How ICICI Lombard Leverages AI and Analytics For Automated Processing Of Insurance Claims

In the last decade, insurance providers have had to move away from their traditional core systems towards more flexible, cloud-based applications that have auto-scaling capabilities. This has been done to help consumers have a seamless insurance purchase and a faster and better consumer experience. In this context, the insurance industry is leveraging several technologies, including digital platforms, robotic process automation (RPA) for quick policy issuance and servicing, AI and ML for claims and policy servicing, and bots powered by Natural Language Processing techniques.

Read more: https://analyticsindiamag.com/how-icici-lombard-leverages-ai-and-analytics-for-automated-processing-of-insurance-claims/

#artificial-intelligence #machine-learning #analytics #insurance

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How ICICI Lombard Leverages AI and Analytics For Automated Processing Of Insurance Claims

How ICICI Lombard Leverages AI and Analytics For Automated Processing Of Insurance Claims

In the last decade, insurance providers have had to move away from their traditional core systems towards more flexible, cloud-based applications that have auto-scaling capabilities. This has been done to help consumers have a seamless insurance purchase and a faster and better consumer experience. In this context, the insurance industry is leveraging several technologies, including digital platforms, robotic process automation (RPA) for quick policy issuance and servicing, AI and ML for claims and policy servicing, and bots powered by Natural Language Processing techniques.

Read more: https://analyticsindiamag.com/how-icici-lombard-leverages-ai-and-analytics-for-automated-processing-of-insurance-claims/

#artificial-intelligence #machine-learning #analytics #insurance

Otho  Hagenes

Otho Hagenes

1619511840

Making Sales More Efficient: Lead Qualification Using AI

If you were to ask any organization today, you would learn that they are all becoming reliant on Artificial Intelligence Solutions and using AI to digitally transform in order to bring their organizations into the new age. AI is no longer a new concept, instead, with the technological advancements that are being made in the realm of AI, it has become a much-needed business facet.

AI has become easier to use and implement than ever before, and every business is applying AI solutions to their processes. Organizations have begun to base their digital transformation strategies around AI and the way in which they conduct their business. One of these business processes that AI has helped transform is lead qualifications.

#ai-solutions-development #artificial-intelligence #future-of-artificial-intellige #ai #ai-applications #ai-trends #future-of-ai #ai-revolution

Shardul Bhatt

Shardul Bhatt

1624620512

Unlocking the Power of Data And AI for Businesses

The latest reports show that the Artificial Intelligence market was worth $40.2 billion in 2020. Experts attribute this growth to the increasing business adoption of AI. Data is the biggest asset that a company has in 2021. A comprehensive data strategy ensures the company can derive valuable insights that enable better decision-making.

What is the role of AI in business? There are countless examples of AI in business for forecasting and predicting values. For example, AI solutions enable financial companies to oversee stock price fluctuations and take advantage of trading opportunities. MetaStock is one such solution that provides analytical tools based on AI and data to predict stock prices.

Another approach of data and analytics that companies take is to forecast their budget, expenses, and revenues. It involves using historical data and transactional patterns to identify a company’s budget. Big data provides solutions to specific business problems through Artificial Intelligence models.

This article will focus on the following topics – 

  • AI capabilities in business
  • What is intelligent analytics?
  • Latest trends in data analytics
  • Business cases for AI

Let’s have a look at these topics in detail.

AI Capabilities in Business 

Harvard Business Review suggests that AI can do three significant things for your business. It can – 

  1. Automate business processes
  2. Gain valuable insights
  3. Improve customer engagement

When people ask, “Why is data important for AI,” the answer is that all AI solutions require data. The foundation of AI analytics is historical or real-time data. With the help of data, AI can deliver the above three capabilities.

AI enables business process automation with the help of Robotic Process Automation. RPA bots can work with triggers, while smart bots can identify things that need to stay or eliminate during automation. 

The biggest role of AI in business is to generate and deliver valuable insights. When there’s tons of data, patterns are bound to emerge. The power of data in business is brought to its best use when companies can use any kind of data to improve business results. It usually happens by finding opportunities that are only seen through data and analytics. 

Artificial Intelligence drives customer experience by learning from the environment. Smart bots and AI algorithms deliver better customer service by understanding the conversation in real-time. Several companies use AI customer experience bots to enhance service delivery to their audience.

Read More: A CTO’s guide for AI Implementation

What is Intelligent Analytics?

Intelligent analytics is a branch of Artificial Intelligence. Companies use intelligent data and analytics to uncover meaningful insights. The purpose is to derive value from collected business data.

INSIGHTS – that’s the keyword here. Intelligent analytics is a part of the overall data strategy. It showcases the business that even their top management might sometimes miss. It includes new market segment opportunities, product differentiation parameters, honest product feedback, actual growth rate or products, expense and income status for a particular time period, and much more. 

There are three different types of intelligent analytics – 

  • Descriptive analytics
    It focuses on using historical and real-time data to identify what happened during a time period. Examples of AI in business for descriptive analytics include understanding the new customers acquired, sales during a period, and more.
  • Predictive analytics
    It focuses on predicting future values for the company. The Machine Learning algorithm uses past values and identifies values that are likely to occur in the future. Examples include predicting the budget for the next year, identifying the number of potential customers, and more.
  • Prescriptive analytics
    It focuses on determining the right path that companies should take based on the insights generated from data. It highlights the action steps that will bring the desired results. Examples include price changes based on the historical demand of a product. 

Latest Trends for Data Analytics

As companies move towards modern applications of data analytics, everyone asks the same questions – “How can I use AI in my business?”. We will address the answer in the next section. For now, let’s understand the latest trends in data analytics that make AI possible for businesses. 

  1. AI SaaS
    Data solutions are increasingly available as Software as a Service product. Companies can use off-the-shelf software to identify patterns by inputting data in different columns. There are software-based on multiple business functions, while some are common for all processes. However, if you want an enterprise-grade analytical tool, custom AI solutions are more effective.
  2. Natural Language Processing
    AI machines are now learning on their own. More and more companies are experimenting with unstructured data through natural language processing. Intelligent bots now have conversations as real as humans do. It brings a new age of AI systems that can interact using emotions, feelings, and everything that humans have.
  3. Extended Reality
    Data analytics combined with extended reality is generating unfathomable results. Through AR and VR, companies are creating virtual simulations – but combining it with the AI capabilities for predicting future environments, opportunities and making plans to understand how to solve future challenges. Extended reality delivers an AI customer experience that highly improves the customer satisfaction rate.
  4. Interactive Visualizations
    Intelligent analytics is simplifying data representation for companies. There are interactive dashboards through which all stakeholders can better understand the data. It gives a personalized view of all the data points and showcases how it is performing. Interactive visualizations are useful for identifying visual patterns that might be missed otherwise.

Business Cases for AI

Today, there are use cases of AI in almost every business process. Even in the manufacturing sector, intelligent data analytics can help build systems that can identify the maintenance schedule of equipment – keeping it safe from breakdowns. 

There are plenty of applications that are common in business. We will talk about these popular business use cases of AI, based on different functions –

  • Customer Service
    Sentiment analysis is a useful application of data analytics in business. Companies can identify what customers are saying about their product. It also includes:

    – Customer responses
    – Call forwarding
    – Call classification
    – Intelligent routing
  • Marketing
    Marketing analytics is the most significant use. It helps to determine the results that we can achieve from the marketing budget. It also includes:

    – Personalized marketing
    – Intelligent content
    – Contextual marketing
    – Budget forecasting
  • Sales
    The sales team can identify the future sales potential by analyzing the historical data. It helps to find areas where activities need to be improved. It also includes:

    – Predictive sales
    – Sales chatbot
    – Appointment scheduling
    – Lead management
  • Operations
    The role of AI in business operations is increasing dramatically. Companies use automation and data analytics to drive valuable insights. It also includes:

    – Data mining
    – Predictive maintenance
    – Operational analytics
    – Inventory optimization

Apart from this, there are various business cases in different domains like healthcare, FinTech, HR, Insurance. In every domain and area of business, AI is showing significant results.

Checkout: The Future of Artificial Intelligence Services for Business

Bottom Line

Artificial Intelligence is transforming how companies used to work. Today, intelligent analytics is a business priority. Since data is the biggest asset they have, companies want to leverage AI and derive valuable insights from that data. That’s why more and more businesses are now hiring AI developers to build intelligent solutions.

BoTree Technologies is a leading Artificial Intelligence and Data Analytics company in the US, India, and Singapore.

Contact us today to understand how we can build AI capabilities for your business.

 

Source: https://www.botreetechnologies.com/blog/unlocking-the-power-of-data-and-ai-for-businesses/

#artificial intelligence #ai #ai solutions #ai in business #ai capabilities in business #robotic process automation

Origin Scale

Origin Scale

1620805745

Automation Management System

Want to try automated inventory management system for small businesses? Originscale automation software automate your data flow across orders, inventory, and purchasing. TRY FOR FREE

#automation #automation software #automated inventory management #automated inventory management system #automation management system #inventory automation

Angela  Dickens

Angela Dickens

1596909720

Survey Highlights the Need for Automation to Manage Security Alerts

Twice as many security teams with high levels of automation resolve most or all alerts the same day compared to those with lower levels of automation .

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Continuous intelligence (CI) is essential in situations where actionable insights must be derived from real-time data in milliseconds to seconds. A prime use case for CI is decision support and analysis automation of security alerts. That’s been the case for a while. But the need for automated help is now ever-more critical with cyberattacks on the rise and corporate boundaries being pushed into every employee’s home due to the pandemic.

See also: Using Continuous Intelligence for Decision Support and Automation

A recent Dimensional Research survey, sponsored by Sumo Logic, put the issues into perspective. The survey included 427 IT security stakeholders in organizations with at least 1,000 employees. It found that IT security staff simply cannot keep up with the volume of security alerts organizations receive every day.

Specifically, 56% of companies with more than 10,000 employees must deal with more than 1,000 security alerts per day. Most companies have seen increases in security alerts. Seventy percent of the companies surveyed have seen the volume of security alerts more than double in the past five years.

The challenges are likely to get exacerbated by current work conditions. “You increase the attach surface due to COVID,” said Greg Martin, General Manager of the Security Business Unit at Sumo Logic.

He noted that you have workers and executives using their computers on the same networks as their families. This potentially exposes secure systems to vulnerabilities. “You’re pouring a clean glass of water into a dirty glass of water,” he said.

Overwhelmed with Alerts

Most respondents, 93% of the companies, said they could not address all the security alerts they receive on the same day. And 83% said their security staff experiences alert fatigue.

Such a situation is doubly bad. Lacking the bandwidth, security staff can only do their best in the time available. Certainly, they would focus most of their energy on the highest-level alerts. But therein lies a problem.

Ignoring attacks classified as low-level because there is not enough time or staffing power to get to them opens companies to problems. The reason: Many hackers use compounded and advanced persistent threat (APT) attacks. Essentially, compounded attacks use multiple, small, and less detectable attacks over time. Such an attack might start with a phishing attempt. The result might be the installation of malware or the stealing of credentials. Similarly, an APT attack would have the hacker gains access to a system and remain there for an extended period of time without being detected.

#analytics #streaming analytics #event processing #automation #security challenges #data analytic