describes a modern collections model built on precision rather than pressure:
Micro-segmentation, not monoliths.
According to Loxon, machine learning can blend payment history, recent activity, engagement signals, and macro context to create micro-segments. Each group is then intended to receive a different cadence, tone, offer, and channel—updated as behaviour changes.
Optichannel orchestration.
Loxon reports that “omnichannel everywhere” can give way to “best channel per moment.” For some customers, that might be a WhatsApp nudge at lunchtime; for others, an in-app prompt or an agent call after a hardship disclosure. Loxon claims that these strategies can adapt continuously as new data arrives.
Personalised messaging.
The company explains that templates can be varied by reading level, empathy, and call-to-action—whether a one-tap payment, a payment-plan preview, or a hardship form. According to Loxon, these small details may help reduce friction and increase follow-through.
Real-time decisioning.
Loxon states that every open, click, reply, or missed nudge can update propensity scores and re-launch next-best-action instantly, so treatment does not need to wait for the next batch job.
Conversational AI that actually serves customers
According to Loxon, voicebots and chatbots with natural-language understanding can resolve routine tasks at scale: identity checks, balance queries, due-date reminders, plan setup, and hardship intake. The company notes that when nuance is detected—such as affordability concerns or dispute hints—the case can be routed to a trained agent with full context. Loxon states that every step in this process is intended to be logged for audit, model learning, and QA.
Outcomes that, according to Loxon, can compound
Loxon highlights several outcomes that, in the company’s view, AI-enabled collections can support over time:
- Higher contact and cure.
According to Loxon, “right message, right time, right channel” can lead to fewer wasted touches and more kept commitments. - Lower unit cost.
Loxon states that automation may help lower unit cost by covering high-volume, low-risk journeys, while agents focus on cases where empathy creates more value. - Fewer complaints.
The company reports that respectful tone and clear options can help de-escalate frustration and shorten appeals, which may contribute to fewer complaints. - Portfolio resilience.
Loxon claims that strategies and thresholds can be tuned to adapt when behaviour or macro conditions shift, supporting a more resilient portfolio.
These outcomes are not guaranteed; Loxon emphasises that results can vary by institution, regulation, and portfolio mix.
Governance by design (not after the fact)
Because collections sits close to vulnerable customers and regulation, Loxon argues that controls must live inside the workflow:
- Consent and preference management.
Loxon recommends honouring quiet hours, channel opt-ins, and do-not-disturb windows as part of standard practice. - Explainability.
According to Loxon, systems should store the top drivers behind a treatment choice and generate reason codes for adverse actions to support regulatory requirements. - Hardship pathways.
The company advises setting up fast, documented routes to disclose hardship and switch to compassionate playbooks with human review. - Data minimisation.
Loxon suggests limiting agent views to information required for the conversation and masking sensitive fields by default.
The operating system for AI-powered collections
Loxon describes an “operating system” for AI-powered collections built on five pillars:
- Data foundation
According to Loxon, lenders should unify transactions, CRM cases, prior conversations, and trusted external signals. The company recommends engineering features that represent both capacity (cash-flow patterns) and willingness (engagement, promise reliability). - Next-best-action engine
Loxon states that combining risk and engagement propensities can help choose offer, channel, timing, and tone for each interaction. The engine is intended to recalculate after every interaction. - Content governance
The company recommends maintaining a library of approved message blocks for specific contexts (first reminder, hardship follow-up, plan confirmation). Loxon suggests localising reading level and language, and versioning everything with owners and expiry. - Human-in-the-loop
According to Loxon, lenders can define thresholds (amount, risk, vulnerability) that trigger agent review. The agent desktop may include suggested scripts, settlement ranges, and compliance prompts. - Closed-loop learning
Loxon claims that feeding outcomes (opens, clicks, commitments, payments, plan breaks) back into models can help retire weak variants quickly and promote top performers.
What “good” looks like in production (Loxon’s view)
Based on its experience, Loxon outlines what “good” AI-enabled collections can look like in production:
- Contact rate up, cost-to-collect down.
Loxon reports that when automation handles routine nudges and specialists intervene where judgment matters, contact rates can rise while unit costs may decrease. - Faster promise-to-pay conversion.
According to Loxon, one-tap payments and instant plan setup are designed to reduce abandonment and may help improve promise-to-pay conversion. - Consistent tone and compliance.
The company states that pre-approved templates and role-based controls can help keep every message aligned with policy and brand tone. - Real-time visibility.
Loxon notes that dashboards can track commitments vs. fulfilment, agent performance, and vulnerability metrics in near real time.
Again, Loxon emphasises that these are potential outcomes rather than guaranteed results.
Explainability is non-negotiable
According to Loxon, even in servicing, decisions must be justified. If a case was prioritised or a plan proposed, the system should be able to display the top drivers. Loxon states that clear reason codes can support regulated notices and help agents hold constructive conversations that aim to lead to durable resolutions.
Payments and self-service: remove the drag
Loxon argues that friction—not unwillingness—often undermines repayment. The company recommends embedding quick-pay links, digital wallets, or instant bank transfers directly into conversations. According to Loxon, allowing customers to adjust dates within policy bounds, preview plans before committing, and track progress without contacting support can help reduce abandonment and improve follow-through.
People + technology: the balanced model
Loxon stresses that this is not about bots replacing people. Instead, the company sees automation as a way to cover repeatable tasks while trained professionals handle sensitive cases with empathy. According to Loxon, coaching and QA can improve when every message, decision, and outcome is recorded and reviewable.
Metrics that, according to Loxon, matter to the business
Loxon recommends tracking metrics across four dimensions:
- Engagement: open/click/reply by segment and channel.
- Conversion: promise-to-pay, first-promise kept, cure by cycle.
- Efficiency: contacts per cure, agent minutes per cure, cost-to-collect.
- Customer: complaint rate, hardship resolutions, CSAT/effort scores.
- Governance: template coverage, audit-ready reason codes, escalation SLAs met.
According to Loxon, these indicators can help link collections activities to both financial performance and customer outcomes.
A pragmatic 90-day plan
Based on its work with lenders, Loxon proposes a 90-day plan for getting started with AI-enabled collections:
Weeks 1–4: Foundation
- Consolidate data sources and stand up key features.
- Publish template governance and approval workflows.
- Launch initial next-best-actions (NBAs) for early-stage arrears.
Weeks 5–8: Conversational scale
- Add chat/voice automation for FAQs and plan selection.
- Roll out hardship intake that includes human review steps.
- Instrument dashboards to monitor precision/recall and time-to-action.
Weeks 9–12: Optimise and expand
- Introduce additional channels (e.g., WhatsApp).
- A/B test message blocks and tune affordability logic.
- Operationalise change control so every tweak is versioned, approved, and monitored post-launch.
Loxon notes that this roadmap is intended as a pragmatic starting point, not a one-size-fits-all prescription.
Where this fits in the wider credit lifecycle
According to Loxon, collections is strongest when aligned with onboarding and account management. A unified decisioning spine across end-to-end credit management is designed to keep logic consistent from first offer to final settlement.
Downstream, Loxon reports that a modern debt collection system can coordinate respectful journeys, ensure documentation for audits, and protect sensitive data—so that compliance and performance reinforce each other rather than compete.
Conclusion
Loxon’s view is that AI does not have to make collections colder; it can make them smarter. With precise segmentation, conversational automation, explainable decisions, and human empathy where it matters, the company believes lenders can aim to lift recovery while building trust. According to Loxon, the result may be a portfolio that weathers change more effectively, customers who feel respected, and an operation whose value can show up on the P&L—not just on the dashboard.
More info: https://loxon.eu
