Machine Learning for Government Agencies
Serving your constituents with intelligent tools
Most local and state government customer services lines are overwhelmed with the number of resident complaints, inquiries, and concerns they receive daily. This leads to constituent dissatisfaction and inefficient use of public resources. Government entities can use systems that are machine learning enabled to triage incoming calls and answer common questions. High-priority and less common calls can be routed to a human for resolution. This results in better use of public resources, lower wait times, and happier residents.
Machine learning and artificial intelligence can automate:
• Customer service centers and other information lines
• Detection of erroneous financial transactions
• Evaluation of environmental hazards
• Due diligence of business entities
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As constituent concerns increase, state and local agencies need to explore customer experience strategies that enable them to address demand while keeping costs low. Constituent inquiries are typically addressed through office emails, 800 numbers, websites and mobile applications. The reality is most government agencies are not equipped with the resources to continually update these lines of communication, or speak with every single resident that calls in. Automation is key to improving civic engagement and transforming constituent services.
Machine Learning presents local and state government with a more seamless, cost-effective approach. Government agencies can use machine learning to provide services via a chatbot. A chatbot is an artificial intelligence tool that can be programmed to provide a response based on an input; this input can be via text on a computer, phone, or text message, or via voice. When the input is received, the chatbot analyses the input and within fractions of a second provides a response. For example, if someone was to call and ask, “How can I contact my City Council representative?”, the chatbot would then ask for the caller’s zip code and upon receiving the zip code as input, the bot would perform a search and find the City Council representative for that specific zip code and provide the caller with the desired information – all with no human interaction.
In disaster situations like earthquakes and hurricanes, an emergency response line can be established using a mobile app. When someone who is trapped and can’t get to safety calls for help from the app, the app can read the person’s location and transmit it to a central location where a machine learning algorithm can determine which rescue team is closer and can provide help faster. The same algorithm could also determine how many rescue teams are needed in a specific area based on the number of calls for help that are received.
Most chatbot-based Machine Learning solutions can be acquired by public sector agencies at low cost. Microsoft provides special pricing to government agencies and nonprofits on all its products which makes adoption of this technology very inexpensive when compared to commercial solutions.
To learn more about how Advoqt helps government agencies, download our case study:
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