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AI is advancing rapidly, and the world has taken notice. As artificial intelligence tools completely change the landscape of manufacturing, writing, video production, stock trading, and just about every sector of the economy, companies are working quickly to get in front of this technological revolution.
That is certainly clear in the case of financial institutions. Faced with many hardships over the past few decades — many of which were exacerbated or even created by the great financial problems of 2008 —, institutions are looking for better ways to automate, generate data, and ultimately grow revenue.
This brings us to the story of ElectrifAi and its recent endeavors with an unnamed financial institution somewhere in Asia.
According to ElectrifAi reports, the AI researchers worked with a financial institution to find new ways to catalog and utilize user data in order to recover lost revenue. The institution serves more than 150 million customers across Asia, and it provides insurance, banking, and investment resources to those customers.
The institution was searching for ways to integrate finance and internet strategy into existing channels in search of better revenue streams. The plan was to view each individual customer across diverse business units to find ways to personalize upsell and cross-sell endeavors.
ElectrifAi took on the challenge by using AI consolidation tools. These tools proved able to rapidly consolidate data for the millions of customers. The data was sourced from customer profiles, claim records, loan applications, investment records, call logs, internet activity, and more.
All of that data was then cataloged and binned into demographic and psychographic profiles. The profiles were used by the institution’s marketing and research teams to search for new ways to interact with customers and improve returns across the board. Once the strategy was solidified, AI tools were used to automate this new outreach, and the results proved impressive.
The AI tools were able to generate more than 2,000 descriptive features for each individual customer. Multiplied by the number of customers, and that totals more than 3 billion descriptive features across the entire project. The tools also created at least 60 predictive features for each customer, enabling the institution to better understand customer behavior and desires.
As a result, the project saw rapid success. To begin with, the institution was able to recover $10 million in lost annual premiums, turning around a troubling negative trend. Beyond that, the institution generated an additional $15 million in new annual revenue.
All of this came from the personalized outreach efforts that were powered by AI data management and AI communication. This sole project was responsible for these large revenue changes, proving that AI can improve the bottom line for financial institutions, regardless of how narrow or broad their range of services might be.
For ElectrifAi, revenue growth is a priority when working with clients. Looking forward, AI will certainly transform every industry that it can touch, and AI developers like ElectrifAi will be searching for ways to grow revenue for clients while improving data management, understanding of markets and customers, and developing brand-new automation.
