Case Studies

Case Study: Reducing Static Arrears and Roll-to-Worse for a Major Australian Bank

Executive Summary

Recoveriescorp partnered with one of Australia’s largest banks to improve collections performance across its personal loan portfolio. Over a four-month period, static arrears reduced from 38% to 34%, roll-to-worse decreased by nearly 40%, and cure rates increased by 6 percentage points, with no increase in FTE.

The results were achieved through an end-to-end collections management model supported by AI-driven insights, behavioural segmentation and targeted treatment strategies aligned to different stages of delinquency. These capabilities supported account prioritisation, channel selection and agent allocation based on risk profile, customer behaviour and portfolio performance.

The Client

One of Australia’s largest financial institutions, servicing more than 17 million customers across consumer, business and institutional banking.

Recoveriescorp has partnered with the bank for more than 13 years, providing collections support across personal loans and credit cards in both pre- and post-write-off environments. Over time, the engagement expanded to include insourced collections teams, with Recoveriescorp-employed agents operating within the bank’s systems, processes and governance framework as an extension of its collections function. Recoveriescorp is responsible for recruitment, training, people leadership and quality assurance, while the bank retains responsibility for operational direction and priorities.

The relationship has also evolved to include Recoveriescorp agents being used as a benchmark when assessing the performance of new collections recruits, reflecting the maturity of the partnership and the consistency of operational delivery.

The Challenge

The personal loan portfolio was experiencing declining collections performance. A seasonal reduction in recovery outcomes, commonly observed during the first quarter, coincided with increasing financial pressure on customers and a requirement to improve results without additional resourcing. Four key challenges were identified:

• Elevated static arrears. Static arrears, where accounts remain at the same delinquency stage month on month, exceeded 35%, while cure rates remained below target levels.
• Increasing roll-to-worse. The proportion of accounts progressing into the 60 to 89 days past due (DPD) segment was increasing, driving greater exposure to write-off risk.
• Reduced recovery effectiveness. Despite ongoing customer contact activity, fewer accounts were returning to performing status, with customer responsiveness affected by inflationary and cost-of-living pressures.
• Fixed workforce capacity. Performance improvement was required within existing FTE constraints, placing greater emphasis on treatment effectiveness, prioritisation and resource allocation.

Our Approach

Recoveriescorp applied an end-to-end collections management model supported by AI-driven insights, behavioural segmentation and treatment design. Each stage of delinquency was managed through a treatment strategy aligned to its specific risk profile, customer behaviour patterns and performance objectives.

Early stage (0 to 30 DPD) – Strengthening early intervention

Propensity-to-pay modelling and behavioural segmentation were used to identify accounts showing early signs of financial stress and prioritise engagement activity. Digital communications were brought forward within the treatment cycle, supported by IVR-based outreach and access to a self-service portal that enabled customers to make payments, establish arrangements and access hardship support. Contact strategies were tailored according to customer behaviour, risk profile and likelihood of engagement.

Mid stage (30 to 60 DPD) – Improving account stabilisation

Treatment strategies focused on increasing customer engagement before accounts progressed into later stages of delinquency. Earlier voice outreach, response-based workflows and targeted payment arrangements were used to improve account outcomes. Resource allocation was aligned to risk and engagement profiles, with manual collections activity focused on higher-risk customers and digital channels supporting lower-risk segments. Self-service capabilities enabled customers to establish and manage payment arrangements throughout the treatment lifecycle.

Late stage (60 to 89+ DPD) – Prioritising high-risk accounts

Collections activity was concentrated on customers presenting the highest risk of progression to write-off. AI-informed performance insights and business intelligence tools were used to identify agents delivering the strongest outcomes in late-stage delinquency environments. These agents operated within a portfolio ownership model, providing end-to-end accountability for customer outcomes. Performance measures and incentives were aligned to roll-better and write-off prevention objectives.

The Results

Over a four-month period, the personal loan portfolio recorded sustained improvements across key collections performance measures:

• Static arrears reduced from 38% to 34%, improving the rate at which accounts progressed through the collections lifecycle.
• Roll-to-worse decreased by nearly 40%, reducing the flow of accounts into 90+ DPD and limiting exposure to write-off risk.
• Cure rates increased by 6 percentage points, with more customers returning to performing status.
• No increase in FTE was required, with the improvements delivered through treatment optimisation, targeted resource allocation and data-informed decision making.

Why It Matters

The engagement demonstrated how behavioural segmentation, targeted treatment design and performance-led resource allocation can improve collections outcomes within existing workforce capacity. By aligning strategies to different stages of delinquency, the bank achieved measurable improvements in static arrears, roll-to-worse performance and cure rates.

The resulting operating model provides a structured framework for managing future periods of portfolio stress, leveraging more than 13 years of collections experience and operational partnership.