E-Commerce Chatbots: Use Cases & Benefits Explained for 2024

By unity in AI News Posted November 15, 2023 Tags

All About Conversational AI in 2024: Why Is It Integral For Your Business?

They can be used for customer care and assistance and to automate appointment scheduling and payment processing operations. An example of an AI that can hold a complex conversation in action is a voice-to-text dictation tool that allows users to dictate their messages instead of typing them out. This can be especially helpful for people who have difficulty typing or need to transcribe large amounts of text quickly.

Healthcare companies are using conversational AI to improve patient care and increase revenue. Hospitals and medical practices can use these bots to provide patients with 24/7 access to their medical records, schedule appointments, get lab results, and more. This technique eventually gave way to the process of creating vectors, or sequences of numbers, out of words. This allowed engineers to take a bunch of data and condense it into numerical form, which can then be used to capture the semantics of a given statement or conversation. Conversational AI is a form of artificial intelligence that enables a dialogue between people and computers. Thanks to its rapid development, a world in which you can talk to your computer as if it were a real person is becoming something of a reality.

What is Conversational AI?

Here’s how brands big and small are using conversational AI-powered chatbots and virtual assistants on social media. For example, if a customer messages you on social media, asking for information on when an order will ship, the conversational AI chatbot will know how to respond. It will do this based on prior experience answering similar questions and because it understands which phrases tend to work best in response to shipping questions. IBM watsonx Assistant is a cloud-based AI chatbot that solves customer problems the first time. It provides your customers with fast, consistent and accurate answers across applications, devices or channels. With watsonx Assistant you can help customers avoid the frustration of long wait times while you reduce costs and churn, improve the customer and employee experience, and achieve 337% ROI over 3 years.

• Similarly, conversational AI can help resolve customer issues without them needing to speak to an agent.
• The time brackets are usually outlined during the discovery phase once the team knows the volume of work and the end goal.
• The technologies used in AI chatbots can also be used to enhance conventional voice assistants and virtual agents.
• Due to this, voice-based conversational AI can differentiate between a forged client’s voice and a genuine one, instantly identifying criminals and protecting client data from vishing.
• Click the link below to watch a free demo of Forethought in action, because when you see what it’s capable of, you’ll immediately think of ways it can benefit your own business.

IBM watsonx Assistant provides customers with fast, consistent and accurate answers across any application, device or channel. Since Conversational AI is dependent on collecting data to answer user queries, it is also vulnerable to privacy and security breaches. Developing conversational AI apps with high privacy and security standards and monitoring systems will help to build trust among end users, ultimately increasing chatbot usage over time. According to the United States Bureau of Labor Statistics, the average tenure of a support agent is only 2.6 years or lower in most cases.

Use Cases for sales

Natural Language Processing enables humans to speak as they normally would–using basic slang or abbreviations, expressing things colloquially and with emotions, or varying speech tones and speeds. But making Conversational AI a part of your business communications strategy feels daunting when you’re not sure what it is, how it works, and if it will truly benefit your customer base and employees. Here are two types of tools that are very useful to increase lead generation. For more information on expert development and deployment of Conversational AI applications and systems. Furthermore, Conversational Artificial Intelligence creates less work for employees—which enhances compliance efforts within regulated industries, such and financial institutions.

Emotions, tone and sarcasm all make it difficult for conversational AI to interpret intended user meaning and respond appropriately and accurately. When responding to a question, it cites its sources, so users can see how it develops its responses and explore other sites for more context. Bing Chat is compatible with Microsoft Edge, but it can be accessed on other browsers as an extension with a Microsoft account.

It enables conversation AI engines to understand human voice inputs, filter out background noise, use speech-to-text to deduce the query and simulate a human-like response. There are two types of ASR software – directed dialogue and natural language conversations. The AWS Solutions Library make it easy to set up chatbots and virtual assistants. You can build your conversational interface using generative AI from data collection to result delivery. Use the foundation model that best fits your needs inside a private, secure computing environment with your choice of training data.

Read more about https://www.metadialog.com/ here.

READ MORE

Automation in Banking and Finance AI and Robotic Process Automation

By unity in AI News Posted November 13, 2023 Tags

Automated Banking For The People

Today, many companies use automated underwriting platforms to calculate loan terms and insurance premiums for their new/existing customers. With AI and propensity modelling techniques, finance companies calculate risks based on the data from their existing customer base. It enables them to underwrite terms based on customer attributes and creditworthiness instead of being subjective about it.

• Many industries are transitioning towards the adoption of automation in end-to-end processes.
• Similar to any other industry, cost-saving is critical to the banking industry as well.
• Some Bank of America branches have become fully automated, with a single off-site banker available on FaceTime to respond to questions.

With the lack of resources, it becomes challenging for banks to respond to their customers on time. Consequently, not being able to meet your customer queries on time can negatively impact your bank’s reputation. An experienced business leader, heads the UK operations of Silver Touch Technologies Ltd. With 15 years of experience in the industry, he has set the track record of delivering transformation and revenue growth with SAP Solutions. We at Silver Touch Technologies believe that keeping our goals aligned with YOUR goal is what makes an RPA implementation successful.

Risk and compliance reporting

According to a McKinsey study, up to 25% of banking processes are expected to be automated in the next few years. Similarly, banking RPA software and services revenue is expected to reach a whopping $900 million by 2022. These indicators place RPA as an essential ingredient in the future of banking; banks must consider how strategic implementation of RPA could become the wind beneath their wings. Thanks to the virtual attendant robot’s full assistance, the bank staff can focus on providing the customer with the fast and highly customized service for which the bank is known. When robotic process automation (RPA) is combined with a case management system, human fraud investigators may concentrate on the circumstances surrounding alarms rather than spend their time manually filling out paperwork.

To meet these demands, RPA (Robotic Process Automation) has become an effective tool. It has taken forward the transition from service-through-labor to service-through-software. Get in touch with us to know how to transition your business to be at par with the world’s best with state of the art banking automation solutions.

Why Banks Embrace IT and Automation

Institutions that embrace this change have an excellent chance to succeed, while those who insist on remaining in the analog age will be left behind. Similar to any other industry, cost-saving is critical to the banking industry as well. Banks and financial institutions can look at saving around 25-50% of processing time and cost. With the banking fraud landscape expanding, banks are worried about strengthening their fraud detection mechanism. With the advent of the latest technology, banking frauds have only multiplied. Thus, it is next to impossible for banks to check every transaction to identify fraud patterns manually in real time.

… that enables banks and financial institutions to automate non-core banking processes without coding. Let’s explore in greater detail how banking automation is influencing the financial services sector. Facing competition from both traditional banks and fintech startups, these organizations are constantly striving to improve customer experience and often use automation to help with that.

Automation is fast becoming a strategic business imperative for banks seeking to innovate – whether through internal channels, acquisition or partnership. Automation is fast becoming a strategic business imperative for banks seeking to innovate[1] – whether through internal channels, acquisition or partnership. Both the speed at which you initially respond to the client and the total time it takes to resolve their issue are significant factors influencing the customer experience.

Banks in the UK have to mandatorily validate customer information and collect their identification documents. RPA in banking sector can collect, verify and even nudge customers to submit their KYC documents if pending. IA can also build credit risk models and identify a band of low credit risk for an applicant. Based on this, if the applicant qualifies for a higher loan, organizations can carry out upselling. Banks can use intelligent automation to create self-serve application intake processes for customers across various channels, including online, mobile, and in-branch.

Robotic process automation in finance: implementation tips

In the past few years, many banks have enhanced some of their customer-facing, front-end operations with automated digital solutions. Online banking, for example, offers consumers transparency and convenience. As it often happens when predicting the future, both customers and financial experts expressed high expectations as to the role technology would play in the evolution of banking services today. What we’ve now come to realize is that the mass adoption of new technologies is a slow process, due to economic, legal and societal hurdles, meaning that technological substitution often does not take place as expected. What we’ve foreseen 50 years ago or even 10 years ago might not stand true, even in the context of recent developments in artificial intelligence, blockchain technology or quantum computing.

However, today, organizations can invest in advanced reporting and analytics platforms to avoid this problem. For instance, with LeadSquared, you can set up dashboards/smart views to analyze the performance of their teams/products/regions, and much more in real-time. This helps leaders set up appropriate incentives, promote growth, and align your business with the market reality. Call/Contact Center Management is another use case for automation in the financial services industry due to the daily high volume of calls companies receive. With the more advanced HRMS solutions today, you can hire and pay salaries across continents without worrying about local compliance. Additionally, by implementing automation, your HR team can streamline and automate various tasks, such as paperwork and document management, scheduling orientations and training sessions, and communication with new hires.

How is Automation Used in the Banking Sector?

Number two, ATMs freed tellers from some of the basic tasks and enabled them to focus on more “relationship-building” efforts and complex  activities. In fact, ATMs also introduced new jobs such as armored couriers to resupply units and technology staff to monitor ATM networks. If you look at the banking industry today, it’s impossible to miss the major changes that the advances in technology have prompted over the last 50 years. If you would have asked a 1967 bank customer how they imagine their bank in 2017, they might have successfully predicted a highly automated financial institution.

A baby stroller and car seat automate its accounts payable validation process. The company has branches at various locations, and each one sends its financial documents in its own unique format, which differs from other departments. It is tedious to process all this manually and validate if the provided information is consistent with the bank’s statements. Bots will necessarily require some adjustments to comply with unexpected changes in processes. And continuing maintenance and support require a different set of RPA skills than development.

Banking automation is applied with the goals of increasing productivity, reducing costs and improving customer and employee experiences – all of which help banks stay ahead of the competition and win and retain customers. To get the most from your banking automation, start with a detailed plan, adopt simple-but-adequate user-friendly technology, and take the time to assess the results. In the right hands, automation technology can be the most affordable but beneficial investment you ever make. Banks and the financial services industry can now maintain large databases with varying structures, data models, and sources.

These new banking processes often include budgeting applications that assist the public with savings, investment software, and retirement information. Among many industries, the banking and finance industry is the one where employees have to spend too much time on manual processes.No; we aren’t just talking about document-related processes. Many aspects of loan approval, account opening, customer service etc., are manual or rules-based in nature.

Mihir Mistry is a highly experienced CTO at Kody Technolab, with over 16 years of expertise in software architecture and modern technologies such as Big Data, AI, and ML. He is passionate about sharing his knowledge with others to help them benefit. The Global Robotic Process Automation market size is $2.3B, and the BFSI sector holds the largest revenue share, accounting for 28.8%. According to the same report, 64% of CFOs from BFSI companies believe autonomous finance will become a reality within the next six years.

Often, back offices have thousands of people processing customer requests. At Maxima Consulting, our core competencies revolve around the current requirements of the financial services sector. BPA software can create a centralized network of information from which it pulls information about customers easily. With the help of machine learning, the system can extract information even from PDF documents. Capgemini suggested that the financial services industry could get up to $512 billion in new global revenue thanks to automation.

Read more about https://www.metadialog.com/ here.

READ MORE

Image Recognition with AITensorFlow

By unity in AI News Posted November 1, 2023 Tags

A beginners guide to AI: Computer vision and image recognition

They can check if their treatment is functioning properly or not, and they can even recognize the age of certain bones. Image Recognition is indeed one of the major topics covered by this field of Computer Science. It allows us to extract as much information as we want from a picture and has the ability to be applied to multiple areas of businesses. When all the data has been analyzed and gathered in a feature map, an activation layer is applied. This one is meant to simplify the results, allowing the algorithm to process them more rapidly.

It is a useful tool for both the buy-side and sell-side of advertising, benefiting advertisers, publishers, and agencies. With Verity’s advanced image recognition and contextual targeting capabilities, users can achieve better accuracy, engagement, and ROI in their ad campaigns. This is done by providing a feed dictionary in which the batch of training data is assigned to the placeholders we defined earlier. We don’t need to restate what the model needs to do in order to be able to make a parameter update.

AI can instantly detect people, products & backgrounds in the images

R-CNN belongs to a family of machine learning models for computer vision, specifically object detection, whereas YOLO is a well-known real-time object detection algorithm. A computer vision algorithm works just as an image recognition algorithm does, by using machine learning & deep learning algorithms to detect objects in an image by analyzing every individual pixel in an image. The working of a computer vision algorithm can be summed up in the following steps. Computer Vision is a branch in modern artificial intelligence that allows computers to identify or recognize patterns or objects in digital media including images & videos. Computer Vision models can analyze an image to recognize or classify an object within an image, and also react to those objects. Once the images have been labeled, they will be fed to the neural networks for training on the images.

Home Security has become a huge preoccupation for people as well as Insurance Companies. They started to install cameras and security alarms all over their homes and surrounding areas. Most of the time, it is used to show the Police or the Insurance Company that a thief indeed broke into the house and robbed something. On another note, CCTV cameras are more and more installed in big cities to spot incivilities and vandalism for instance.

Quality assurance

Identification is the second step and involves using the extracted features to identify an image. This can be done by comparing the extracted features with a database of known images. Medical images are the fastest-growing data source in the healthcare industry at the moment. AI image recognition enables healthcare providers to amplify image processing capacity and helps doctors improve the accuracy of diagnostics.

The minimum number of images necessary for an effective training phase is 200. When installing Kili, you will be able to annotate the images from an image dataset and create the various categories you will need. For example, if Pepsico inputs photos of their cooler doors and shelves full of product, an image recognition system would be able to identify every bottle or case of Pepsi that it recognizes. This then allows the machine to learn more specifics about that object using deep learning.

Step 1: Extraction of Pixel Features of an Image

Usually, the labeling of the training data is the main distinction between the three training approaches. Image recognition, in the context of machine vision, is the ability of software to identify objects, places, people, writing and actions in digital images. Computers can use machine vision technologies in combination with a camera and artificial intelligence (AI) software to achieve image recognition.

Read more about https://www.metadialog.com/ here.

READ MORE