AI IN DEBT OPTIMISATION Th The Debt t Challen llenge ge A - PowerPoint PPT Presentation

AI IN DEBT OPTIMISATION

Th The Debt t Challen llenge ge A growing issue that businesses must address to: Accurately predict what debt will or won’t be resolved Effectively plan for debt to mitigate risk in times of uncertainty Identify and protect vulnerable customers, preventing potential debt

Key y Concer erns s in Debt

Op Optimi misi sing ng Debt t with AI AI PREVEN ENTIO TION – Predict customers at PROV OVISI ISION – Forecast bad debt risk of debt, implement strategies and more precisely and optimise annual actions to prevent debt and mitigate financial provision operational risk COLLECTION ION - Assess the SIMULATIO ION - Multi-dimensional models and accurate, data-driven likelihood to pay and Implement scenario modelling optimum strategy and channel for collections

CASE STUDY AI in Action: Intelligent Debt Collection in Utilities The Customer: The Sector: Utilities The Requirement: Driving customer enhancements in debt resolution The Solution: Using a series of ML models to uncover the ‘Next -Best- Path’ actions to shorten the debt resolution process AI/ML models build to intelligently predict what method would maximise the success of debt recovery Deployed using Inawisdom RAMP (Rapid analytics and ML platform) and proven Discovery approach The Result: ➢ Hyper-personalised next best action and collections path ➢ Improved collections by 22 days s ➢ Streamlined process, reducing debt resolution cost and time ➢ Embedded by NWG into their existing collections process 5

Building the ‘AI in Debt’ story Customer Case Study: Northumbrian AI in Debt Overview Customer Journey: Accelerated Water Group (NWG) path to production

Next st step: : Inawi wisd sdom Disc scovery very Works kshop hop Getting g Started ted AI/M /ML L – Debt t Optimisat ation on Move to Prod oduc uctio tion n – Debt Optimisat ation on Opportunity Roadmap and Defining Success Productisation & Acceleration Roadmap Business, IT, DevOps, DataOps, Data Science Inawisdom + Business Stakeholders • Business Priorities/KPIs • Review existing AI/ML Roadmap/Models, Scope • Ideation’ - The Business Opportunities production MVP • Speed to value and quick wins • Opportunity Cost and Capability Gaps • Critical Success Factors (CSFs) Strategy: ➢ Prioritised use case Strategy: ➢ Acceleration Roadmap Outcome: Outcome: ➢ Priority use case(s) for Discovery-as-a Service ➢ High level plan for productionisation ➢ Agreed business outcome ➢ Agreed business outcome/value ➢ Business case for productionisation