Welcome to the world of Artificial Intelligence (AI) and Machine Learning (ML). You are probably already familiar with these terms, but let's take a look behind the scenes.

ML is nothing other than the beating heart of AI. This is where powerful algorithms come into play, searching for hidden patterns and regularities in huge data sets. And what emerges are true marvels of automation - multifunctional solution models with automation potential.

The crucial difference to Deep Learning is that ML involves human intervention in the analysis of the data, the learning process and the actual decision-making process, which can create more transparency for banks and financial service providers.

Note: In the German version, there are links and detailed explanations of the corresponding contents.

Services

With our ML Toolbox, we offer you tools and resources to build your own ML models and benefit from the endless possibilities of Artificial Intelligence. Whether you want to explain the movement of financial ratios or customer behaviour in day-to-day operations, make complex predictions, classify investment data, recognise patterns or identify outliers - our toolbox has you covered.

Imagine that ML systems can make decisions that can approximate or even surpass any model or way of thinking. It is almost magical how such models make predictions, perform classifications, and organise data into clusters. It's as if they reveal the hidden secrets of data and offer us a new perspective.

Our dedicated team of experts will accompany you on your journey, saving you valuable resources such as your time and human capacity. In doing so, we lift your efficiency and increase productivity when needed. We help you analyse your data, optimise the learning process and refine the decision-making process - which is both automated and human-assisted. Together, we create transparency and innovation.

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Fundamentals

Fundamentals

In this section, we explain the basics of machine learning and show you the different types, models and algorithms available.

ML systems learn patterns and relationships from data and improve with experience without being explicitly programmed.

Machine learning is multi-faceted: it is divided into the three methods of

  • Supervised Learning,

  • Unsupervised Learning and

  • Reinforcement learning.

ML/AI systems

  • use algorithmic and statistical methods to learn from historical data,

  • are validated with out-of-sample data with the aim of generalisation and

  • recognise patterns during use.

Articles on Fundamentals

This area is currently only available in German. Please contact us if you are interested in any of the topics.

Strategy and Events

Strategy & Events

Finbridge assists you in developing AI strategies and processes that are aligned with your organisation's goals and business strategy, as well as current regulatory requirements.

In addition to undisputed advantages, AI/ML approaches in banks bring with them new types of risks that have not yet been sufficiently considered in the existing model risk management. The chosen AI strategy must be aligned with national and international regulatory frameworks, laws and regulations. This includes the growing regulatory requirements such as the "EU AI Act".

Articles on Strategy & Events

This area is currently only available in German. Please contact us if you are interested in any of the topics.

Use Cases

Business Cases

Machine learning offers a wide range of possible applications in business that go beyond the functions presented in finance.

Learn about use cases in this section to get a feel for the potential and added value created by Machine Learning:

 
 
  • Alternative Modelling: Instruments, Portfolios, Risk & Financial Metrics
  • Credit Risk Analysis: Default, Ratings, Risk-Networks
  • Customer Risk Analysis: Clustering, Churn, Creditworthiness
  • Fraud Detection: Transaction, Money Laundering

  • Robo-Advisor/Recommender for Traders or Customers
  • Algorithmic Trading & Portfolio Optimization
  • Stock & Time-Series Data Forecasting
  • Sentiment Analysis for Stock & Market Data

  • Automated Document Processing: E-Mails, Invoices, Cheques
  • Summarization & Question-Answering for Long Texts: Regulations, Documentation, Contracts, etc.
  • Customer Service: Biometrics, Chatbots

  • Model Explanation & Compliance through XAI
  • Process Automation & Optimization
  • Data Analysis & Insights
 

Articles on Use Cases

This area is currently only available in German. Please contact us if you are interested in any of the topics.

Tools & Services

Tools & Services

We give you an insight into our development-related tools and techniques.

There is a wide range of machine learning software available in the market. We currently use the following libraries, among others: Scikit Learn, PyTorch, TensorFlow, KNIME, Streamlit and Dash.

These bring with them features such as data preparation, data mining & analysis, models & algorithms, optimisation, automation, XAI techniques, the development of interactive applications and many more.

Demos

Discover the machine learning tools and techniques we use in interactive demos*:

ML Engineering Stack

On the way from the raw data to the finished user interface for the representation of result objects from the machine learning models used, there are numerous standard libraries. We use selected well-known libraries* at all stages of development:

[*]: The links refer to external data outside our domain. Despite careful control of the content, we assume no liability for the content of external links.


Articles on Tools & Services

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References

References

With our comprehensive range of AI and ML services, Finbridge supports clients at every stage of ML adoption with a set of AI and ML services and software engineering solutions.

Our modular and interactive applications are already used in various processes (predictive models for financial parameters, anomaly detection, XAI techniques, etc.).

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Your expert for inquiries

Dr. Carsten Keller

Partner at Finbridge GmbH

Tel.: +49 151 58 25 90 05

AI and ML are key elements of a digitalised company

Financial service providers and banks are expanding their engagement in the areas of machine learning and artificial intelligence and are using these techniques in their processes. However, institutions also need the right prerequisites for the successful use of ML - above all, a qualitative data basis enables potential to be raised and precise predictions to be made.

"The acceptance of machine learning is higher than most people realise. However, well-maintained data management and long-term thinking have a top priority for a successful use in tailored use cases."

— DR. CARSTEN KELLER

Finbridge is your partner when it comes to digitally transforming your processes and your business model. Our machine learning experts support you in the formulation and practical implementation of a strategy in the field of artificial intelligence. With our ML knowledge and our powerful applications, we help generate added value for your processes.

Articles on ML

This area is currently only available in German. Please contact us if you are interested in any of the topics.