Neema Breaks Down Barriers to Cross-Border Payments — and AI Adoption — With Snowflake

In an increasingly globalized world, financial services remain as one of the sectors where requirements and regulations still vary across countries. For individuals who relocate for work or move around frequently, and for companies that serve global clients or manage international operations, managing money across borders payments can be unnecessarily complex. 

Neema’s mission is to simplify global money movement, making it easier for individuals and businesses to send and receive funds across currencies, no matter where they are.

Before Neema, many of its users had to rely on traditional methods to send money, often at a high cost and with long delays. Since launching in 2017, Neema has made sending money across borders dramatically simpler and more affordable for its customers. In 2023, it expanded its infrastructure to support financial institutions and Electronic Money Institutions (EMIs), enabling them to offer faster and more reliable cross-border payments to their clients. Today, with Snowflake’s AI Data Cloud, Neema is leveraging cutting-edge AI solutions to bring even more speed, efficiency and intelligence to global money movement.

Neema has always managed data in the cloud, but used to connect multiple systems as part of its data estate. While this served the business for a time, Maor Nativ, VP of Data at Neema, soon found a complex ecosystem came with significant drawbacks.

“We’d hit brick walls and find we didn’t have the resources to overcome them,” Nativ explains. “So, we’d need to spend time coming up with a solution, which slowed down our time to value across all data products. Ultimately, there was a lot of time spent maintaining systems instead of solving real business challenges.”

These systems also created a steep learning curve that led to slow onboarding for new employees and demand for specific skill sets across Neema’s analysts and engineers, which limited the available talent pool.

“The real thing I found lacking, though, was innovation,” Nativ adds. “Many platforms perform well, but they just do the same thing they’ve always done. I wanted a solution that would evolve with our needs and deliver added value. And that’s what I got with Snowflake.”

By consolidating its data stack in Snowflake’s AI Data Cloud, Neema has simplified and streamlined three crucial data use cases.

First are its internal use cases. With all data in a single repository, Neema can quickly power dashboards and reports that decision-makers need to take decisive action.

Then there are Neema’s operational use cases, like using data from Snowflake to accelerate marketing automation and better target its campaigns.

Finally, Neema will use Snowflake data to power use cases around personalizing products and services. For example, during the onboarding process, Neema sees key stages where certain types of users may not complete necessary steps to set up their accounts. By drawing data on how similar users behave, Neema plans to offer custom messages that guide each user through the onboarding process in an intuitive way that’s personalized to their exact situation.

Between these three systems alone, Snowflake already helps Neema save valuable time, while making more informed decisions and supporting customers with tailored products.

With the AI Data Cloud, Neema gets much more than a central repository for data use cases and products — it also gets a foundation for continued innovation.

Nativ recently hosted a hackathon for the whole Neema team to experiment with AI use cases. Thanks to its centralized data platform, and Snowflake’s Cortex AI tools, Neema transformed one of the hackathon’s most promising ideas from concept to MVP to production in just two and a half weeks.

Despite its rapid development, this valuable AI tool solves a significant challenge for Neema’s sales and data teams. In the past, when trying to provide a quote for a new business customer, Neema teams would need to manually research competitor pricing, exchange rates and other costs to ensure they could offer a competitive rate at a reasonable cost. However, finding all this information took a significant amount of time, to the point where managers, sales reps and analysts were spending between 2,500 hours collectively each year manually searching documents.

By powering a Mistral large language model with Cortex AI and data from Snowflake, Neema now automatically collects publicly available competitor pricing information and combines it with internal data to offer rapid pricing recommendations.

Now, Neema’s AI use case shows reps benchmarks for any pricing offer to ensure strong margins and provides alternatives in cases where the pricing isn’t competitive — with guidance on how to adjust the offer to make it more enticing. All of this is completed in just 40 seconds, saving significant people hours and delivering a faster, more responsive service to potential clients.

With Snowflake and a powerful AI use case at its side, Nativ and his team find they get more value from their data — faster than ever. “Compared to platforms I’ve used in previous roles, I find Snowflake generally delivers data much faster, with far less maintenance overhead required,” Nativ says. 

A large part of this efficiency is thanks to the data sharing and collaboration opportunities from Snowflake’s extensive user community. “The data sharing technology with Snowflake is nothing short of genius,” Nativ says. “When my team had to do a reverse ETL with Hubspot, I was worried it would be a manual, time-consuming process that would have taken two or three days. But because Hubspot is also a Snowflake user, we could set up a data share in just two clicks.”

With a faster, leaner and more performant data platform, Nativ and his team are free to look to the future of data and AI tools. “There’s no escaping the impact AI will have,” Nativ explains. “With Snowflake, we can really look at everywhere we can use AI to improve our products and operations — without having to worry too much about technical or resource constraints.”

For example, Neema is already looking at building a central repository of company processes, product data and other insights that large language models can pull from to answer queries. That way, Neema can automate even more processes with confidence that AI models are basing decisions and guidance on trusted data.

“Due to its performance, flexibility and ease of use, the AI Data Cloud is a powerful enabler for everything we do,” Nativ says.