Banking

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Banking

How data science is helping traditional banking thrive.

 

Data science in Banking

Banks have a natural affinity and a crescent need for Data Science and Artificial Intelligence applications. According to Forbes, 65% of senior financial management expects positive changes from the use of AI in financial services.

The impact from these applications vary from cost reductions, ability to reply faster and more efficiently to customers, decrease in down-times, etc.

This recent pandemic has brought to our attention that some of these technologies are more crucial than ever, and companies that want to thrive in a post-COVID era must embrace them.

Those technologies are crucial both for the immediate response and in the long term, as banks and society shift to a new normal were the data and technology is even more important.

Update models to deal with new reality

With the drastic shifts in the economy, previous risk models will not be effective anymore. Therefore, the adaptation and perhaps the creation of the new models that can support data-driven decisions are essential.

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FAQ

  • Are the results explainable?

    Some more "white-box" models can be applied such as decision trees or random forests, providing the banks with an automated but still auditable process.
  • What if contexts change, like due to COVID-19?

    Machine learning techniques provide the ability to adapt to new information and context through continuous training and performance monitoring.
  • How can models be aware of context?

    Geographic, demographic, network, customer profile, and previous behaviors can provide valuable insight into the AI, features that a human doesn't have time to check.
  • Is it possible to screen through large volumes of data?

    The vast amount of data available allows for the training of machine learning models that will quickly screen through the millions of transactions in seconds.

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