Intelligent Decision Automation

Combine data knowledge and expert knowledge to make better decisions in the digital world.

Machine Learning & Business Rules
Data Knowledge Meets Expert Knowledge

Companies rely on Intelligent Decision Automation to automate operational decisions using intelligent technologies. They combine machine learning technology with the business rules management approach to make better, predictive decisions and drive digital transformation through automation.



Data-driven knowledge through artificial intelligence (AI)

Data-driven Decision Making

Apply machine learning technology to unlock data-driven knowledge and realize valuable insights.

Automation of operational decision-making processes

Business Automation

Combine business rules and artificial machine learning to automate operational decision-making processes.

Digital transformation through real-time decisions

Real-time Decisions

Leverage real-time decisions across all processes and channels to drive digital business transformation.






Machine Learning as a Key Technology in the Financial Industry

The combination of intelligent technologies and automation is driving digital transformation in the financial industry. With their knowledge-intensive business models, banks and insurance companies benefit in a large variety of application scenarios.


Fraud Prevention

The automation and monitoring of financial transactions opens up large potentials to improve fraud prevention. Applying machine learning to payment data helps reduce the number of false positives. New fraud patterns can be recognized and applied automatically through self-learning models.

Market Abuse Surveillance

Machine learning can significantly improve and automate market abuse and insider dealing detection. It is able to recognize complex patterns of abusive conduct across large trading portfolios and include all kinds of information for a more accurate market abuse surveillance.

Money Laundering Detection

Artificial intelligence is able to identify highly complex relationships in data to detect money laundering in financial transactions. It helps minimize the amount of false positives, prioritize suspicious transactions and reduce the efforts for processing and clarifying each case.



Early Warning

Early warning systems allow companies to detect risks and opportunities early on and take the right actions. Using machine learning, companies can uncover new data correlations and thereby predict developments very precisely. Artificial intelligence thus enables them for example to reduce credit defaults and counteract customer churn.

Insurance Underwriting

Insurance companies can leverage existing data to improve and automate underwriting risk decisions. They apply machine learning to create more accurate risk models. By combining artificial intelligence and decision automation, insurers can reduce underwriting costs and drive the digital transformation of their core business operations.

Customer Engagement

To retain customers and increase the customer lifetime value, companies need to stay relevant to their customers and anticipate their needs. With personalized product recommendations and customer care, artificial intelligence enables smart services in real-time that meet customer demands at every digital touch point.




Definitions

What is Artificial Intelligence?

Artificial Intelligence (AI) deals with methods and approaches that enable computers to perform tasks intelligently. Machine learning is considered a key technology to achieve artificial intelligence and to realize intelligent software systems.

What is Machine Learning?

Machine learning is the application of methods to achieve artificial intelligence. Therefore, learning algorithms are used that independently generate knowledge from experience (data) with the help of computers. The result is a model that can be embedded in software systems, giving them intelligence.

What are Business Rules?

Business rules are policies and statements about how to deal with a situation depending on certain criteria. Examples include lending or pricing guidelines, compliance checks, process control specifications, calculations, and more. Most business rules exist as knowledge in the minds of subject matter experts.




Combining Machine Learning and Business Rules

Intelligent decision automation combines data knowledge and subject matter expert knowledge to automate operational decisions. To this end, machine learning models (data knowledge) are orchestrated together with business rule models (expert knowledge) and executed automatically in any software solution.


Intelligent Decision Automation combines business rules and machine learning to automate decisions

Expert Knowledge 

Expert knowledge is implemented by business experts using business rules. The rules-based approach allows companies to specify internal policies, regulatory requirements and business needs that influence business decision-making.

Business Decision

Driving business decision automation depends on the ability to capture both expert knowledge and data knowledge. ACTICO allows companies to individually combine both approaches to make better decisions, improve transparency and drive digital transformation through automation.


Data Knowledge 

Data knowledge is learned automatically out of data using machine learning algorithms. This approach of applying artificial intelligence results in adaptive decision models that learn continuously without requiring human interaction.





costumer

Read our innovation insight to learn how intelligent decision automation enables truly digital business processes.

Products
Software for Intelligent Decision Automation
Learn more
Software for Intelligent Decision Automation through Machine Learning
ACTICO Machine Learning
Decision automation with artificial intelligence

ACTICO Machine Learning is an integrated software platform for intelligent decision automation. It offers a well-aligned toolset for modeling, managing and automating operational decisions and apply machine learning technology.



Start with intelligent decision automation today!

What data is available for your machine learning project?