Corpus ID: 43996674

Optimizing Bank Overdraft Fees with Big Data

@inproceedings{Liu2016OptimizingBO,
  title={Optimizing Bank Overdraft Fees with Big Data},
  author={X. Liu and A. Montgomery and Kannan Srinivasan},
  year={2016}
}
In 2012, consumers paid $32 billion in overdraft fees, representing the single largest source of revenue for banks from demand deposit accounts during this period. Owing to consumer attrition caused by overdraft fees and potential government regulations to reform these fees, financial institutions have become motivated to investigate their overdraft fee structures. Banks need to balance the revenue generated from overdraft fees with consumer dissatisfaction and potential churn caused by these… Expand
1 Citations
- 1-Reshaping Bank Branch Networks due to Mobile Banking
  • PDF

References

SHOWING 1-10 OF 45 REFERENCES
Limited and Varying Consumer Attention: Evidence from Shocks to the Salience of Bank Overdraft Fees
  • 203
  • Highly Influential
  • PDF
Consumer Dynamic Usage Allocation and Learning Under Multipart Tariffs
  • 16
  • PDF
The Demand for Risky Assets
  • 1,160
Consumer Inattention and Bill-Shock Regulation
  • 113
  • PDF
Investor attention , overconfidence and category learning
  • Lin Penga, Wei Xiongb
  • 2006
  • 558
  • PDF
Shrouded Attributes and Information Suppression in Competitive Markets
  • 103
  • PDF
...
1
2
3
4
5
...