Insurance is a serious and complex subject. When it comes to securing their lives, their health, and their finances from any possible eventuality, customers understandably want to leave no stone unturned. During the process of buying insurance, they require access to detailed information while evaluating multiple options, in order to make an informed decision. And they will also need constant post-purchase support when it comes to making inquiries about their policies or filing claims.
Needless to say, insurance firms across the globe receive massive volumes of queries every day, from prospective customers looking to buy insurance, and existing customers looking for help. Given the sheer volume of inbound queries, it is not always possible for human insurance agents or support staff to handle these conversations with the speed, efficiency and precision required.
This is where Conversational AI can help.
AI-powered Intelligent Virtual Assistants (IVAs) offer insurance customers a seamless conversational interface to get all their queries resolved, both before and after buying an insurance plan or policy.
Here are five of the key Conversational AI use cases for insurance brands.
Conversational AI is a very effective tool for information dissemination. An AI Assistant essentially functions as an interactive, conversational FAQ for your firm — answering customer queries about plans, policies, premiums, coverage, and more.
A key advantage of using an AI Assistant is that your customers can get instant answers to their 24/7, without needing to wait for ‘office hours’ to get hold of a sales or customer care representative on the phone or email. Conversational AI also ensures that the information provided is accurate, consistent, and up-to-date with your firm’s policies and standards.
With Conversational AI, the process of filing a claim becomes a lot faster and more efficient for customers. Instead of waiting to talk to a service representative, a customer can instead file the claim anytime by chatting with your AI Assistant on their smartphone. The assistant can instantly pull up the customer’s information from your database and take them through the claims process in a swift and seamless manner.
Conversational AI can be very useful when it comes to helping customers manage their policies. For instance, the AI Assistant can send renewal reminders to the customers and keep them up-to-date on policy information. The conversational interface simplifies the process of modifying personal details in the policy.
The process of submitting documents and getting them verified also becomes a lot easier — a customer can simply upload the documents in the chat window and the AI can scan it within seconds, and accept them or reject them in case it identifies any discrepancies. The assistant can also send customers reminders about upcoming payments, and simplify the payments process on the customer’s preferred channel.
An AI Assistant can serve as a virtual insurance advisor for customers. Simulating the behavior of a human insurance agent, it can engage the customer in a conversation and ask them questions to understand their needs and expectations. Leveraging the power of Natural Language Understanding (NLU), the AI can precisely pinpoint the customer’s intent based on their responses. Based on this, the assistant can then make personalized policy recommendations to the customer.
The customer details and data gathered during this process also provides your sales team with better context about what the customer is looking for, which further improves the likelihood of a conversion further down the sales funnel.
As we discussed earlier, an AI Assistant is not bound by office hours and is available to your customers 24/7. This is particularly useful when it comes to resolving urgent customer issues. When a customer requires an instant response to a query or a swift resolution of a problem with their policy, facing the long wait times of a customer care helpline can be very frustrating. An AI Assistant helps insurance firms prevent the likelihood of this kind of negative experience.
Moreover, Conversational AI enables you to scale up your customer support capacity exponentially. The vast majority of support queries, as much as 80%, require the same routine, repetitive responses or tasks. The AI Assistant can seamlessly resolve these simple customer queries and issues. When 80% of your customer care is fully automated, you can effectively handle a spike in customer queries without the need for additional investment in human resources.
AI automation of customer care also improves the productivity and efficiency of your customer service representatives, as they are now free to dedicate their time and attention to tackling complex customer issues that require their intervention.
Zurich Insurance, one of the world’s largest and most experienced insurers needed a solution that would enable them to instantly resolve customer queries, and make their customer support experience seamless and as easy-to-access to possible.
Haptik built ‘Zuri’, an Intelligent Virtual Assistant (IVA) available 24/7 on Zurich’s official website. Zuri helps customers manage their existing policies and enables faster query resolution by guiding them through every step. It swiftly handles routine tasks such as making a claim or withdrawal, modifying personal details in the policy, offering premium-related information etc. Zuri successfully resolved 70% inbound queries end-to-end, with no human intervention required.
Watch the video above to see Zuri in action!
Conversational AI has proved to be a great asset for the insurance sector, helping brands significantly enhance their customer experience, scale-up support, and drive conversions and sales.
The need for insurers to adopt AI Assistant solutions is only likely to grow, as their focus increasingly moves towards targeting digitally-savvy Millennials. This demographic is estimated to make up 75% of the global market by 2025 and will be actively seeking insurance. The onus lies on forward-looking insurance firms to undergo the digital transformation required to engage these customers with the speed, accuracy and efficiency that they expect.
Want to develop an Intelligent Virtual Assistant solution for your brand?Get in Touch
Originally published at https://haptik.ai
Using embedded analytics in software applications can drive your business forward
Analytics in your tools can help users gain insights that can help move your clients and the organization to the next level.
More than two years ago, Edsby, which provides a learning management system for educational institutions, began embedding analytics into its software that enabled teachers and administrators to detect student learning trends, assess test scores across student populations, and more, all in the spirit of improving education results.
The Edsby example is not an isolated event. Increasingly, commercial and company in-house software developers are being asked to deliver more value with their applications. In other words, don’t just write applications that process transactions; tell us about the trends and insights transactions reveal by embedding analytics as part of the application.
“Software teams are responsible for building applications with embedded analytics that help their end users make better decisions,” said Steve Schneider, CEO of Logi Analytics, which provides embedded analytics tools for software developers.” This is the idea of providing high-level analytics in the context of an application that people use every day.”
SEE: Microservices: A cheat sheet (free PDF) (TechRepublic)
Schneider said what users want is transactional apps with built-in analytics capabilities that can provide insights to a variety of users with different interests and skill sets. “These are highly sophisticated analytics that must be accessible right from the application,” he said.
With the help of pick-and-click tools, transaction application developers are spared the time of having to learn how to embed analytics from the ground up in their apps. Instead, they can choose to embed an analytics dashboard into their application, or they can quickly orchestrate an API call to another application without a need to custom develop all of the code.
“You can just click on the Embed command, and the tool will give you a Java script,” Schneider said. “In some cases, you have to do a little configuration for security, but it makes it much easier to get analytics-enriched apps to your user market faster.”
Getting apps to market faster
Here’s how an embedded analytics tool can speed apps to market.
A marketing person is tasked with buying ads and organizing campaigns. He or she gathers information and feeds it to IT, which periodically issues reports that show the results of ad placements and campaigns.
SEE: How to overcome business continuity challenges (free PDF) (TechRepublic)
Now with an application that contains embedded analytics, the marketing person can directly drill down into the reporting information embedded in the app without having to contact IT. This can be done through a self-service interface in real time.
“In one case, a manufacturer was trying to improve operational performance through the use of an application and set of stated metrics,” Schneider said. “Everyone had to log in to the application to record their metrics, but the overall goal of improving performance remained elusive. The manufacturer decided to augment the original application with an embedded analytics dashboard that displayed the key metrics and each team’s performance. This provided visibility to everyone. This quickly evolved into a friendly competition between different groups of employees to see who could achieve the best scores, and the overall corporate metrics performance improved.”
For most developers, embedding analytics in applications is still in early stages—but embedded analytics in apps is an area that is poised to expand, and that at some point will be able to incorporate both structured and unstructured data in in-app visualizations.
Best practices for embedded analytics
Companies and commercial enterprises interested in using embedded analytics in transactional applications should consider these two best practices:
- Think about the users of your application and the problems that they’re trying to solve
This begins with asking users what information they need in order to be successful. “Application developers can also benefit if they think more like product managers,” Schneider said. In other words, what can I do with embedded analytics in my application to truly delight my customer—even if it is the user next door in accounting who I see every day?
2. Start simple
If you haven’t used embedded analytics in applications before, choose a relatively easy-to-achieve objective for your first app and work with a cooperative user. By building a series of successful and high usable apps from the start, you instill confidence in this new style of application. At the same time, you can be defining and standardizing your embedded app development methodology in IT.
China and AI: What the World Can Learn and What It Should Be Wary of
China announced in 2017 its ambition to become the world leader in artificial intelligence (AI) by 2030. While the US still leads in absolute terms, China appears to be making more rapid progress than either the US or the EU, and central and local government spending on AI in China is estimated to be in the tens of billions of dollars.
The move has led—at least in the West—to warnings of a global AI arms race and concerns about the growing reach of China’s authoritarian surveillance state. But treating China as a “villain” in this way is both overly simplistic and potentially costly. While there are undoubtedly aspects of the Chinese government’s approach to AI that are highly concerning and rightly should be condemned, it’s important that this does not cloud all analysis of China’s AI innovation.
The world needs to engage seriously with China’s AI development and take a closer look at what’s really going on. The story is complex and it’s important to highlight where China is making promising advances in useful AI applications and to challenge common misconceptions, as well as to caution against problematic uses.
China’s approach to AI development and implementation is fast-paced and pragmatic, oriented towards finding applications which can help solve real-world problems. Rapid progress is being made in the field of healthcare, for example, as China grapples with providing easy access to affordable and high-quality services for its aging population.
Applications include “AI doctor” chatbots, which help to connect communities in remote areas with experienced consultants via telemedicine; machine learning to speed up pharmaceutical research; and the use of deep learning for medical image processing, which can help with the early detection of cancer and other diseases.
Since the outbreak of Covid-19, medical AI applications have surged as Chinese researchers and tech companies have rushed to try and combat the virus by speeding up screening, diagnosis, and new drug development. AI tools used in Wuhan, China, to tackle Covid-19 by helping accelerate CT scan diagnosis are now being used in Italy and have been also offered to the NHS in the UK.
But there are also elements of China’s use of AI that are seriously concerning. Positive advances in practical AI applications that are benefiting citizens and society don’t detract from the fact that China’s authoritarian government is also using AI and citizens’ data in ways that violate privacy and civil liberties.
Most disturbingly, reports and leaked documents have revealed the government’s use of facial recognition technologies to enable the surveillance and detention of Muslim ethnic minorities in China’s Xinjiang province.
The emergence of opaque social governance systems that lack accountability mechanisms are also a cause for concern.
In Shanghai’s “smart court” system, for example, AI-generated assessments are used to help with sentencing decisions. But it is difficult for defendants to assess the tool’s potential biases, the quality of the data, and the soundness of the algorithm, making it hard for them to challenge the decisions made.
China’s experience reminds us of the need for transparency and accountability when it comes to AI in public services. Systems must be designed and implemented in ways that are inclusive and protect citizens’ digital rights.
But a closer look at the dynamics of China’s AI development reveals the importance of local government in implementing innovation policy. Municipal and provincial governments across China are establishing cross-sector partnerships with research institutions and tech companies to create local AI innovation ecosystems and drive rapid research and development.
Beyond the thriving major cities of Beijing, Shanghai, and Shenzhen, efforts to develop successful innovation hubs are also underway in other regions. A promising example is the city of Hangzhou, in Zhejiang Province, which has established an “AI Town,” clustering together the tech company Alibaba, Zhejiang University, and local businesses to work collaboratively on AI development. China’s local ecosystem approach could offer interesting insights to policymakers in the UK aiming to boost research and innovation outside the capital and tackle longstanding regional economic imbalances.
China’s accelerating AI innovation deserves the world’s full attention, but it is unhelpful to reduce all the many developments into a simplistic narrative about China as a threat or a villain. Observers outside China need to engage seriously with the debate and make more of an effort to understand—and learn from—the nuances of what’s really happening.
Building a Discord Bot for ChatOps , Pentesting or Server Automation (Part 5)
Coding and debugging with Visual Studio Code
Open Visual Studio Code and press CTRL+Shift+P to enter the input window. Write “ssh” and select “Remote-SSH: Add New SSH Host…” for adding our server. It will ask you IP Address and the user of our Digital Ocean server
The app will show us the success message allowing us to connect directly
Once again press CTRL+Shift+P and enter “Remote-SSH: Connect to Host…” and select the connection
Now we will use the knowledge of the previous steps. Create the “.env” file with your secret constants, the “requirements.txt” file with the dependencies and the “bot.py” file with your existing bot’s code
To test it quickly we need a “.env” file with the “DISCORD_TOKEN” constant
A “requirements.txt” file like this one
And for the simplest bot code write this in the “bot.py” file
Go back to the terminal or use the integrated terminal in Visual Studio Code and install the requirements with the command
To test the bot write the command
You should see the “<Your bots name and id> is connected” message in the terminal and in Discord you should see the bot status as online
If you like to debug in Visual Studio Code to fix some bugs or to understand the logic, press F5 key in the IDE and select “Python File”
The IDE will enter debug mode allowing you to breakpoint the code and see the content of the variables
We are all set for this step.
If you encounter typos or something doesn’t work no more write me a comment and I will keep this guide updated. Last update June 28 2020.
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