aichannel, Author at AI Channel

May 28

Cloud migration for financial services: 5 benefits of making the switch

By aichannel AI, Cloud, UCaaS

What are the reasons financial institutions are moving to the cloud?

Several catalysts have accelerated the move to the cloud in financial services, as part of a broader digital transformation. As a result of the global pandemic, employees became geographically dispersed and needed digital connectivity. Additionally, customer markets became more digitally inclined.

Throughout 2022, rising inflation led to interest rate hikes, which can be good for some financial institutions (banks and credit unions) but challenging for others (mortgage lenders). In addition to rising borrowing costs, uncertain economic conditions increase business risks.

As a result, financial services companies are increasingly using advanced technologies to improve customer experience, improve risk management, and increase operational efficiency.

In spite of reduced IT budgets, financial services organizations are investing in cloud technologies to increase agility and future-proof their operations.

Top Five Benefits of Cloud Migration for Financial Services

1. Productivity is increased

Agents can easily move interactions with customers from one channel to another with UCaaS, and dispersed work teams are able to hold planned and spontaneous meetings without any hassle.

In addition to spurring product & service innovation and employee productivity gains, UCaaS maintains low IT costs and in-house management requirements. Because of the reduced demand for hardware and on-premise system infrastructure, an organization can significantly reduce its physical footprint.

2. Enhancing the customer and employee experience

It has always been a competitive industry, but fintech has made it even more so. The increase in competition emphasizes the importance of maximizing customer and employee experiences. Customers must be attracted and retained to sustain revenue and profits, especially in tough economic times. To ensure an optimized customer experience, it is necessary to retain talented employees.

Despite the focus on cost controls, organizations continue to invest in transformative technology, such as:

In addition to providing real-time data, analytics, and communication tools, cloud systems provide employees with omnichannel flexibility and enable a seamless omnichannel customer experience.

A key component of cost efficiency and employee productivity is automation of manual processes.

API based solution integrations: APIs enable companies to monetize data, provide valuable financial product and solution partnerships, and provide rich customer experiences across all channels.

As an industry, financial services added many tech tools from 2020-2022, and this trend will continue in 2023. The goal is to standardize common business processes and simplify for ease and efficiency.

3. Artificial intelligence: harnessing its power

A recent Accenture report predicts a 38 percent increase in the profitability of global firms by 2035 thanks to artificial intelligence (AI). This technology will have a significant impact on various industries, including financial services. In fact, Accenture estimates that financial services will experience a 4.3-percent growth rate driven by AI use, placing it third among all sectors studied. The only industries expected to grow at a higher rate are information and communication at 4.7 percent and manufacturing at 4.4 percent. It’s worth noting that the growth in communication and financial services are closely linked.

Moreover, Accenture evaluated the impact of artificial intelligence on productivity for companies. By the year 2035, firms in the United States will be 35 percent more productive than those without. All developed countries with the necessary technology infrastructure are experiencing similar levels of impact. As well as benefitting employers, these productivity boosts allow workers to maximize their potential.

Increasingly, banks, insurers, and investment firms use AI to gather research, leverage deep learning, and personalize customer experiences.

4. Integrations and custom APIs streamline workflows

Among the ways digital expansion optimizes cost efficiency is by integrating digital solutions rather than having them exist in silos.

Digital solutions require access to open architectures and communication APIs. Open architectures give firms the greatest flexibility in implementing digital solutions based on their existing infrastructure and strategies. By integrating communication APIs with popular cloud tools, UCaaS solutions allow businesses to maximize their past investments and create new opportunities for innovation and growth by leveraging communication APIs.

To streamline customer management, RingCentral integrates seamlessly with Salesforce, so your team can make, transfer, and record calls from the Salesforce app:

5. Digital maturity is a competitive advantage

Digital champions, according to Deloitte’s Digital Banking Maturity 2022 report, have several advantages over other financial services organizations.3 As defined by Deloitte, digital champions are those that:

Provide a wide range of customer-relevant functions (e.g., omnichannel communications).

Provide a compelling user experience
Identify key digital trends
Practice leading market practices

The digital champions develop functionalities that build and expand customer relationships based on these criteria. Communication technology plays a major role in digital champion organizations having a competitive advantage. Digital champions, for example, offer the following advantages compared to less digitally mature organizations, according to the Deloitte report:

Access to channels is 1.5x more convenient
Onboarding functionality for new users is 1.3x increased
A 1.5x increase in customer support functionality
Investment services with 2.5x the functionality
Cross-selling functionality 1.8x greater
Optimize your organization’s productivity with cloud migration for financial services

In financial services firms with tight budgets, cloud communications are essential. Innovation leads to fewer manual processes, increased accuracy, and better financial decisions. In addition to its cost, flexibility, and security advantages, an all-in-one cloud communications system supports the best user experience strategies.

Financial services organizations can achieve these objectives with RingCentral, a comprehensive, cloud-based communications system that offers seamless, cross-channel communication. It integrates with many popular applications banks already use and features the most advanced security protocols

May 20

Love0

The Difference Between Generative AI and Conversational AI

By aichannel AI

What’s the difference between conversational AI and generative AI and which solution is right for your business? There has been a meteoric rise of LLMs (large language models) and generative AI in the past couple of years.

In the meantime, machine learning algorithms, natural language processing solutions, and neural networks are becoming increasingly sophisticated. Business leaders, contact centres, and modern teams should pay attention to two flavors of artificial intelligence in this vast landscape: generative AI and conversational AI.

Understanding the key differences between these two solutions will ensure you’re investing in cutting-edge technology that’s right for your business, even though they might work together.

Let’s examine conversational AI and generative AI in more detail.

Conversational AI: what is it?

The conversational AI subset of artificial intelligence allows bots and computers to mimic human conversation and understand user input. This is a tool that allows people to interact with machines as though they were speaking to another person (without writing code).

You’ve probably encountered conversational AI in a chatbot on a website, a voice bot in an IVR system, or a self-help tool like Slackbot.

Natural language processing is used to interpret human input. Databases are also used to determine how to respond to a user via natural language generation. However, some bots are more advanced than others.

In addition, some solutions can draw insights from customer profiles and CRM systems to personalize the user experience based on the tone of voice or the words used by the user.

AI tools that simulate human interactions cannot create unique responses, as they are trained on massive datasets and insights into human dialogue, and they draw their responses from a predefined pool.

Conversational AI: What Companies Can Do

Conversational AI has a significant impact on customer experience. It can enhance virtually every customer-facing operation, from answering customer questions to troubleshooting product problems.

Several contact centers integrate conversational AI tools into their platforms, which can help:It can help sales professionals gather and qualify leads, analyze market trends, and even recommend products to customers, as well as empower them to complete transactions on their own.

Marketing teams: Conversational AI tools provide insights into customer trends, preferences, and journeys. They can also dynamically share marketing content with customers.

Conversational AI solutions can also provide valuable insights into customer needs, preferences, and trends. In the customer service landscape, these solutions allow teams to offer 24/7 service to customers in the language of their choice.

Various industries have benefited from conversational AI, including retail, banking, and healthcare. In retail, for example, it can improve 24/7 order processing and customer engagement, in banking, it can simplify transactional tasks, and in healthcare, it can streamline patient care.

Conversational AI can also be found in smart speakers and personal assistants. Apps like Siri, Alexa, and Google Assistant all use conversational AI algorithms.

Conversational AI: Benefits and Challenge

The pros are:

Enhances and delivers omnichannel customer service well.
Provides 24/7 customer support.
Enhances productivity and efficiency of teams to save money.
Guides business decisions with valuable insights.
Provides continuous support and guidance to enhance team performance.

The cons are:

The ability to process complex queries (without regular training) is limited.
Specific linguistic nuances are difficult to understand.
Data compliance risks.

t’s important to note that both generative AI and conversational AI have pros and cons. Conversational AI can empower teams to deliver exceptional customer service 24/7 across any channel.

Besides improving operational efficiency, it can improve agent productivity by automating routine and recurrent tasks (like summarizing and transcribing text). Additionally, conversational AI tools can help businesses make intelligent decisions and optimise workplace processes by giving them insights they need.

It is also true that there can be challenges. For instance, most conversational AI solutions are capable of handling routine requests, but struggle with complex queries. In order to deal with more complex inquiries, conversational AI tools need constant training and fine-tuning.

Customer experiences can be hampered by solutions that don’t understand finer linguistic nuances, such as satire, humour, or accents. Moreover, conversational tools interact with customer data, so compliance with data privacy regulations is always a risk, just like most forms of artificial intelligence

How does Generative AI work?

AI that uses deep learning and neural networks to create new content, such as text, images, and sounds, is known as generative AI. Some of these tools can create content based on the prompts you give, and some respond to text, video, audio, and images in a multimodal manner.

There is a confusion here between generative AI and conversational AI. In fact, generative AI is often referred to as an updated version of conversational AI. In fact, natural language processing and generation tools are still used by apps like ChatGPT and Microsoft Copilot to facilitate human-bot interactions.

Generated AI, unlike conversational AI, can create original content rather than just responding to questions based on what it finds in its database.

Neural networks are used by generative AI tools to identify patterns and structures in their training data, then generate new content from those patterns. As an example, Microsoft Copilot will generate a list of dates for your next team meeting based on your meeting habits, schedules, and shared calendars.

Generative AI: Benefits and Challenges

The pros are:

Enhances employee creativity and productivity.
Strengthens team connections and enhances collaboration.
Analyzes growth opportunities and provides actionable insights.
Provides personalized 24/7 service to transform the customer experience.
Produces original, unique content in a variety of formats.

The cons are:

Ethics and transparency issues (collection and use of data).
Copyright and IP infringement risks.
Incorrect or biased responses are caused by AI hallucinations.

Just like conversational AI, generative AI has pros and cons. For most professionals, the best advantage of this type of intelligence is that it enhances creativity and productivity. Using these tools, teams can generate original ideas and content that inspires and motivates them.

Additionally, it can enhance collaboration by summarizing meetings in seconds, providing action items for each team member, creating agendas, and translating content in real time. In addition to automating the follow-up process, Microsoft Copilot in Outlook can suggest the best time to call colleagues after an event or conversation.

Aside from boosting customer experiences, generative AI can pinpoint trends, deliver personalized and unique responses to questions, and boost customer satisfaction. It can increase your company’s revenue by enabling proactive product recommendations, identifying opportunities for product optimization, and centralizing market research.

Customer experiences can be hampered by solutions that don’t understand finer linguistic nuances, such as satire, humor, or accents. Moreover, conversational tools interact with customer data, so compliance with data privacy regulations is always a risk, just like most forms of artificial intelligence