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Case Study: How This Forex Incumbent Improved Their Cross Border Payment Process With The Help Of An AI & ML Data Aggregator



Money transfer companies make a considerable amount of their revenue from the fees they levy on each transaction, and therefore factors like transfer rate, fees associated, and transfer speed plays a vital role for a company to thrive in this competitive world of money transfer. With banks offering attractive prices, money transfer companies started to struggle to stay on top of their game. In a bid to stay relevant, one such veteran forex company switched to artificial intelligence-based solutions for critical data in order to tackle the changing forex landscape. 

The Challenge

With the rise in advanced technologies in other parts of our lives, tech-savvy customers expect BFSI companies to deliver seamless financial experience. And that’s why BFSI companies are equipping themselves with tools that are required to stay ahead of competitors. 

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A Colorado-based cross border, cross-currency, money movement payment company (wishes to remain anonymous) that allows transactions, online as well as offline, have been struggling a long time in streamlining their business process in order to deliver enhanced customer service.

Considering the massive scale of operation, with five lakh agent locations in over 200 countries and territories, and a plethora of online payment options, the customer had to deal with numerous challenges, such as volatile market rates, competitors trying to undercut them, and a changing marketplace. An incumbent in the money transfer space, the company had completed more than 800 million transactions in 2018 and has moved over $300 billion in principal amount. 

To tackle these problems efficiently, the customer required a seamless framework that can provide detailed information on the competitors’ positioning and their pricing strategies, along with their transfer fees, transfer speed, and the exchange rate of the competitors per transaction. 

The Solution

The answer to the company’s problem was data. And, therefore, the customer demanded an AI-based framework that could obtain the high quality, hard to get, competitor intelligence, which Bridged agreed on to provide. 

Bridged is a company that provides hard to obtain human-powered data for artificial intelligence (AI) models at a scale that can create a competitive advantage and grow revenue for its customers. The company used a combination of artificial intelligence technologies and a 13,000 strong, highly skilled workforce to develop unique and vast data at a scale, significantly improving the quality of data models. Whether it be training data for machine learning models or competitive intelligence, they drew a “bridge” between the problem and solution for the customer.

The scalable solution included retail intelligence, content generation, content categorisation, and video and image tagging for companies across the world. The massive workforce captured over a million data points from top-tier competitors daily, which they converted into statistical information and leveraged it to provide insights into new market opportunities, competitor behaviour, and price optimisation, amongst many others.

Bridged has serviced numerous AI/machine learning companies with their data requirements and therefore, has become the perfect choice for the forex exchange company to deal with their challenges. With the help of Bridged’s expertise, the customer was able to receive agent store location data of its competitors for 50+ countries; and pricing data such as transfer speed, transfer rate, and the exchange rate of 13 competitors spanning 1300+ countries and 650+ currency pairs in the online and offline space. 

With the combination of technology and crowdsourcing Bridged delivered artificial intelligence as a service, providing scalable data solution for the customer. Along with a scaled process to capture real-time data on multiple competitors, Bridged also designed a fully managed process to cover multiple sources, and utilise multiple APIs, to ensure the data shared was accurate and complete. Additionally, the customer used the data provided, giving it the ability to move to a dynamic pricing structure and thus winning business.

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Being a brand that services artificial intelligence and machine learning companies with their data requirements, Bridged understood the company’s requirement for high-quality data. And therefore, it delivered 150k+ data rows per day, thus giving the customer precise insights for the company to adjust its offerings and prices. With the help of the structured data, it adjusted its offering and pricing parameters, which, in turn, increased their earnings from each transaction. The customer also enjoyed the luxury of obtaining data that they’ve struggled to find and utilise in the past.

By collaborating with Bridged, the customer was able to gain a strong data-driven understanding of the competitive landscape across geographies. Alongside, they had the privilege of receiving critical data on store locations, for its offline market, that wasn’t accessible before. This information also helped the customer to analyse into which geographical locations they can maximise their bottom-line.

In terms of return of investment, working with Bridged, helped the customer to reduce its expenses by 70% and time to access this data by 90%.

Future Prospects

The customer aims to expand its collaboration with Bridged and the scope of its requirements with a focus on capturing the location data for more countries, and the pricing data across multiple channels, such as mobile apps, etc. Besides, Bridged is looking to use its crowd for auditing the retail locations to ensure better customer service. 

Presently, they’re working on an FX product — Smart Pricing System, that has already captured 25M+ data rows from leading money transfer companies. It captures data points such as transfer fees, transfer speed, and exchange rate for various send amounts and currency corridors. The product has been designed to help money transfer companies to locate new market opportunities, track their competition, increase their revenue, price their transactions strategically, and analyse competitors’ reactions to a given change in price.

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