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Informing Risk Operations Strategy

Affordability Data in the Consumer Duty Era

Video Interview

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This discussion examines how financial-services organisations can use more granular and timely data to improve affordability assessment, credit risk management, vulnerability identification and collections.

Paul Moran, Head of Analytics at DutifulData, discusses the limitations of traditional credit-bureau, ONS and open-banking information and explains how application-level data can provide greater insight into individual circumstances and emerging financial stress.

The conversation also explores Consumer Duty evidencing, repayment intent, sustainable arrangements, forward-looking affordability and the growing role of AI in creating more personalised customer outcomes.

Find out more about DutiFulData -> Here.

Key Discussion Points

  • The evolution from static credit-bureau information to a broader data ecosystem.
  • The benefits and limitations of open-banking transaction data.
  • The movement from propensity-to-pay decisions towards customer-outcome assessment.
  • Consumer Duty expectations for evidence, vulnerability identification and individual treatment.
  • Limitations in traditional ONS-derived expenditure benchmarks.
  • The use of detailed application data to produce more targeted affordability assessments.
  • Early identification of customers borrowing to cover essential bills.
  • Data-led detection of changing circumstances and potential vulnerability.
  • Differences between portfolio-level arrears trends and stress within individual segments.
  • The importance of customer intent alongside affordability and credit risk.
  • The need for sustainable collections arrangements that customers can maintain.
  • The future role of AI, flexible models and new data sources in personalised customer journeys.

Take Aways

  • Financial-services decisioning is shifting from reliance on a single credit bureau towards combining multiple specialist datasets.
  • Open banking is now widely used, but transaction categorisation remains difficult where payment intermediaries obscure underlying expenditure.
  • Consumer Duty requires firms to focus on customer outcomes and demonstrate how their decisions support those outcomes.
  • Traditional affordability benchmarks may lack the recency and granularity needed to reflect individual circumstances accurately.
  • DutifulData uses credit-application information to assess essential living costs across specific customer characteristics and locations.
  • Large datasets can reveal changes in expenditure, seasonal utility costs and differences in household financial pressure.
  • Credit-application activity may provide early warning that customers are borrowing to meet essential expenditure.
  • Changes in personal circumstances can identify potential vulnerability and trigger more appropriate communications or intervention.
  • Portfolio averages can conceal significant financial stress among non-prime and lower-income customer segments.
  • Customer intent may be as important as affordability when predicting repayment behaviour and sustainable outcomes.
  • Collections arrangements should reflect both immediate affordability and the customer’s likelihood of maintaining payments.
  • Competitive advantage will increasingly depend on flexible data models, incremental improvements, AI and evidence-based innovation.

Innovation

  • Combining products from different data providers to improve specific decision points.
  • Applying a “20 tweaks” approach, where multiple small improvements create a material cumulative benefit.
  • Using self-reported application data to establish granular living-cost benchmarks by geography, income, employment, housing status, marital status and dependants.
  • Monitoring repeated credit applications as an early indicator of financial stress.
  • Tracking changes in customer circumstances throughout the agreement lifecycle.
  • Identifying potential vulnerability through changes in information such as marital status.
  • Forecasting future affordability by overlaying expected cost increases onto current customer data.
  • Using financial education engagement as a potential indicator of repayment intent.
  • Applying AI-enabled digital journeys to engage customers who may feel embarrassed about arrears.
  • Designing adaptable models capable of incorporating new data sources without extensive redevelopment.

Key Statistics

  • Cited ONS affordability data was described as covering approximately 10,500 consumers across around 5,500 households.
  • Prices during the cost-of-living crisis were described as increasing by 18–19%.
  • Furloughed consumers were described as potentially receiving 80% of their salary during COVID-19.
  • A 20% utility-cost increase was used as an example for forward affordability forecasting.
  • A £50 monthly collections payment was used to illustrate the risk of agreeing an unsustainable arrangement.

#DutifulData

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