University of Salford Manchester

Salford data-mining knowledge helps not-for-profit lending company

Tuesday 23 October 2012
The University of Salford has been working with East Lancashire Moneyline to help people on low incomes who find it impossible to secure affordable loans access finance more readily.

Following a two-year Knowledge Transfer Partnership (KTP) funded with the help of the ESRC and EPSRC, lending by East Lancashire Moneyline (IPS) Ltd, a not for profit industrial and provident society that provides access to credit, savings and advice, primarily to low income individuals, is set to increase by 50 per cent from 2011 to 2012. Total lending by East Lancashire Moneyline to people on low incomes will top £10 million this year.

“Benefit claimants and others on low incomes typically have difficulty in obtaining loans because they fall outside the lending criteria of high street banks and building societies,” explains Diane Burridge, Chief Financial Officer of Moneyline. “Our typical customer relies wholly or partially on benefits and has no alternative but to manage their finances from week to week, so when an unexpected item of expenditure occurs they often have little or no disposable income or savings to meet that need.”

To help tackle the financial exclusion experienced by low income groups as well as reduce the risk of bad debts, researchers from the University of Salford worked in partnership with Moneyline to develop a consistent and objective framework of risk assessment for use in the process of loan approval.

Professor Sunil Vadera, from the School of Computing, Science and Engineering at the University of Salford explains: “The main aim of the KTP was to create a framework to assist loan approval at East Lancashire Moneyline by applying data mining methods to a real world problem where success would have a positive impact on improving financial inclusion.”

Data-mining is the process of detecting patterns in data which can be used to inform decision-making models. "For this project we were able to combine our expertise in Data Mining, with Professor Karl Dayson's knowledge of microfinance, and Dr Jia Wu's experience of data analysis to understand the socio-economic factors and personal circumstances that can influence loan approval for those on low incomes," Professor Vadera continues. Identifying patterns of data in, for example, cash machine use enabled researchers to create a web-based Credit Risk Assessment Tool (CRET) for use in the objective assessment of loan applications.

Using this tool has enabled East Lancashire Moneyline to move from a relatively simple and informal means of assessing loan applications to a consistent framework for risk management.

“Through the project there has been a significant improvement in the collection, storage and analysis of client data,” says Diane Burridge. “There is also increased appreciation, by the underwriting staff, that social factors can have as significant impact on the client’s ability to pay. The KTP has helped us gain the confidence to move our business into more geographical areas and, as a result, more people now have access to affordable finance than would previously have been possible.”

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