How Predictive Analytics Are Transforming Customer Intelligence Within Banks

6/29/2017

Key takeaways from the predictive analytics webinar

  • Evolutions in machine learning and artificial intelligence have drastically improved business intelligence and predictive analytics capabilities.
  • More and more banks are embracing predictive analytics to improve customer intelligence.
  • Most organizations are currently in the early stages of implementing advanced analytics.

Embracing advanced analytics in the banking industry

As banks become more comfortable with how machine learning works, and the benefits it can provide, we are seeing more institutions making significant investments to implement advanced bank analytics solutions. Banks that have already incorporated this technology are now able to make smarter business decisions, increase their profitability, and improve the overall customer experience. In this on-demand webinar, Chris Sifter, Managing Director of Banking Information Management and Pani Koduru, a Senior Manager of Banking Information Management, discuss using machine learning and predictive analytics in the area of customer intelligence.

The evolution of business intelligence and advanced analytics

Until recently, business intelligence and advanced analytics has meant collecting large amounts of unstructured data and asking a team of people to analyze and interpret it. Now that we have the ability to layer in machine learning and artificial intelligence, we have been able to better organize and analyze large volumes of data with insights being presented to us automatically. Today, most banks have begun, or are beginning, to launch predictive analytics projects to better mine their data with the goal of improving their customer intelligence.

Advanced analytics in banking

While organizations within the banking industry know the importance of using innovative technology to stay competitive, most organizations will say that they are still in the early stages of implementing advanced analytics processes. Some of the areas that banks are focusing on as they launch these projects are:

  • Customer Alignment
  • Marketing and Sales
  • Asset Management
  • Operations
  • Workforce
  • Risk Management

Using technology to build customer intelligence

Customer relationships are an important part of the banking industry. Knowing what customers are looking for will help to retain and increase the customer base. It’s important that organizations invest in advanced analytics to help predict customer behavior to be sure resources are allocated appropriately. Some key customer intelligence information that needs to be obtained in order to get the most out advanced analytics includes:

  • Lifestyle Changes
  • Bank Likes/Dislikes
  • Bank Interaction Data
  • Interaction Channels
  • Transaction Information

Without recent improvements in technology, it would be impossible to gather the above information, categorize it, and analyze it in a meaningful way. With machine learning, it is possible to do all of those things and put the insights from that data into action to ensure the best possible customer experience.

Crowe banking analytics and data insights

Machine learning technologies and predictive analytics are changing how banks turn data into detailed, actionable insights about their customers. Contact Crowe today to learn more about how your company can put predictive banking analytics for customer intelligence to use with our innovative machine learning and artificial intelligence solutions.