Replacing the core of Mobile Money with AI – Is it worth it?

Transactions that would traditionally take thirty minutes to complete, like paying electricity bills or depositing and withdrawing funds from the bank, could be instantaneous thanks to AI-powered mobile money. Will this generation of millennials be impatient when it takes ten seconds for a transaction to happen instead of five?

Recently, personal finance, mobile payments are managed by an AI-powered solution backed by robust machine learning algorithms, big data analytics, statistical modeling, and more. An AI system of this kind is currently being adopted within the Mobile Financial Services Industry.

Transaction Experience – AI would take over the heavy lifting in the transaction experience. With today’s fast-paced, days on end with little sleep, even noticing whether or not billing was paid can be difficult. Let AI and voice remind us via a hands-free, time-efficient payment process. It removes the temptation to cheat on our payments by reminding us if we have already done so this month.

Another example is AI can detect if a payment was an expected purchase or not, based on historical trends of location and type of merchant. In the event that authorization is needed, AI can also provide more challenges to secure their account from fraudulent activity.

Customer Onboarding – Rather than go through the cumbersome process of customer onboarding, something that usually happens in person, AI-powered assistants can help in simplifying this step. We are no longer restricted to present hours to interact with customers, so imagine being able to offer self-service onboarding anytime during the day. This could increase conversions by minimizing the amount of time spent waiting for physical approval.

AI-enabled communication channels like chatbots for providers, which come close to replicating real human interaction, are using AI to deliver customer engagement and enhance the user’s experience.

Fraud detection benefits from AI – AI can be used to detect fraud using hybrid algorithms, either developed in-house or through 3rd party enabled services. A user could be thrown additional validation when using the mobile money solution on a bad source by historical analytical data.