Alkami Technology, a cloud-based digital banking solutions provider for U.S. financial institutions, announced it created and released a first-of-its-kind Engagement AI Model.
According to the Plano-based company, the brand new model is a combination of artificial intelligence (AI), machine learning (ML) and Alkami's own proprietary Key Lifestyle Indicators (KLIs).
Utilizing the amalgamation of technologies, the model enables financial institutions to pinpoint account holders that demonstrate behaviors most likely to lead to retention and account growth.
Once these retainable account holders are identified, financial institutions are then able to increase their engagement with products, service offerings and digital channels, according to Alkami.
"The model assesses the entire universe of a financial institution's account holders on a daily basis to identify those account holders exhibiting behaviors that have historically led to deeper engagement," said Mark Leher, director, product management at Alkami. "Not only does this save on account acquisition costs, but it also empowers the financial institution to engage with those who are more likely to take action on a targeted campaign."
According to Alkami, numerous financial institutions use an attrition model, which is when they identify account holders that have a high risk of leaving, allowing the financial companies to develop “win-back strategies” as a result.
Using internal research, the North Texas company says that it discovered that account holders that scored the highest risk for attrition were around 15 times more likely to leave a financial institution than account holders that scored as “highly engaged.”
Alkami made the decision to invert the attrition model with its Engagement AI Model, so that financial institutions are able to “focus their time and budget where it matters most—in retaining and growing their engaged account holders,” according to the Plano company.
"When we looked at the full spectrum of attrition scoring, our research showed that attrition is significantly lower among highly engaged account holders, so we developed a model that not only identifies these highly engaged account holders but also layers in Alkami's KLIs—labels describing the type of transaction or behavior a customer or member engages in—to best predict which behaviors drive incremental engagement," said Leher.