Record numbers of people are financially excluded. 17.5m people lack access to affordable credit according to Fair4All Finance. There's a shortfall in affordable credit of £3bn. Not having access to affordable credit has been made worse as individuals face high inflation and rising interest rates. NestEgg's data shows those people with lower credit scores use up to 40% of their discretionary spend on high cost credit, reducing their ability to pay **priority bills**. The Financial Conduct Authority (FCA) estimates extra interest **costs households £2bn every year.** Paying high interest makes it harder to afford to live. To make ends meet people borrow. The cycle repeats itself. **Further exclusion** results. Moreover, there's a group of **newly excluded** individuals who've seen expenses rise with inflation and mortgage rates. Legacy loan decisioning systems make it hard for lenders to make quick and fair decisions. They focus too much on Credit Bureau data which fails to take into account the needs of the financially excluded. There's no view of net worth or whether an applicant's financial health is getting better or worse. If an applicant is declined they're not provided with any information about how to improve their chances next time. As a result, **decisions aren't sufficiently aligned with the real circumstances of the borrower nor the risk appetite of the lender.** To ensure a comprehensive view of loan applicants, NestEgg will build a next generation version of its software (Decision 360). Using Open Finance NestEgg's decisioning service will provide: * Insight into credit card behaviours * Details of assets and net worth * Financial health direction indicators * Tips and advice to borrowers using open finance * Machine Learning, improving decisioning algorithms in the long-term. Currently the market is **data siloed.** This project **accelerates** our ability to bring more data sources _together_ for fairer, automated decisioning based on a **more accurate** picture of an applicant. Responsible lenders using Decision360 will: * Increase lending by 30% within six months * Make 180,000 _new_ loans during 2024 * Accept 20% of previously declined loans * Increase revenue by £720m Borrowers with poor credit scores will have saved £600m per year compared to using a high cost creditor. 75% of these borrowers will live in the top 20% most deprived neighbourhoods in England and Wales.

NestEgg connects borrowers to responsible lenders. The NestEgg platform is one place for people to check whether they are eligible to join as well as qualify for loans from responsible lenders across the UK. Once someone has found a suitable loan, NestEgg supports their lender to make a fair credit decision. This process will improve by using AI to combine credit reference and open banking information. This enables lenders to make consistent and fairer decisions in double quick time. But more can be done. Currently the data sources for credit decisioning are in too many different places. A manual review to cross reference the data sources takes too long and is subject to human error. Furthermore, the Financial Conduct Authority found that insufficient credit information leads lenders to make decisions that do not reflect an individual's financial circumstances. As a result, consumers are more likely to have access to credit they can't pay back or denied credit they could afford. This project uses Artificial Intelligence to make decisions based on the interplay between these distinct data sets for responsible, non-profit lenders. Over a five month period 20+ SME lenders will work together using AI to automate credit decisions by cross referencing data sets, with an initial focus on affordability assessment because of the cost of living crisis. The project will evaluate: a) the proportion of lending decisions overridden by humans b) whether AI fairly automates affordability assessment c) how AI can help lenders meet regulatory requirements d) the extent to which sharing information based on these results improves the credit profiles of declined applicants. This initial small scale experiment uses a fraction of the data. It simply isn't possible for a human to consider every way in which data could be used to support automated decisioning. We'll therefore also consider ways to introduce machine learning so we're better able to respond to changes in the market and economy whilst carefully balancing the risks of automation to ensure continued transparency in how decisioning algorithms work. Our lender clients serve a diverse population. We must ensure that AI does not undermine their ability to support those from disadvantaged backgrounds.