Financial Services

25+ years working inside banks like ČSOB, Raiffeisenbank, and BNP Paribas means we have intimate knowledge of regulatory requirements, legacy constraints, and operational realities. We solve banking challenges because we understand banking operations.

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Trusted by Top Financial Institutions across Europe

Who we helped to become innovation leaders

Raiffeisenbank

To drive operational efficiency, Raiffeisenbank CZ collaborated with Profinit to launch an AI initiative. Our AI and machine learning experts helped design and deploy an AI Assistant that leverages natural language processing to improve employee productivity by providing real-time, contextual access to the bank’s internal knowledge base.

KBC ICT Services

KBC Global Services NV is the IT provider for the KBC Group, a major Belgian-based banking group. For over 25 years, Profinit has been delivering software solutions and application management for group members, including ČSOB Pojišťovna. Our long-standing relationship is built on consistent delivery of reliable financial systems and trusted outsourcing services.

Komerční banka

Profinit has been a BI partner to Komerční banka (a member of the Société Générale group) since 2005. Our work spans consulting, BI & DWH solution development, infrastructure, and operations. Key projects include the migration and redevelopment of a management information system and BI solutions for risk and distribution. Profinit delivers thousands of MDs annually—primarily as fixed-scope engagements.

Thanks to Profinit’s AcceptAI, we achieved a sixfold improvement in call centre conversion rates in a credit campaign targeted based on customer behaviour. The advanced propensity model running on the big data platform achieved a 50% better overall result than the existing model and improved success rates across all channels.

Milan Jirkovský

Head of CRM at Raiffeisenbank CZ

I definitely view the REMOS solution as a successful project that fulfilled the defined expectations and, in many aspects, even exceeded them. A large portion of the credit goes to the Profinit team, who was professional, well-organized, and otherwise great to work with.

Martin Šindelář

Executive Manager IT DEVOPS at ČSOB

Featured Solutions

Dynamic pricing

Dynamic pricing empowers financial institutions to make smarter, data-driven decisions. Advanced analytics simulate thousands of pricing scenarios to identify effective strategies.

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AcceptAI uses predictive decision-making in campaign targeting and customer communication to maximize engagement. This solution analyzes transaction data and predicts customer needs.

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Profinit Modernization Framework

Our systematic approach helps banks and insurers evolve critical systems without the risks and disruption of a full system replacement. We preserve valuable business logic while upgrading essential components—enabling faster innovation and reduced operational costs.

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AI Assistants

Our AI Assistant enhances internal support by allowing employees to query organizational knowledge in natural language. It delivers fast, accurate responses while reducing support tickets and improving productivity across your workflows.

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More than 25 Years in Financial Services

1998

Profinit was founded

2001

First major SW projects

2006

Served 4 of the 6 largest Czech banks

2010

Gartner BI Award EMEA awarded to our client

2017

Increased focus on Big data and Data Science projects

2021

AI, MLOps and decision engine for a London FinTech

2023

AI and SW services for the oldest German bank

2025

Joined Amdocs to deliver our services globally

Our Insights

We navigate the dynamic landscape of modern technology in today’s world. Stay ahead with us.

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Berenberg and Profinit launch strategic tech partnership

Berenberg, Germany’s oldest private bank, and Profinit announced the commencement of a strategic nearshore software development partnership.

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Interest Rate Optimizer

Setting interest rates is complicated: too low cuts profit, too high drives clients away—competition is stiff, especially online. Most banks use risk-based pricing, assigning rates by default probability. Data-science algorithms improve this approach. By giving price-sensitive clients small bonuses at the expense of less price-sensitive ones while maintaining average rates per risk class, we improve acceptance, reduce turnover, and boost profit while complying with banking regulations.

Case Study