Motor Finance - Navigating Creditworthiness
The motor finance industry is under intense scrutiny, with the FCA’s ongoing Borrowers in Financial Difficulty (BiFD) work and the regulator shining a spotlight on commission models. But is affordability the next big target?
Our work with firms reveals a surge in regulatory focus on creditworthiness frameworks within the motor finance sector. From what we are seeing, the regulator is not holding back, taking decisive action where controls fall short.
But why are firms still missing the mark?
This blog explores some key areas where motor finance firms’ practices are falling short, resulting in an increased risk of customer harm.
1. Failing to tailor creditworthiness assessments to the target market
Due to the non-prescriptive nature of the creditworthiness rules, firms have the flexibility to align their lending approach to their target market. The introduction of the Consumer Duty, however, necessitates firms to identify groups of customers, within their target market, who are particularly susceptible to harm.
Common Issues:
One-Size-Fits-All Approach: Applying a uniform approach to assessing creditworthiness without considering the diverse risk characteristics of different customer groups within the target market.
Undefined Risk Rating Systems: Creating risk rating methodologies or tiering systems without clearly defining or documenting the characteristics that justify the risk rating and/or the lending parameters.
Automated Credit Risk Scorecards: Implementing automated systems for credit risk scoring without adequate documentation, understanding, or clear definitions of their functionality, output, and quality assurance controls.
Inadequate disposable income buffers: Adopting thresholds for disposable income that leave customers with inadequate disposable income to withstand unexpected expenditure or income shocks.
Questions firms should be asking themselves:
Has the firm taken steps to identify and assess the risk profiles of customer groups within its target market? Are these clearly defined and documented?
Does the firm apply more robust creditworthiness controls to customers displaying a higher affordability or sustainability risk e.g. introducing loan caps for younger borrowers with limited credit experience or a higher disposable income buffer for customers posing a higher affordability risk?
Are the robust creditworthiness controls and an explanation of how they are applied to each of the risk cohorts clearly defined and documented?
Does the firm have in place, robust QA processes to assess the output of its automated credit risk scoring system to provide assurance that the scorecard is working as intended?
Does the firm have in place an effective suite of hard stop rules which are tailored to the risks prevalent within their target market?
2. Complex methodologies for modelling customer expenditure
Due to the motor finance industry relying primarily on broker networks to introduce customers, firms lack the ability to shape the collation of customer information, particularly declared expenditure, at application stage. In the absence of customer derived expenditure values, many firms have developed a methodology for modelling expenditure. There is an inherent risk however, that modelled expenditure is underestimated resulting in the loan being unaffordable from the outset.
Common issues:
Quality of life expenditure: Failing to consider discretionary expenditure for quality-of-life expenses or the costs associated with owning a vehicle within the creditworthiness assessment.
Deductions made to modelled expenditure data: Making deductions to modelled expenses based on an assumed contribution from another person without adequate validation.
Open Banking data: Failing to utilise Open Banking data held on file for income verification which evidences that customer expenditure differs materially to the modelled values.
Modelling monthly credit commitments: Utilising modelled data for monthly credit commitment repayments when the actual monthly commitment is included within the customer's credit file e.g. Buy Now Pay Later.
Tailoring expenditure to target market: Failure to stratify data used for modelling expenditure to ensure it is representative of the target market, e.g. by factors such as customer type, geographical location, household composition, number of dependents or income quintile.
Questions firms should be asking themselves:
Is the underlying engineered methodology adopted by the firm to model expenditure unnecessarily complex? Can the firm clearly articulate the methodology and rationalise the calculations made are applicable to their target market?
How does the firm ensure that modelled expenditure data keeps up to date with inflation or other rising costs e.g. utilities during the cost-of-living crisis?
How does the firm utilise open banking data when available? What approach is taken where the Open Banking data suggests significantly higher expenditure than the modelled data suggests?
How does the firm calculate credit commitment expenditure, particularly for revolving credit items e.g. overdrafts and credit cards?
How does the firm stratify its modelled expenditure data to align to its target market? And what measures does the firm adopt to ensure the stratified data continues to align to target market expenditure on an ongoing basis?
3. Contribution towards household expenses by another adult within the household
The rules pertaining to creditworthiness provide firms with the ability to include the income received by another person within its creditworthiness assessment for sole applicants. However, this income can only be included where:
It is reasonable to expect this income to be available to the customer to make loan repayments; and
The firm has also considered the non-discretionary expenditure relating to that person.
In the absence of adequate controls around the use of another person’s income, there is an inherent risk that a customer is unable to afford a loan without the income of another person. It is also important to note that where this information is obtained directly from a customer, it is subject to optimism bias particularly from higher risk customers who are potentially unable to afford the loan based on their income alone.
Common issues:
Unverified income: Assuming another person’s income contribution to the household, based on the customer’s marital status or joint flags within their credit report.
Reliability, sustainability and availability: Failure to assess the reliability, sustainability and availability of another person’s income towards the customer’s monthly repayments.
Expenditure associated with the other person is not considered: Failure to consider the expenditure relevant only to the other person whose income is factored into the creditworthiness assessment.
Questions firms should be asking themselves:
What steps does the firm take to determine the reliability and sustainability of the income belonging to another person? How is this considered in the context of customers financial standing?
What steps does the firm take to ensure that the expenditure belonging only to the other person is also considered?
What validation does the firm complete to ensure the assumed contribution towards non-discretionary expenditure by another person is not resulting in the customers expenditure being underestimated?
What additional controls does the firm adopt for higher risk customers to ensure that the loan is affordable and sustainable for the customer based on their income alone? How does the firm test this through their outcomes testing?
4. Failing to utilise management information to identify instances of potential harm
Effective management information (MI) that enables firms to identify early indicators of foreseeable customer harm and instances of different customer outcomes being received by different groups of customers is pivotal to the successful embeddedness of the Consumer Duty.
Common issues:
Existing MI: Failure to apply a customer outcomes lens to existing suites of MI which are not primarily focused on customer outcomes but can provide useful early indicators of customer harm, e.g. MI used to assess whether the firm is operating within its credit risk appetite.
Different outcomes for different customer groups: Many firms are still not segmenting their MI to monitor different outcomes received by different customer groups resulting in indicators of potential harm for a certain cohort being diluted by the overall MI.
Green = good: Failing to enhance MI, thresholds or limits where KPIs continuously demonstrate the firm is operating within its risk appetite which suggests that the MI is not adding value in identifying instances of potential customer harm.
Questions firms should be asking themselves:
What steps has the firm taken to apply a customer outcome lens to its existing suite of MI? Are customer outcomes discussed at forums whereby customer outcomes may not be the primary focus e.g. credit risk forum?
How does the suite of MI in place allow the firm to consider the different outcomes received by different cohorts of customers?
What steps is the firm taking to continually evolve its suite of MI to identify instances of foreseeable customer harm?
What investigatory controls does the firm adopt to assess and monitor instances whereby the MI suggests customer harm? How is this articulated within the supporting commentary within the MI reports?
How we can help
Want to learn more about the broader issues we are seeing across the motor finance sector or how Avyse can support your firm? Contact our Compliance Team at ComplianceTeam@avyse.co.uk