Case Study
Fintech saves $25M in write-offs with automated dunning
Results
$25M+
$25M+ saved annually from write-offs
100%
Hit 100% reconciliation across collections
Speed
Tightened collections cycle and DSO
Company Profile
Public global fintech operating across fleet, travel/corporate payments, and healthcare benefits, fleet cards serve millions of vehicles, corporate solutions process tens of billions in annual purchase volume, and the healthcare platform helps hundreds of thousands of employers and tens of millions of consumers manage healthcare expenses. Thousands of employees across multiple countries and currencies.
Challenge
Manual dunning couldn't scale with the receivables book
Collections were aging at scale. The company runs an invoice-factoring business that buys receivables from clients needing working capital, then collects from the underlying debtors. Most debtors paid on time, but enough slipped past terms that the team was constantly chasing late payments by hand.
The manual chase didn’t scale. As the receivables book grew, invoice aging rose and days-sales-outstanding (DSO) drifted in the wrong direction. Every dollar that aged past collectability was a write-off; every late dollar pushed working-capital cost higher. The team needed a consistent, scalable approach to keep aging in check.
Solution
Risk-scored dunning ladder automation
The firm deployed an automated dunning workflow tuned to its invoice-factoring operation. The solution scored every active receivable for default risk, applied a configurable dunning ladder (timing, message, escalation path) maintained by the business, and triggered the right reminder to each debtor at each stage of aging.
As the bot took over the routine reminder work, collections staff shifted to higher-judgment escalations and complex accounts. Reconciliation tightened; DSO drift reversed.
The configuration-led design also gave management a reporting view of dunning effectiveness across the book, supporting continuous tuning of the ladder and surfacing patterns by debtor segment that informed credit decisions upstream.
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