PAYMENT ACCURACY & OPTIMIZATION
Debt detection, collection, recovery and prevention are a common and critical business problem widely seen in government agencies and business operations to improve and optimize payment accuracy and compliance. Debts (overpayments) arise when a client is expected to pay her/his liability on due date but it remains unpaid. For example, Government agencies pay healthcare allowance to the eligible individuals each year based on personal situations. If an individual may have been over-paid due to some reasons, and the client needs pay the money back. The amount over-paid becomes debt that needs to be detected and recovered instantly. This involves early intervention. By engaging with clients early - as soon as a debt arises - the agencies can help the debtors manage their obligations and prevent their debt from escalating. If debtors choose not to respond to the recovery, harsher treatments might be applied to ensure fairness to all clients and to reinforce community confidence of compliance. However, the debt recovery, prevention and optimisation may be undertaken in alignment with client's specific condition and circumstances for more personalised customer care.
We developed state-of-the-art data mining models to improve efficiency of debt collection activities by deeply scrutinizing client behaviours, risk profiles, debt case characteristics and impacted payments. The data mining models disclose evidence for risk management, early prediction, intervention and treatment, towards delivering discriminative business rules, optimising current business lines, and discovering inside factors triggering client/debt risk. In order to reduce the cost of collection and the number of collectable debt cases, an effective and practical method is to optimise resources, for example, as follows.
- Finding characteristics of the clients who are likely to self-finalise cases even without any actions. It will be helpful to reduce cost without impacting the collection.
- Finding characteristics of debts/clients which are likely to respond to a given action. It will enable each debt case to be matched with the most effective and low-cost action. Assigning easily-collectable cases to auto action, instead of manual processing, will allow FTE to be allocated to most productive actions.
- Looking for the next best action for a case according to its current status and situation.
- Predicting potential high-risk debt cases, to make early-prevention on those cases.
- A huge number of debt cases tend to remain unprocessed. If each case was assigned with an accurate priority score, which is based on business requirement, it will guide manual actions to deal with high priority cases first, that would improve the efficiency of manual processing.
Deliverables & Benefits
The outcomes of the debt analytics are multi-fold, generating informed business models for purposes including scoring risk of debts, optimal treatment of debt cases with the most cost-effective actions, discriminative business rules for intervention and treatment, improved customer relationship management, and early prediction of future debt occurrences. Risk scores are generated for predicting self-finalised debt cases. Based on the predictive scores, huge amounts of money was saved each year after the recovery optimization.