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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. Banks and other financial instances feature immense amounts of data, which inadvertently entails complexity and urges caution. A solid data governance structure is thus crucial for any development of ML or AI models.

What do you need before you can start implementing ML and AI in the finance sector?

Data Governance

By optimizing your master data management by a Data Architect you ensure a solid foundation from where other data specialists can work on and add value to your company

Data Engineering

After your data governance is sturdy and future proof, Data Engineers can create a data pipeline that extracts data from your data warehouse (or data lake) for further pre-processing.

Data Scientist / Machine Learning engineer

After a data pipeline is created, Data Scientists (for more general purposes) and Machine Learning Engineers (for implementing, validating and maintaining machine learning models) can be utilized to create insights or direct impact on the bottom line of your company.

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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