The very fabric of enterprise data utilization is terraforming, as companies move from historic to real-time, operational data. With their split-second sensitivity, these binary bits serve as the heartbeat of innovation.

MongoDB’s Shiv Pullepu discusses real-time data for financial services.

Given the resources needed for such data and artificial intelligence operations, how are data wranglers such as MongoDB Inc. helping financial services companies navigate the resulting complexities?

“You need to have the right data in the different structures and also the right data to these models in the moment, in real time,” said Shiv Pullepu (pictured), principal of industry solutions financial services, U.S., at MongoDB. “MongoDB provides the right data to these AI/ML models. The gap that we are filling with MongoDB here is that historically if you think about real-time analytics, the analyses are generally done using historical data. We provide that real-time operational data that is needed for these real-time analytics applications.”

Pullepu spoke with theCUBE’s John Furrier at the AWS Financial Services Symposium, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed MongoDB’s value proposition for the financial services industry to transform traditional practices, enhance accuracy and drive significant returns. (* Disclosure below.)

Transforming DevOps and legacy systems with real-time analytics and gen AI

Gen AI is not only modernizing applications, it’s also assisting in core migrations from legacy systems. Several use cases of this exist, including translating legacy code such as PL/SQL into modern equivalents such as MQL for MongoDB. This facilitates smoother transitions and reduces the burden on developers during massive transformation projects, according to Pullepu.

“Gen AI provides that positive catalyst type of helper to help migrate the code,” he said. “In addition to the core migrations use case, there are a number of other innovative use cases. The way I see it is there’s a convergence between real-time analytics using the generative AI to open up many innovative use cases for our businesses.”

The intersection of real-time analytics and gen AI unlocks innovative use cases, particularly in fraud prevention, risk management and compliance. For example, vector search, a core technology behind gen AI, significantly enhances these applications by improving detection accuracy, according to Pullepu.

“It’s an innovative approach because you might ask me, ‘Why vector search? Why can’t you use real-time, predictive analytics?'” he said. “In the case of predictive analytics, there is always this delay in actually addressing emerging fraud schemes. There is some new fraud that is emerging as we speak today. Real-time analytics that are models may not be able to address that because they’re too new.”

Vector search allows for the combination of transaction data, customer profiles and other relevant information into vectors. These vectors can identify similarities and anomalies at unprecedented speeds, enabling real-time fraud prevention. This proactive approach can significantly reduce fraud losses and enhance customer satisfaction by preventing fraudulent transactions before they occur, Pullepu noted.

“That’s where I think our Vector Search plays a significant role in that as you [carry out] transactions like a card payment, for example,” he said. “You can actually take the transaction information as one vector and also the customer profile as another vector and also anything around that customer, you can combine it and create these vectors and be able to find these different patterns and anomalies at the speed of light.”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE Research’s coverage of the AWS Financial Services Symposium: 

(* Disclosure: Amazon Web Services Inc. and MongoDB Inc. sponsored this segment of theCUBE. Neither AWS or MongoDB nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE