Dataiku Case Studies Finexkap: From Raw Data to Production, 7x Faster

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	Finexkap: From Raw Data to Production, 7x Faster Dataiku

Finexkap: From Raw Data to Production, 7x Faster

Dataiku

Technology Category	Analytics & Modeling - Predictive Analytics Platform as a Service (PaaS) - Data Management Platforms
Applicable Industries	Finance & Insurance Retail
Applicable Functions	Sales & Marketing Business Operation
Use Cases	Predictive Quality Analytics Supply Chain Visibility
Services	Data Science Services System Integration
Challenge	Finexkap, a leading fintech providing digital solutions for B2B operators, marketplaces, and e-commerce in western Europe, was facing a challenge with its data science team. The team, consisting of only three data scientists, was using Python in notebooks and a bit of C# to automate processes, but they didn’t have any visual tools for building data pipelines or to conduct on-the-fly data analysis. This method was functional but extremely tedious, and in the long run, they realized it was not sustainable, especially with the company’s growth and plans for future products and expansions. Read More
About Customer	Finexkap Group is a leading fintech company founded in 2012. It provides digital solutions for B2B operators, marketplaces, and e-commerce in western Europe. The group is composed of two companies: Finexkap, in charge of IT development/R&D, and Finexkap AM, a regulated AIFM company in charge of refinancing. The company has financed 400M€ for 3,500 SMEs and has several major partnerships with B2B top-tier players such as METRO Group. The company has a small but ambitious data team that envisions data science and machine learning as a frictionless part of their product and organizational processes. Read More
Solution	In July 2020, Finexkap launched Finexpay, a new service that provides a machine learning-based service that helps B2B e-commerce or marketplace operators offer their clients longer payment terms. The extended payment terms module adds up to 90 days on top of existing terms and is based on a client proprietary score. The Finexpay client score is generated by Dataiku, from which the team built the entire project end-to-end. The team chose Dataiku for its user-friendly interface, easy data exploration and analysis capabilities, flexibility, integrated notebooks connected directly to datasets, visual recipes, and ability to facilitate quick and easy project deployment to production. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Predictive Analytics

Platform as a Service (PaaS) - Data Management Platforms

Applicable Industries

Finance & Insurance

Retail

Applicable Functions

Sales & Marketing

Business Operation

Use Cases

Predictive Quality Analytics

Supply Chain Visibility

Services

Data Science Services

System Integration

Challenge

Finexkap, a leading fintech providing digital solutions for B2B operators, marketplaces, and e-commerce in western Europe, was facing a challenge with its data science team. The team, consisting of only three data scientists, was using Python in notebooks and a bit of C# to automate processes, but they didn’t have any visual tools for building data pipelines or to conduct on-the-fly data analysis. This method was functional but extremely tedious, and in the long run, they realized it was not sustainable, especially with the company’s growth and plans for future products and expansions.

About Customer

Finexkap Group is a leading fintech company founded in 2012. It provides digital solutions for B2B operators, marketplaces, and e-commerce in western Europe. The group is composed of two companies: Finexkap, in charge of IT development/R&D, and Finexkap AM, a regulated AIFM company in charge of refinancing. The company has financed 400M€ for 3,500 SMEs and has several major partnerships with B2B top-tier players such as METRO Group. The company has a small but ambitious data team that envisions data science and machine learning as a frictionless part of their product and organizational processes.

Solution

In July 2020, Finexkap launched Finexpay, a new service that provides a machine learning-based service that helps B2B e-commerce or marketplace operators offer their clients longer payment terms. The extended payment terms module adds up to 90 days on top of existing terms and is based on a client proprietary score. The Finexpay client score is generated by Dataiku, from which the team built the entire project end-to-end. The team chose Dataiku for its user-friendly interface, easy data exploration and analysis capabilities, flexibility, integrated notebooks connected directly to datasets, visual recipes, and ability to facilitate quick and easy project deployment to production.

Impact #1	The team at Finexkap is seven times more efficient with Dataiku than they were using a notebooks-only approach.
Impact #2	The process of connecting to various data sources to pushing models to production has been significantly streamlined.
Impact #3	The time taken for data ingestion, data wrangling, and release to production has been drastically reduced.

Impact #1

The team at Finexkap is seven times more efficient with Dataiku than they were using a notebooks-only approach.

Impact #2

The process of connecting to various data sources to pushing models to production has been significantly streamlined.

Impact #3

The time taken for data ingestion, data wrangling, and release to production has been drastically reduced.

Benefit #1	Data ingestion process reduced from 2 days to a few minutes to hours.
Benefit #2	Data wrangling process reduced from 1 day to a few minutes.
Benefit #3	Release to production process reduced from 2 days to one click + 2 minutes of remapping.

Benefit #1

Data ingestion process reduced from 2 days to a few minutes to hours.

Benefit #2

Data wrangling process reduced from 1 day to a few minutes.

Benefit #3

Release to production process reduced from 2 days to one click + 2 minutes of remapping.

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Overview

Finexkap: From Raw Data to Production, 7x Faster

Operational Impact

Quantitative Benefit