AI in Public Sector

AI IN PUBLIC INDUSTRY

Public institutions are being pressed by private companies and new generations on the labor market to provide better service at a lower cost. At the same time, many public institutions are suffering from legacy IT systems, as well as an extreme focus on the legality of their processes, which makes adoption of new technology complicated.

AI can improve service for citizens and companies. It can lower costs for the services provided and at the same time raise satisfaction level. This can be achieved through, for instance, better personalization (portals, communication), fraud detection and prevention or, for instance, channel preference. Utilizing AI at scale (including gathering the right data for analytics purposes, improving data quality and also putting analytical models into production) is the key to future service offerings in the public sector.

The public sector can use machine learning solutions of all types – natural language processing, supervised learning, and unsupervised learning.

Areas of AI application in the Public sector

  • Personalized content.
  • Prediction of potential fraudsters or find historical fraud patterns and predict new.
  • Sort and categorization of large amounts of documents, cases or text material in general for QA.
  • Categorization and predictions on companies or citizens according to the behavior and needs.

Use cases

 
Email routing – classification in Public sector
Emails get classified by using a supervised learning algorithm, reducing manual work
Content comparison in Public sector
An algorithm compares the content of documents and highlights similarities and differences
Phrase detection
An algorithm scanning contracts and detecting phrases that are not allowed to use
Chatbot in Public sector
A more dynamic Q&A page improving instant communication and interaction with clients
Fraud predition
Predict and find fraud patterns that cannot be seen using traditional rule-based methods
Client reaction based on scenarios in Public sector
A model predicting which client might react in a specific scenario
Case complexity
Prediction of difficult cases that might take longer to process
Categorize companies or individuals
A non-supervised learning algorithm clusters individuals or companies based on descriptive variables
 

Meet the industry leader

Christian Villumsen

Christian Villumsen

Public, 2021.AI

Christian is a passionate Senior Advisor with 20 years of experience within the Fintech market. He has a proven track record in Fintech, Credit & Market Risk, Compliance, Project, Program, and Portfolio Management. Previously, he worked as a Director at Saxo Bank and spearheaded the Global Enterprise Risk initiative.

cvi@2021.ai in

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