Machine learning is the subfield of computer science.

computers have the ability to learn without being explicitly programmed

There are many machine learning applications.

Machine learning evolved from the study of pattern recognition and computational learning theory in artificial intelligence. Data scientists construct algorithms that learn from the data and adapt over time.

The focus is statistical learning. Machine learning deals with predictions and not causality. It is effective at developing more complex relationships than humans can process. Machine learning performs well in nonlinear and interactions settings.

Examples of machine learning application

• Assessing financial transactions – advanced detection of anomalies though flow analysis of transitions. The MindBridge Ai Auditor is an excellent example.
• Outage grid management – predict storm damage and direct workflow and assets, thereby minimizing outages and operational costs
• Wire-Down Management – identify key drivers in wire-down events and direct preventive measures
• Self-driving cars
• Classifying DNA sequences
• Other applications for machine learning include: adaptive websites, affective computing, bioinformatics, brain-machine interfaces, cheminformatics, classifying DNA sequences, computational anatomy, computer vision, including object recognition, detecting credit card fraud, detection of network intruders or malicious insiders working towards a data breach, email filtering, game playing, information retrieval, internet fraud detection, marketing machine learning control, machine perception, medical diagnosis, economics, natural language processing, natural language understanding, optimization and metaheuristic, online advertising, recommender systems, robot locomotion, search engines, sentiment analysis (or opinion mining), sequence mining, software engineering, speech and handwriting recognition, financial market analysis, structural health monitoring, syntactic pattern recognition, user behavior analytics, translation 

Learn about how we use machine learning for financial risk assessments.