In 2015 JPMorgan Chase decided to use data to gain insight on how the income and spending habits of their customers in the US fluctuate in a year

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In 2015 JPMorgan Chase decided to use data to gain insight on how the income and spending habits of their customers in the US fluctuate in a year. They are also using business intelligence for fraud detection. Banks lose millions each year due to fraud. They started using data mining techniques to identify fraudulent transactions from legitimate transactions. They used two mechanisms to detect fraud. The first one uses a data warehouse that is maintained by a third party and identifies fraudulent information using data mining. The other method is the banks identify fraud information by comparing the information with their databases. Data mining uses specific patterns and behavior of individual customers from the big data and detects fraud through the pattern.
JPMorgan also uses BI to improve the customer experience (Kognitio, 2018). One way to improve customer satisfaction is knowing what customers think about their business providers. BI tools can provide reports of customer feedbacks on various internet platforms. Other ways are trend identification, customer engagement, and more employee engagement. JPMorgan uses business intelligence to analyze data collected from nearly 30 million customers. Business leaders will make informed choices about the banks’ economic policy decisions. JPMorgan uses the Apache Hadoop framework to manage big data and to manage their exponentially growing data size. They also use data analytics tools to drive their insights from a large amount of data.
JPMorgan implemented a software called COIN that interprets commercial-loan agreements. The task took lawyers 360,000 hours annually before the software went live in June 2015 (Son, 2017). The program helped JPMorgan cut down on loan-servicing mistakes, which mostly occur due to human error.
Recently JPMorgan is working on how to “learn more from less data”. They are focusing on reducing the need for data to build models (JPMorgan, 2019). They are planning to use a labeled dataset, so machine learning can be applied to the data so that new, unlabeled data can be presented to the model.

JPMorgan. (2019, October 10). Learning More From Less Data With Active Learning. Retrieved From.

https://www.jpmorgan.com/global/technology/blog/active-learning

Son, H. ( 2017, February 28 ). JPMorgan software does in seconds what took lawyers 360,000 hours. Retrieved From. 

https://www.independent.co.uk/news/business/news/jp-morgan-software-lawyers-coin-contract-intelligence-parsing-financial-deals-seconds-legal-working-hours-360000-a7603256.html

 

Kognitio. (2018. August 30). JPMorgan Chase turns to big data for economy monitoring. Retrieved From. https://kognitio.com/blog/jpmorgan-chase-turns-to-big-data-for-economy-monitoring/