Akash JalilCID: 01248714Andrew Jennings ScholarshipThe idea for generating models that can implement a credit score system for consumer lending requires fascinating statistical methodology that is both complex and evolving. I am most interested in the use of different statistical tools within credit scoring, some of the methods I have looked into range from implementing logistic regression, which is most used within the UK, to Markov chain models to predict changes within time and consumer behaviour. I have further looked into how Bayesian inference could be used in helping create credit scores for first time borrowers or updating credit scores for existing borrowers.

The challenging aspect of credit scoring is the prediction of future behaviour of the consumers; these predictions are of vital importance due to their effects in other areas. Since the mid-1980s the lending to consumers has exceeded that to companies, nevertheless with the mortgage crisis of 2007 and the resulting credit crunch it has now become prevalent the impact such lending can have of the financial sector and thus how under researched consumer lending is. The amount of variables that contribute to a credit score can be exceptionally large which causes problems when trying to select a model. Big models tend to find features that are specific to the data being used and cannot generalise relationships between the data, this results in poor predictions and thus improper lending to an inadequate consumer. I would like to work towards creating and developing existing models that can aid the credit scoring system and help minimise these problems. The MSc Statistics course will provide me with an insight into how to develop these models.

Furthermore I have read around the existing methods currently used by the financial sector. The most popular technique used within the UK retail banking sector is a frequentist technique of using logistic regression to predict a binary output of bad debt or no bad debt. The method allows for the analysis of datasets with one or more independent variables, this is an advantage as there are many governing factors that make up a credit score and being able to identify relationships between them will allow for an accurate credit rating. As credit scores are subject to change, one variable that might lead to this can be the behaviour of a consumer. A behavioural model can be used to monitor a person’s credit usage and their behaviour associated with this. The reasons for such a model are so that it can be used for risk assessment/ management and to make operational and marketing decisions. The way in which this can be conducted is by using Markov chain models. Markov chains are a topic that I most enjoy in statistics and their use in credit scoring is one reason why I am interested in knowing more about retail credit scores. This method allows for modelling changes in a customer’s account over time, it can be used to model the number of periods of delinquencies (the missing of repayments) a customer may incur or it can further used to model changes in behavioural score.

Bayesian inference is module that I have been undertaking in my final term of my undergraduate and the usefulness and depth of the module can be modelled in many situation and circumstances. Its application in credit scoring is another reason why I became interested in this area. The recorded data and history of a customer’s actions with their credit can be used to produce or update their prior probability; we are able to use this to evaluate the posterior. The posterior represents the updated beliefs about the customer which can then be used to evaluate whether this customer should be given a loan or deemed too risky. The banking sector is always looking at ways it can expand on the credit scoring system to make it a more accurate representation of customer future behaviour, thus the importance of statistical modelling in this area will always remain uniform.