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Case Study: Anonymous Lender

Increasing profitable marketplace lending with Affordability Engine

A major UK personal lender wanted to improve affordability assessments in their high-volume, broker-led marketplace journey. Their reliance on generic ONS data for expenditure modelling and incomplete CATO-based income verification was causing excessive affordability-based declines and manual referrals. 

The lender tested how Infact's Affordability Engine – a frictionless, responsive, performant API built to cater for marketplace volumes – would impact their lending performance relative to their incumbent solution. 

The data showed a transformed pre-approval process, achieving a potential 4.23% increase in lending. 

Client: A major UK personal lender 

Objective: To achieve at least a 1% uplift in the affordability and income verification pass rates 

Background: The lender’s existing pre-approval process was multi-stage. At each stage, they anticipated drop-off as their hard-fail rules, then credit checks declined applicants. 

Funnel Design Number of Consumers (1)

The set up

The lender did not have confidence in the ONS-data used in their expenditure models and their existing model was causing more affordability-based declines than they felt was reasonable based on economic research.  

This was a frustration as it meant the lender was declining applicants who had good creditworthiness and excluding people based on poor availability of affordability data to drive a responsible lending strategy.  

In addition to this, they had a large percentage of applicants they could not verify income for using their CATO-based models, resulting in larger manual referrals. 

Frame 3180

Funnel Design Conversion Rate

What did Infact do?

Infact's Affordability Engine was tested against the incumbent solution to assess the potential impact on the lender's pre-approval decisioning process, and the net reduction in affordability-based declines by: 

  1. Replacing the in-house ONS-based expenditure model with expenditure components.  
  2. Supplementing their existing CATO strategy with Infact’s income components, giving 100% coverage of applicants. 
  3. Creating a swap set inference to assess the positive swap-in vs. negative swap-out impact.

The solution proposed offered frictionless delivery of personalised income and expenditure metrics on 100% of applicants, giving the lender more nuanced affordability data and a higher degree of configurability and control over their model inputs.  

The lender also benefits from continuous model refinement ensuring output accurately reflects the current economic environment, while offering full explainability that improves transparency for customers, auditors, and regulators. Additionally, the solution reduces the cost per acquisition of new customers by driving down OPEX costs. 

Measured impact

After the retro, analysis showed that the following uplift could be expected by implementing Infact’s Affordability Engine: 

Frame 3180 (4)

Qualitative impact

In addition to the quantitative impact of the solution, the implementation of the Affordability Engine also offered the potential for: 

Diversification

Reducing the lender’s reliance on an increasingly unreliable data source. 

Confidence

Greater confidence in responsible lending decisions thanks to transparent, explainable expenditure insights, based on affordability information that is real-time, accurate and relevant – making Affordability Engine very agile. 

Access

Improving access to competitive rates for non-average customers previously disadvantaged by public data. 

Speed

Faster decisions with reduced friction in the customer journey from application to approval. 

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