Professional services for finance and banking
We build predictive behavioural and application scorecards to estimate propensity for:
- Paying back arrears
Deploy scorecards in real time to drive informed decisions for origination, pricing and marketing recommendations throughout the customer journey.
Financial lenders now use advanced customer data enhancement to reduce lending risk. Using statistical and machine learning techniques, we analyse the available data and reduce it down to a single value known as a credit score representing the lending risk for each individual record.
Credit scoring is a form of Artificial Intelligence, based on predictive modelling, that assesses the likelihood of a customer defaulting on a credit obligation, becoming delinquent or insolvent. A high credit score indicates to the lender a high confidence in that customer’s creditworthiness. Once we have built a predictive model, the model “learns” from key inputs such as customer historical data alongside peer group data and other data to predict the probability of that customer displaying a defined future behaviour.
We design models for scorecards that describe propensity for pre-defined desirable or undesirable behaviours based on risk within an existing customer base. For example, a behavioural score on default propensity informs decisions relating to account management such as credit limit, over-limit management, new products and similar.
- Standardise customer credit scores by score bands
- Reduce expected default rate
- Increase auto approve rates for lowest risk customers