Today’s bank lending process is characterised by uncertainty and volatility. At Finalyse, we help banks quantify their risks. We see credit scoring as a vital risk management tool, revealing untapped opportunities to create a healthy loan portfolio.
However, the complex regulatory landscape, the changes in business strategy, new technologies, and the uncertainty of the overall economy lead to the need of building robust scoring models. Quality scoring models help controlling credit risk, identifying opportunities, and lead to a new way of doing business.
A credit score is a numeric value that represents the creditworthiness of the customers. Credit scoring models are categorised into two different types: application scoring and behavioural scoring. Application scoring attempts to predict a customer’s default risk at the time an application for credit is made, based on information such as applicant demographics and credit bureau records. Behavioural scoring assesses the risk of existing customers based on their recent accounting transactions, recent financial information, including repayment performance, delinquencies, credit bureau data, and their overall relationship with the bank. By identifying the riskiest clients, the bank can take preventive actions to hedge itself against future potential loss.
Machine Learning is a method of teaching computers to parse data, learn from it, and then make a decision or prediction regarding new data. Machine Learning overlaps with statistical learning as both attempt to find and learn from patterns and trends within large datasets to make predictions.
This article discusses the benefits of applying advanced analytics in the development of behavioural scoring models. It investigates how Machine Learning techniques can be used to model the behavioural scores of consumers in each step of the development and model assessment. Some concerns regarding the usage of Machine Learning in behavioural scoring model are addressed.