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: Identify and protect vulnerable customers, preventing potential debt Accurately predict what debt will or won’t be resolved Effectively plan for debt to mitigate risk in times of uncertainty

Key y Concer erns s in Debt

Op Optimi misi sing ng Debt t with AI AI

PROV OVISI ISION – Forecast bad debt

more precisely and optimise annual financial provision

PREVEN ENTIO TION – Predict customers at

risk of debt, implement strategies and actions to prevent debt and mitigate

• perational risk

SIMULATIO ION - Multi-dimensional

models and accurate, data-driven scenario modelling

COLLECTION ION - Assess the

likelihood to pay and Implement

• ptimum strategy and channel for

collections

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CASE STUDY

The Customer:

AI in Action: Intelligent Debt Collection in Utilities

The Sector: Utilities

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 The Requirement: Driving customer enhancements in debt resolution

AI in Debt Overview

Building the ‘AI in Debt’ story

Customer Journey: Accelerated path to production

Customer Case Study: Northumbrian Water Group (NWG)

Opportunity Roadmap and Defining Success

Inawisdom + Business Stakeholders

• Business Priorities/KPIs
• Ideation’ - The Business Opportunities
• Speed to value and quick wins

Strategy: ➢ Prioritised use case Outcome: ➢ Priority use case(s) for Discovery-as-a Service ➢ Agreed business outcome/value ➢ Business case for productionisation

Productisation & Acceleration Roadmap

Business, IT, DevOps, DataOps, Data Science

• Review existing AI/ML Roadmap/Models, Scope

production MVP

• Opportunity Cost and Capability Gaps
• Critical Success Factors (CSFs)

Strategy: ➢ Acceleration Roadmap Outcome: ➢ High level plan for productionisation ➢ Agreed business outcome

Next st step: : Inawi wisd sdom Disc scovery very Works kshop hop

Getting g Started ted AI/M /ML L – Debt t Optimisat ation

• n

Move to Prod

• duc

uctio tion n – Debt Optimisat ation

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