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Better decision making within banking: less risk, more control.

The finance industry has adopted AI quickly and is one of the most mature players in the world of AI. Large parts of the value chain can be improved by the use of AI and there is much more to come.

Possible AI-powered Applications for the banking industry

Smart Loans

The evaluation process of loan eligibility can be improved by the use of machine learning algorithms.

Risks management

More accurate predictions and detailed forecasts based on real-time activities in any given market or environment.

Robotic process automation (RPA)

Replacing small repetitive work with robots.

Fraud Detection

Classification algorithms are trained to detect divergent behaviour of clients, such as abnormal location and buying habits.


High-frequency and quantitative trading with smart algorithms.

Personalized banking

AI powers smart chatbots that provide clients with comprehensive self-help solutions while reducing the call-centers’ workload.

86% of bank executives agreed that the implementation of AI solutions brings a competitive advantage beyond cost savings.

Key components

Credit/loans, asset management, commercial banking, risk management.

Global footprint

7% of all employees in the world are active in the finance sector. The finance industry contributes with 20% to the global GDP.


Aggressive competition, BlockChain disruptive business models, open banking,

What value can Data Science & AI bring to your company?

Data capturing

Using smart cameras, nodes and IoT sensors, you can gather more information about the developments of your goods.

Machine Learning

Using deep learning techniques and image recognition, you automatically convert your data to actionable insights.

The data ecosystem

Connecting data sources from different stakeholders in the value chain, you can create an ecosystem and profit from large scale data analysis.


An optimal approach to Productizing Artificial Intelligence

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