Banking
Usecases
Baidu does underwriting for consumers with limited credit …
Baidu partners Zest Finance to use their machine learning platform to underwrite credit risk for consumers with little credit history. The platform analyses data such as payment and purchase history, customer support data etc. They also analyse variables such as how customers fill out forms, how they navigate websites, whether they are being honest about reporting income etc.
Scotiabank improves payment collections of credit card customers …
Scotiabank is using deep learning to better manage credit card collections. The platform developed by Dessa identifies risky customers and classifies them according to risk which is calculated by analysing historical data from the bank. It then predicts whether the customer needs a reminder or not, whether there is a chance of delinquency and so on.
Recurly recovers 70% of previously declined card payments …
Recurly has developed a random forest model for its payment processing products which predicts the best day to retry processing a payment for a subscription that has been declined. Recurly claims it can recover 70% of failed payments on average.
Monzo decreased pre-paid card fraud to 0.1% and …
Monzo's machine learning system predicts which online banking and card transactions are potentially fraudulent. Upon detection, extra security is required to verify user identity. This has led to a decrease in pre-paid card fraud to 0.1% and false positive rate to 25%.
Earthport Payment Network reduces false positives of automated …
Earthport implemented ComplyAdvantage's Transaction Monitoring platform to analyse data in real time to identify and report suspicious payment behaviour which may be linked to money laundering or terrorism-financing. Minimising false positives for this behaviour reduces the amount of human intervention needed making the process more efficient.
American Express Australia used machine learning to identify …
American Express has over 100 million credit card customers globally representing over $1 trillion in annual charge volume. The Australian company used advanced data analytics and machine learning to analyse historical transactions along with 115 variables to forecast customer churn. They were able to identify 24% of accounts that would close within four months allowing them to take preventative save …