Flowpay’s journey to enable fast expansion into new markets with IBM
Flowpay + IBM
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Flowpay, a fintech startup, began its journey with a lot of data and one idea: provide simple, online, operational financing for small and medium-sized enterprises (SMEs) across the Czech Republic. After seeing great progress in its first market, Flowpay was ready to target customers in other countries. When it is preparing to enter new markets, Flowpay must collect metadata to categorize Payment Services Directive (PSD2) transaction data from prospective customers. Historically, this has been a very time-consuming process for Flowpay. The team knew they needed to revise their approach in order to achieve their growth goals.

Flowpay began collaborating with IBM® Client Engineering as part of IBM’s Fintechx program and it joined a series of innovation workshops. The firm’s goal was to strengthen its processes by implementing generative AI. The partnership resulted in the creation of two AI-assisted processes that were built and tested by seven users over the course of a four-week pilot. The first one is designed to accelerate and simplify the metadata creation process, and the second one automates the creation of an underwriting report. Both processes rely on IBM watsonx.ai™, an integrated suite of AI tools designed for secure, collaborative data management and process automation.

IBM’s Client Engineering team helped us to build a viable, innovative solution within a very short timeframe. The cooperation was professional, the teams are knowledgeable and proactive. William Jalloul Founder & CEO Flowpay
The pilot demonstrated very promising outcomes, on average, an estimated: 100% time savings for new market research and metadata creation ~50% time savings per country for metadata error correction, monthly ~20% speed increase in loan approval process by under-writer
Flowpay is going to enter several new markets in 2024, starting with Slovakia in April. We are going to use the watsonx pipeline to generate the initial version of metadata for Slovakian market—and also the other new markets—while we gradually fine-tune our solution. In parallel, we are going to further enhance the automatically generated underwriting report with new sections—for example, assessing the applicant’s digital presence, main business partners, forecasted income and expenses—and make it available in our main product via API calls. David Hanzelka Head of Data Science Flowpay

The first solution uses large language models (LLMs) to generate completely new content, and to process internet-based search results. It can be easily configured in minutes to analyze publicly available data in any country that Flowpay targets for its expansion. It automatically creates an initial dataset, which makes it easier for Flowpay to categorize and analyze PSD2 transactions and prepare for its first local customers.

The second solution is designed to significantly speed up Flowpay‘s underwriting process. Based on the raw PSD2 transactions from a loan applicant’s bank account and the calculated data points, the LLM will generate a high-level summary of the applicant’s financial health and the risk associated with granting the loan to that person, in easy-to-grasp, non-technical language.

The prototype of the solution we‘ve built together with IBM within the Fintechx program helped us to better understand the ‘modus operandi’ and capabilities of generative AI and is going to significantly speed-up our future expansion to new markets. David Hanzelka Head of Data Science Flowpay
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About Flowpay

Flowpay (link resides outside of ibm.com) is a fintech startup in the Czech Republic that uses predictive AI models to assess risk more precisely and to truly understand the potential of SMEs. The firm acts as a direct lender to SMEs, provides an embedded finance infrastructure for SME platforms and a Risk as a Service (RaaS) solution to lending institutions.

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