Benefits of machine learning for issuers
Machine learning can help issuers make better sense of the exponentially increasing data (both transactional and behavioural) and more accurately predict spending behaviour. Ultimately, this can lead to better customized experiences and drive top line and bottom line growth. Let’s now look at some specific applications of machine learning in the credit card value chain.
Risk assessment: Timely, accurate assessment of credit worthiness is vital as credit card applicants dislike the uncertainty around their applications. Conventional models of risk assessment rely on data sources such as existing debt burden and payment histories and deploy linear statistical modelling together with human judgement. However, with the growing volume and variety of data, machine learning algorithms can be more useful, enabling lenders to dynamically include additional sources such as current wealth, property ownership, insurance claims, social network profile and other personal indicators, thereby, creating a more holistic profile of the borrower. This approach also helps includes borrowers with weak credit scores. Already, Mumbai based start-ups, Rubique and CreditVidya leverage AI to innovatively assess credit worthiness, speed up decision making and expand reach for banks.
Effective targeting and retention: Consumers today have a problem of too many credit card options to choose from. Issuers can feed first and third party data to machine learning systems to come up with a target audience profile and the relevant channels to share most appropriate card options. The same principle can also be applied to timely target disgruntled customers and reduce churn.
Customer care: The advancements in machine learning are helping Natural Language Processing (NLP) applications such as chatbots become more contextual. Through chatbots, credit card issuers can ensure round the clock assistance across all stages of customer interaction including product selection, on-boarding, payments and usage. Smarter chatbots can also learn to detect irate human behaviour and accordingly escalate queries to customer care executives.
Personalized rewards: Rewards are most important reasons for keeping a credit card on top of the wallet. Machine learning can help issuing banks get a true understanding of each customer and follow it up with tailored reward experiences. American Express for example uses machine learning to recommend restaurants to its card members. HSBC in the US has experimented with predicting how customers are likely to use their reward points and accordingly market its reward programs more effectively.
Contracting fraud: Issuers bore more than 70% of the losses arising from global card fraud in 2016. Machine learning systems feeding on historical transaction and behaviour data can help identify patterns associated with fraud. This gives issuers a better chance to counter the ever evolving sophistication of cyber-attacks, compared to traditional rule based techniques.
New revenue opportunities: Machine learning capabilities can help banks open-up new monetization avenues. “Predictive spending insights”, built on transaction and third-party data is one such example. Case in point is American Express’s AmexAdvance, that combines transactional and third-party data to brand marketers and media partners deliver personalization services.