Prasanna Dhore, Chief Data Officer at Fiserv, discusses credit challenges for 'invisible' individuals and the use of AI to improve credit access. Topics include predicting creditworthiness with new data sources like rental payments, enhancing financial processes with technology, and exploring data insights on credit behavior and predictions.
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Quick takeaways
AI can predict creditworthiness for 'invisible' individuals using new data sources like cell phone records.
Utilizing alternative data, such as rental payments, reduces information asymmetry in credit evaluation.
Deep dives
Credit Challenges for the 'Invisible' Category
AI can help address credit challenges faced by individuals in the 'invisible' category who lack credit history, including recent immigrants and young adults starting their financial journey. These individuals, comprising about 30 million in the U.S., struggle to build credit history due to the catch-22 situation of needing credit to access credit. By leveraging new data sources like cell phone records and rental payments, AI can predict creditworthiness and offer opportunities to those otherwise excluded from the credit ecosystem.
Importance of Alternative Data in Credit Evaluation
Information asymmetry in credit evaluation can be reduced by utilizing alternative data beyond traditional credit history, such as rental payments and cell phone bill payments. These additional data points provide insights into financial behavior and risk differentiation, offering a more comprehensive assessment of creditworthiness. By incorporating alternate data and adopting advanced analytics techniques like machine learning, lenders can improve risk assessment and potentially extend credit to underserved populations.
Enhancing Efficiency and Responsiveness in Credit Operations
Improving the speed and accuracy of credit evaluations is crucial, especially in times of economic uncertainty like during COVID-19. The traditional credit scoring system's latency in reflecting current financial situations highlights the need for real-time data and predictive analytics to assess both the ability and willingness to repay loans. By leveraging technology advancements and alternative data sources, financial institutions can enhance credit operations, provide faster access to credit, and ensure a more inclusive and responsive financial system.
Today’s guest is Prasanna Dhore, Chief Data Officer at Fiserv and President of Data Commerce Solutions. Prasanna joins us on the program today to discuss credit challenges for those in the “invisible” category, including lack of awareness and information asymmetry, and what AI can do to bring these folks in from the cold. Later in the episode, Prasanna touches on the essentials for predicting creditworthiness using new data sources like cell phone records and rental payments. To discover more AI use cases, best practice guides, white papers, frameworks, and more, join Emerj Plus at emerj.com/p1.
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