ForMotiv is a Behavioral Intelligence platform that is changing the way financial service companies predict customer and agent ‘intent’ by analyzing their digital body language.
Using a combination of A.I., machine learning, and predictive behavioral analytics, ForMotiv’s proprietary technology solves a previously unknown – but obvious in hindsight – problem, reading people’s body language online.
As businesses have shifted from face-to-face interactions to ‘faceless’ digital interactions, the crucial human element of reading someone’s body language and responding to their behavioral cues has been lost. Until now.
Imagine it is 2005 and you are applying for an insurance product. You would likely walk into a retail branch, sit down with an agent and begin filling out a stack of application papers. That agent was trained to read your behavioral cues – if you were confused or frustrated, they offered a helping hand. If you were excited or engaged, they could offer you additional products you may find valuable. If you were nervous, fidgety, or exuding odd behavior like copying answers from a sheet of paper or changing answers to questions, they would (hopefully) know to further qualify you.
Online, companies are completely blind to all of their user’s behavioral cues. It would be like walking into a retail branch, but before you fill out the application, they put a blindfold on (or leave the room). It sounds crazy when you put it that way, but that is exactly how some of the biggest businesses in the world are operating today – strictly on the final answers you provide. The SAT model of handing in bubbles has proven to have flaws. If they operated like your high school math teacher, and instead, forced you to ‘show your work’ to see how you arrived at your answer, they would have been much better off. We are the high school math teacher. HOW you answer can be far more telling that WHAT you answer. Ask any FBI, Government agent, or Poker player and you will find it to be true.
Using thousands of unique behavioral cues, such as typing speed, idle time, copy/paste behavior, and form corrections, ForMotiv is able to accurately predict the ‘outcome profile’ of a user. Their profile could be anything from risky/fraudulent behavior to application abandonment to a likely delinquent payer to a potentially profitable customer.
Given a user’s ‘prediction score’, companies are able to dynamically engage with that user, both from a conversion perspective, as well as a dynamic security perspective. For instance, if a user is showing signs of engagement, but seem likely to abandon the app, ForMotiv can dynamically engage them with FAQ’s on troublesome questions, a Live or Video chat pop-up, or provide the company’s marketing team with a unique identifier so they can adjust their remarketing intensity towards likely profitable customers who abandoned the application. On the other hand, if a user is showing signs of risky or fraudulent behavior, such as copying/pasting their social security number and home address, or changing answers to e-med or financial questions, not only can we alert companies, in real-time, of that behavior, but we can dynamically interact with the customer and add additional friction, such as a pop-up to “upload a government-issued ID”.
A new feature / use case we recently launched helps companies oversee their internal or distributed agent workforce. We have found, through data, that agents often act in their own best interest, changing policies submitted by clients or manipulating applications to receive the best rate, but ignoring the best ‘coverage’ for the customer, opening both the customer and the company up to previously undetectable risk. By analyzing their behavior, we’re able to drastically reduce risk for both customers and companies, as well as save them significant amounts of money.
Our technology is built with supervised machine learning, taking thousands of unique behavioral cues from each individual user application and correlating it against millions of additional data points and outcomes to uncover predictable models. We have analyzed over 300B behavioral data points, to date. While it sounds complicated, the technical integration can be done in 10-minutes and instantly begins detecting forms and applications and the interactions on each.
We believe behavioral biometrics and predictive behavioral analytics will be the key to understanding user behavior, authentication, and digital identity for businesses of all shapes and sizes in the future. Using behavioral analytics and understanding “Digital Body Language” is like putting glasses on for the first time, once you can see, it’s hard to live without it.
We have use cases that span multiple industries and multiple departments within those industries, such as marketing, data science, A.I., underwriting, risk/fraud management, and agent/employee oversight.
