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Reduce delinquency in card portfolios!

As card issuers target New to Credit (NtC) and New to Bank (NtB) customers to expand the base, it is exposing issuers to higher delinquency and charge off or write-off risk. How can issuers actively manage risk when targeting NtC and NtB customers? 

Strong underwriting is the beginning of good risk management, but not the end. Transaction monitoring, repayment analytics and early warning systems are essential to manage risk in #ntc and #ntb portfolios. The need for real time and ongoing #customeranalytics to ensure maximization of portfolio profitability is key.

Probability of default in #creditcards are impacted by a whole range of parameters, while credit score is what comes to mind, this is only the beginning. An active analysis of card utilization, repayment history, length of credit history, economic conditions can play a significant role in predicting card portfolio profitability. 

Even parameters like job change, locality and age can play a significant role in predicting probability of default. #machinelearning models are well suited for this use case. 

The infrastructure needed to leverage ML includes:

·   Store and retrieve large data sets: Transactions, master data, repayments, customer support issues, economic conditions

·   Feature engineering: A process of selecting, transforming and computing relevant variables, necessary for predictive prowess

·   Realtime features: Computing features based on recent data in last 15 mins or 24 hours

·   Model management: An ML model needs validation, auditing and governance with explainability as a guiding principle

Leveraging ML infrastructure can help issuers obtain early warning based on card usage, repayment patterns, change in MCC pattern etc. Moreover, predictive models built through ML continually evolve with incoming data, adapting to changing consumer behaviors and economic conditions. This dynamic approach optimizes risk management strategies, reducing the likelihood of delinquency while maximizing customer satisfaction.

Through meticulous development, validation, and ongoing refinement, these models have the potential to significantly improve the accuracy of delinquency prediction. 

In essence, embracing ML for delinquency prediction in NTC and NTB credit cards equips credit card businesses with a competitive edge. The combination of accurate risk evaluation, dynamic adaptability, and informed decision-making fosters sustainable growth and profitability, solidifying the industry's position in the ever-evolving financial landscape

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