Faster, better underwriting
Banks have always had the opportunity to use their customer’s own transaction data to inform their lending decisions. The problem is that this analysis has often been time-consuming and laborious. With automated income and expense categorisation lenders can streamline their lending processes and create a more complete picture of what a customer can afford at every stage of the credit lifecycle.
This is why Experian Digital, powered by Experian Look Who’s Charging, provides automated income and expense categorisation that helps underwriters see the big picture faster. With it, lenders can enrich and categorise transactions in a customer’s bank and credit card accounts into over 300 different categories of expenditure and over 26 unique income categories to provide granular detail. This data can then be easily summarised into a set of insights around income, expenditure, and credit behaviour. It helps underwriters answer key questions fast:
- Can the customer afford to repay their new and existing obligations?
- How volatile is the customer’s income?
- Are there any red flags in their transaction history?
- Has anything significant changed recently in income or expenditure?
- …and many more
Underwriting challenges and how automated income and expense categorisation helps
Some of the key challenges facing underwriters today include:
- Pressure to significantly reduce costs while maintaining or improving quality
- In general underwriters are unsatisfied with underwriting platforms
- Evolving customer needs and expectations, especially the desire to be wholly digital
Income and expense categorisation helps to address these problems by providing fast, robust insights to make things simpler for consumers and quicker for underwriters.
Jordan Harris, Head of Innovation for Experian Digital adds:
“Lenders who can place useful information instead of confusing data in front of their underwriters are reaping the benefits of faster, consistent decisions and improved underwriter experience”
How does it work?
It is API driven so the solution can be seamlessly integrated with existing digital applications. With a comprehensive dataset of over 96 million transactions linked to a database of 1 million merchants, and over 1 billion transactions processed a week with a match rate of over 98% and a response time of less than 0.3 seconds, underwriters get accurate and fast insights.
With over 300 customisable subcategories for refined spend analytics, as well as the ability to provide custom categories including Household Expenditure Measure (HEM), and more than 26 different income categories, underwriters can get a detailed picture of their customers’ financial circumstances and resilience. This helps them make more informed affordability decisions across the customer lifecycle.
What can it show underwriters?
Automated income and expense categorisation offers a detailed understanding of:
- True income, its sources and stability
- Regular savings contributions
- Level of spending on basic household essentials, mortgages or rent
- High risk transactions such as dishonours, late fees, payday loan payments, and excessive Buy Now Pay Later (BNPL) usage
- Gambling-to-income ratios
- Engagement with debt-collection agencies
- …and much more
- Extending more credit responsibly – Using automated income and expense categorisation, lenders can accept more applications, offer more products to existing customers, or extend their limits responsibly.
- A faster, seamless credit application process – Credit application processes can be hindered by the inefficiency of manual income and expense declarations. It’s burdensome for customers to collate this information and time consuming for underwriters to assess it. Automated income and expense categorisation saves customers time with pre-filled information and reduces application drop off.
- More accurate credit risk management – Automated income and expense categorisation can help make decision models more precise, enabling lenders to make better quality decisions.
- Protecting vulnerable customers – The technology can be used to identify and protect vulnerable customers by revealing important indicators in their behaviour. This may include frequent use of payday loans, a disappearance of the consumers income source, or a rise in payment dishonours. This helps lenders easily identify borrowers showing signs of stress and proactively provide the support they need.
- Validate income and assess affordability – For gig-economy workers for example, income that may come from multiple jobs or seasonal work, automated income and expense categorisation can be used to validate income and properly assess what they can afford, opening up access to credit.
“It can take well over 25 minutes per application to understand, review and comb through a consumer’s unstructured transactional data. Automating the categorisation of this data can save nearly all of this time.”
Simplifying and accelerating the analysis of complex data and using this to reveal new insight, better decisions can be made for consumers and lenders alike.
To learn more about how we can help, please get in touch with us using the form below.