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The AI Advantage: How artificial intelligence is revolutionising commercial finance

960 640 Stuart O'Brien

Artificial intelligence (AI) is everywhere you look, and the financial services sector is no different. Banks and other financial firms like hedge funds were some of the first institutions to adopt artificial intelligence at a corporate level, while new technological advances and applications mean that AI usage is more widespread than ever.

These new applications promise a myriad of benefits for the firms that adopt AI and their customers, who are interacting with the technology in new ways and opening the door to further possibilities. But how exactly is AI being used to transform the world of commercial finance? Anglo Scottish Finance takes a look…

Embracing the future?

For those working within financial services, the outlook on AI is certainly mixed. Generative AI – forms of artificial intelligence capable of generating their own image, text or other forms of media – are typically viewed with caution. 45% of people working in financial services view generative AI as a friend, though a further 40% view it as a foe.

Despite this mixed view, 77% of bankers believe that unlocking maximum value from AI technologies will be the difference between successful and unsuccessful banks. Its value cannot be understated.

There are, of course, concerns around employment: 73% of financial services executives believe that generative AI will take their jobs. Thankfully, those aged 25–34 – who will largely be driving AI adoption forward – feel markedly more positive about AI.

Those concerned about redundancies should not fear – for the present day, at least. At the moment, AI is best used as a supportive, augmentative tool, utilising human input to maximise the tool’s potential.

Tackling financial crime

Banks spend nearly £219.7bn each year on tackling financial crime. It’s a difficult – and often thankless – task, given the sheer number of transactions that must be monitored to weed out the fraudulent ones. International collaboration might be required, and red tape raises further barriers to identifying and preventing these transactions in time.

AI’s ability to digest and analyse huge datasets means it can change the outlook for anti-fraud teams, who can now monitor more information than ever before. For example, high street bank TSB has been utilising machine learning to provide every individual transaction with a score based on how likely is to be fraudulent within milliseconds.

The bank estimates a 20% reduction in push payment fraud – where users are convinced to send money to people pretending to be someone else – as a result of the technology.

A human touch is needed here, too – predictive AI might be able to identify spending patterns and catch fraudulent transactions before they happen, but a human understanding of why a transaction might have taken place in a certain way is necessary to interrogate individual payments on a case-by-case basis.

Precision forecasting

AI advancements in recent years have enabled huge improvements in financial forecasting. Given the increasingly volatile nature of the competitive landscape, real-time updates to your forecasting can be the difference between getting ahead of the game and being left behind.

Machine-learnt algorithms can provide automated forecasting that continuously adapts projections, aggregating massive datasets from a range of sources and in a range of mediums. These can be compared to industry benchmarks or competitor performance to ensure that your firm is on track according to any of your KPIs.

And, of course, as time passes and a growing amount of data is entered, the AI’s predictions will become increasingly accurate. When used in this context, it may be able to identify the real driving factors behind a business’ revenue. In one case, a global business found that units sold and sale price, traditional indicators of high revenue, had far less impact on its overall profit and loss than expected.

Stuart Wilkie, Head of Commercial Finance at Anglo Scottish, comments: “As machine learning becomes more and more accurate, there’s essentially no limit to the predictions artificial intelligence may be able to make.

“Given that high-quality predictive AIs are a reasonably new phenomenon, we can expect forecasting to become more accurate, span longer periods and account for a wider range of events as we continue to feed large-scale datasets through it.”

Investment insight

Given modern AI’s surgical approach to forecasting and its ability to pull from a wide range of different data sources, it’s unsurprising that AI is being used to predict the best-performing stocks to invest in.

In fact, a recent study found that 71% of UK investors would trust AI to recommend products for their portfolio – an 8% rise from 2022. In the US, 45% of investors using tips website The Motley Fool said they’d be comfortable investing based on ChatGPT’s advice and nothing else.

Investment advisors can benefit from machine learning tools’ ability to quickly analyse a portfolio and identify areas of risk. In line with identified risk areas, they can design a newly diversified portfolio based on each customer’s strategic goals, choosing the perfect blend of cash and ETF investments.

Customer service

AI’s ability to handle menial, repetitive queries with greater efficiency than its human counterparts has led to the improvement of customer support chatbots. And, thanks to advancements in natural language processing (NLP), the branch of AI concerned with giving computers the ability to understand text and speech in the same way we can, chatbots are providing a better service than ever before.

Let’s face it, we’d all rather have a human operative deal with our queries – but conversational AI is now able to handle simple, one-size-fits-all queries with ease. In the event of a more complex issue, they’ll send you through to a human customer support employee.

With 79% of financial services leaders aware that a personalised experience increases customer retention, the use of chatbots for standardised tasks frees up manpower to personally deal with more important issues. The bank benefits from increased efficiency, and the end users benefit from more readily available customer service for complex enquiries.

Managing, monitoring and improving AI use

Given the speed at which technological advances regarding AI are taking place, it’s important that businesses using AI understand its potential implications. The British government recently hosted the Bletchley Summit, during which 28 governments from around the world – including China, the EU and the US – agreed to work together on AI safety research. For now, however, there is little in the way of international legislation.

The onus therefore lies with the businesses using AI to manage the way in which they implement it. Long-term strategies are vital in managing AI usage at the corporate level, but as of early 2023, 57% of businesses are currently taking a reactive approach to artificial intelligence.

McKinsey, one of the leading adopters of AI at a corporate level, set out a 66-page document in 2021 with a roadmap to the “AI Bank of the Future.” The introduction extolls the importance of “formulating the organisation’s strategic goals for the AI-enabled digital age, and [evaluating] how AI technologies can support these goals.”

Wilkie comments: “AI adoption can have an almost instant impact upon a financial organisation’s operating practices, and by proxy, its bottom line. With that in mind, it can be tempting to rush through AI integration at various levels of the business.

“However, a considered approach is utterly vital. Understanding how AI fits into your firm’s long-term strategy enables deeper interrogation of your AI usage and ultimately leads to safer and more sustainable use of artificial intelligence. By creating a detailed AI strategy, you can also futureproof your business against any legislative changes which will take place in the coming years.”

One-Third of interactions with GenAI services will use action models & autonomous agents for task completion

960 640 Stuart O'Brien

One-third of interactions with generative AI (GenAI) services will use action models and autonomous agents for task completion by 2028.

Autonomous agents are combined systems that achieve defined goals without repeated human intervention, using a variety of AI techniques to make decisions and generate outputs. They have the potential to learn from their environment and improve over time, enabling them to handle complex tasks.

“In the future, human interactions with GenAI may evolve from users prompting large language models (LLMs) to users interfacing directly with autonomous intent-driven agents, which could allow for a higher degree of autonomy and much better alignment with human goals,” said Arun Chandrasekaran, Distinguished VP Analyst at Gartner.

Autonomous Agents Will Impact Several Business Sectors
Autonomous agents can perform a variety of tasks, such as chaining different types of models, verifying the output of a model before inputting into another model and running in a continuous loop to process streaming inputs. These tasks can translate into capabilities such as accessing the internet and using applications, controlling model output and automating complex business processes based on human intent.

“Autonomous agents can reduce the need for human intervention when interacting with LLMs and reduce the burden on business users across many sectors, as they are able to spend less time on advanced prompt engineering,” said Chandrasekaran.

Autonomous agents will have an impact across several sectors:

  • Healthcare: Autonomous agents can help medical professionals in areas such as disease diagnostics, treatment planning and patient care.
  • Education: Autonomous agents can offer personalized learning experiences and adapt teaching methods to the needs of individual students.
  • Gaming: Autonomous agents can observe and interact with human players and provide more immersive and realistic experiences.
  • Insurance: Autonomous customer service apps can handle most policyholder interactions through voice and text, and can assist with claims, fraud, medical service, policy and repair systems. They can have a dramatic impact on resolution, with responses and actions taking minutes rather than days or weeks.

Use Clear Objective Functions as the Foundation for Autonomous Agents

“Autonomous agents need a clear objective function so that their behaviors can be controlled in a meaningful way to deliver value,” said Chandrasekaran. “The tasks autonomous agents can perform, such as verifying the output of a model before inputting into another model, has the ability to control model output and automate complex business processes based on human intent. But this can only be achieved with a clear objective function.”

In order to achieve this, Gartner suggests that organizations:

  • Identify use cases in which action models and autonomous agents can add value by reducing the amount of human effort and skill needed.
  • Build an architecture to enable autonomous agents to thrive. Do so by providing tool integration and access to knowledge repositories and long-term memory, enabling agents to demonstrate expanded reasoning and expertise.
  • Acknowledge that action models and autonomous agents aren’t a substitute for prompt engineering — their ultimate potential remains tied to the quality of the prompts they receive.
  • Balance between autonomy and control through extended pilots and rigorous agent monitoring.

GUIDE: How to use AI to personalise customer service

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You may be looking to scale your customer service with AI, but hesitant about how AI may present your organisation to your customers and prospects. As a result, we wanted to share this guide from Freshworks, to investigate how you can adopt the right AI workflows to make teams more productive, reduce wait times for customers and also communicate with customers in the right way, the first time of asking.

The guide explores how you can use AI-powered omnichannel solutions to deliver personalised support by:

  • Supporting customers across channels of their choice with chatbots
  • Ensuring that your customers have a conversational experience, even with automated responses, using generative AI
  • Empowering agents with the necessary AI-powered tools and resources for contextual support

Furthermore, the link from Freshworks contains a great kit for Customer Support leaders who are looking at their planning and priority investment areas in 2024. Use the navigation on the left hand side to review the materials.

Take a look at the insights, here!

Want to hear more? Freshworks will be exhibiting at the Contact Centre & Customer Services Summit on the 29th and 30th April, 2024.

Thank you,

Team Freshworks

ANALYTICS MONTH: From enhanced customer insights to better QA – How AI and machine learning are impacting contact centre analytics

960 640 Stuart O'Brien

Artificial Intelligence (AI) and Machine Learning (ML) are pioneering a significant analytics revolution for contact centres. These advanced technologies are transforming the way contact centres operate, offering unprecedented insights into customer behaviour, enhancing service quality, and driving operational efficiency. Here’s an exploration of how AI and ML are at the forefront of the analytics revolution in contact centres…

Enhanced Customer Insights: At the heart of AI and ML capabilities is the ability to process and analyse vast amounts of data at incredible speeds. Contact centers generate a wealth of data from every interaction, whether it’s through voice calls, chat, email, or social media. AI and ML algorithms can sift through this data to extract valuable insights about customer preferences, behavior patterns, and sentiment. This deep understanding enables contact centers to tailor their services to meet customer needs more effectively, improving satisfaction and loyalty.

Predictive Analytics for Personalised Experiences: One of the most powerful applications of AI and ML in contact centres is predictive analytics. By analyzing past interactions and outcomes, these technologies can predict future customer inquiries or issues before they arise. This predictive capability allows contact centres to proactively address customer needs, personalise interactions, and even anticipate and prevent potential problems. The result is a more seamless, efficient, and personalised customer experience.

Optimising Workforce Management: AI and ML are also revolutionising workforce management in contact centres. These technologies can analyze patterns in call volumes, chat traffic, and other interactions to predict peak periods and optimal staffing levels. This enables contact centers to allocate resources more efficiently, ensuring that they have the right number of agents available at the right times. Additionally, AI-driven training programs can identify skill gaps among agents and customize training content to address these needs, enhancing overall performance.

Improving Quality Assurance: Traditional quality assurance in contact centres has been labor-intensive, with managers manually reviewing a small sample of interactions. AI and ML have changed the game by enabling the automated analysis of every interaction. This comprehensive approach ensures consistent quality and compliance, identifies areas for improvement, and highlights exemplary interactions for training purposes. Moreover, real-time feedback from AI systems can guide agents during interactions, helping them to meet quality standards consistently.

Future Trends: Looking ahead, the integration of AI and ML in contact centers is set to deepen. We can expect to see more advanced natural language processing capabilities, enabling even more nuanced understanding of customer sentiment and intent. Additionally, the integration of AI and ML with other technologies, such as Internet of Things (IoT) devices, could open up new avenues for customer support and engagement.

AI and ML are at the forefront of the analytics revolution in contact centres, driving enhancements in customer insights, personalised experiences, workforce management, and quality assurance. As these technologies continue to evolve, they promise to further transform the contact centre landscape, setting new standards for customer service excellence.

Are you looking for Analytics solutions for your contact centres? The Contact Centre & Customer Services Summit can help!

Photo by Kevin Ku on Unsplash

AI set to impact customer experience in financial services

960 640 Stuart O'Brien

New research reveals that the majority (52%) of financial services employees feel positive or very positive about the growth of AI, and 62% say learning to use new technologies increases their motivation at work and improves customer experience. More than half (56%) are also confident they have the necessary skills to work with more AI tools.

The research, which polled 500 financial services employees (banking and insurance) in the UK and Ireland, was commissioned by payroll and HR software provider Zellis and suggests most employees are set to embrace new technologies as adoption increases across the banking and insurance industries.

Other notable findings show that 42% of respondents believe AI will help them to learn new skills, a figure that rises to 60% amongst those who work for larger financial services organisations (+1,000 employees). Thirty-eight percent also believe new technologies will increase their productivity and efficiency at work.

Financial services employees are also more comfortable with the idea of using AI for certain tasks over others. Across the board, respondents are most confident using it to recommend products and services (60%), perform admin tasks such as note taking (58%), and review documents and applications (57%). Confidence drops notably when it comes to using AI for higher risk activities, however: those who work in banking would be least confident in using AI to make investment decisions or inform lending agreements (36% and 28% respectively), and insurance workers are least comfortable in using it to inform underwriting decisions (41%) or handle customer queries (38%).

Overall, the research presents a positive outlook for financial services companies looking to increase their adoption and application of new technologies, though some concerns remain: one in ten feels very negative about the growth of AI, 22% believe the adoption of AI will create difficulties and challenges, and nearly a quarter (23%) are not confident they have the necessary skills to work with new technologies.

Commenting on the findings, Rebecca Mullins, Director of HCM Solutions at Zellis, said: “This research confirms that the majority of financial services employees are primed to embrace new technologies, and that spells good news for the industry’s future success. AI is creating opportunities for banks and building societies to enhance customer experiences through personalisation, while insurance companies are leveraging AI to gather and assess data more quickly for use in decision making and underwriting.”

Mullins continued: “The opportunities are immense but to thrive in this shifting landscape, employers must now focus on identifying how and where to optimise outcomes as new tools and platforms are introduced.”

The research also asked about respondents’ use of current technologies, and while the majority say their existing tools are easy to understand and use (67%), and make their work easier (66%), a smaller number were less positive: two in ten respondents feel frustrated because their current technology is not reliable, does not improve the customer experience, and fails to increase customer spend.

“By leveraging these critical insights to better understand employees’ needs, wants, and concerns, financial services providers will be much better positioned to tackle issues of employee motivation and confidence – and crucially, build technology skills where they are most needed,” concluded Mullins.

Photo by Markus Winkler on Unsplash

Zoom touts AI benefits for contact centres

960 640 Stuart O'Brien

Zoom has announced new AI Companion capabilities that it says will help improve connection, productivity, and collaboration across the platform, with specific benefits for contact centres and agents.

Along with additional enhancements across Zoom Team Chat, Zoom Whiteboard, and Zoom Meetings, Zoom AI Companion, the company’s generative AI assistant that is included at no additional cost, can now help administrators track AI usage and adoption, and contact centers agents improve interactions with customers.

To help streamline the usage and adoption of AI Companion across an organisation, Zoom is introducing an analytics dashboard to help owners and admins gain a better understanding of how AI Companion is being used across their organisation and if more support is needed.

Zoom says AI has become a critical ingredient for many successful contact centres: AI Companion for Contact Center can now summarise customer chats and conversations to enable better agent understanding and smoother hand-offs, generate post-call tasks for follow-up, and support speech analytics and live sentiment.

In terms of productivity the AI Companion also offers agents:

  • Team Chat: When using Team Chat, AI Companion will detect intent to schedule a meeting and will generate a pop-up that, when clicked, opens a calendar invite pre-filled with attendees, as well as date and time information for users, all based on the message’s content.
  • Whiteboard: Users now have the ability to create mind maps within Zoom Whiteboard using AI Companion to help visually organise thoughts that stem from a central idea based on user prompts.

AI Companion will also use attendees’ gender pronouns from their Zoom profile when generating Meeting Summaries to help meeting participants feel seen and acknowledged.

“AI Companion has generated over 5 million meeting summaries since we launched it in September 2023,” said Mahesh Ram, head of AI at Zoom. “We are focused on building AI capabilities that support collaboration, connection, and productivity, and empower users to do their best work. By continuing to launch new features for AI Companion, including an analytics dashboard and AI Companion for Zoom Contact Center, our aim is to help more people feel supported and enabled to build connections together.”

Companies ‘must navigate challenges’ to ensure ethical deployment of AI solutions

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Generative AI has raised new challenges related to ‘Responsible AI’, catapulting concerns into the headlines and forcing enterprises to revisit their AI policies at a time when they investigate new potential applications.

Enterprises need to ensure that AI solutions are implemented in a responsible and ethical way, says GlobalData,  data and analytics company.

Responsible AI refers to the ideal that AI projects, whether based on predictive AI or generative AI, are deployed in a manner that safeguards privacy, does not cause harm, is as transparent as possible, is free from bias, and is fair to all that are impacted by them. The recent lawsuit filed by the New York Times against Microsoft and OpenAI for copyright infringement highlights the challenges our society faces in implementing AI in a responsible manner.

Rena Bhattacharyya, Chief Analyst of Enterprise Technology and Services at GlobalData, said: “Responsible AI has once again been catapulted into the headlines due to the emergence of Generative AI. The ease with which consumers can access Open AI’s ChatGPT has made the concerns posed by the new technology, such as hallucinations and data privacy, readily apparent and easily comprehendible to even casual users.”

GlobalData’s latest reports, “Generative AI Watch: Lessons Learned for Implementing Responsible AI (Part 1)” and “Generative AI Watch: Lessons Learned for Implementing Responsible AI (Part 2),” found that in addition to concerns related to copyright protections, enterprises must contend with issues related to explainability, bias,  ethics,  hallucinations, toxicity and poisoning,  and data privacy and leakage when implementing Responsible AI strategies.

Bhattacharyya concluded: “Challenges related to Responsible AI have existed for years, but they have grown in number with the launch of generative AI and have also become more pressing now. Organizations deploying AI must ensure that they are using the technology in a way that is responsible and ethical; otherwise, they risk significant damage to their brand reputation, if not legal and financial repercussions. It is a highly ambitious goal – and getting there is a daunting task.”

Optimise customer interaction: Effective use of large language models for companies

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By Björn Lorenzen, Regional Vice President EMEA Central at Yext

Linguistic understanding is essential in today’s communication and has a significant influence on our everyday lives. It enables us to exchange information and control processes. In business, language is therefore an essential building block for strengthening customer loyalty and increasing customer satisfaction.

The advanced development of comprehensive language models and their broad application in services such as ChatGPT, Bing Chat and others are creating innovative communication channels and content management options. This enables companies to increase their work efficiency, reduce the workload of their employees and improve customer contact. Artificial intelligence enables an improved user experience and provides customers with direct answers, reducing the need to search FAQ sections or make telephone inquiries.

However, there are also challenges: Large language models can be opaque and contain errors that can affect up to 20 percent of answers. This can undermine trust and impact the customer experience. To avoid this and ensure a pleasant customer experience, companies should optimise their platforms such as websites, intranet or social media with their own data and use it to train artificial intelligence. This not only allows them to retain control over information, but also facilitates the publication of standardised content and streamlines customer service processes. Customers benefit from simpler handling and easier dialog with the company.

But how can this be implemented?

In order to provide targeted information at various contact points such as Google search, website search or chatbots, the following is required:

Large amounts of data (Big Data): This is a collection of all relevant company data. This includes user manuals, FAQs, location information such as address and telephone number as well as product information, company biographies and technical details. It is important that this information base is organised, up-to-date and clear and that sensitive or confidential information is made unrecognisable. The quality of the data directly influences the quality of the derived models and forecasts. In order to be able to make reliable statements, it is necessary to clean the data in advance. This includes finding and completing missing data records, identifying outliers and correcting or removing clearly recognizable erroneous or contradictory data.

A data source: Information can be collected, organised and stored in a knowledge graph or a headless content management system. Here, data is prepared in such a way that it can be related to each other. Artificial intelligence can extract correlations and insights from this data that would otherwise not have been accessible. Even complex queries, such as the search for a Turkish-speaking mortgage consultant in Cologne, can be handled with the help of the system.

In addition, only verified information is included in the system, which gives companies control over the published data. However, before this is possible, the relevant data must be fed into the system. As this often comes from different sources, data transfer via a connected API interface is advisable. This process is not only much more efficient, but also less prone to errors. If the necessary interfaces are not available in the company, the option of manual input remains.

Database technology helps to minimise the risk of data protection violations and adhere to compliance regulations. A headless content management system (CMS) ensures that data is not exchanged directly with AI systems such as ChatGPT. The AI models are only given access to the data they need. In addition, internal training for employees is essential.

Large language models: Language processing models such as GPT-4, LaMDA, PaLM, Gopher, Jurassic-1 and BERT analyse texts depending on the area of application and produce different results. There is no universally superior model, but each is used in different applications according to its strengths. GPT-4, for example, is used to quickly and efficiently create texts such as product descriptions or job advertisements. It can also autonomously generate responses to customer reviews to improve customer service.

Models such as LAMDA and BERT can help to answer user queries directly via a website’s search function. Companies that want to use these technologies need sufficient computing and storage capacity. In addition, the models must be trained regularly in order to gradually increase the quality of the answers and the database must be continuously updated.

Finally, the processed data is made usable for various purposes by the language models and is available for internal and external communication channels. External users receive quick and verified answers in natural language, while internal employees benefit from automatically generated content such as product or personal descriptions and responses to online reviews. With the help of the intranet, internal training resources, such as sales presentations in the finance department, can be accessed quickly. This simplifies work processes and allows specialists to concentrate on more demanding topics.


AI and voice models are more than a trend – they are part of our future working world. Companies should use these technologies to remain competitive. It is important to collect, process and secure data in advance. When integrated into corporate channels, voice models offer great potential for maintaining brand integrity and creating customer-oriented experiences. However, necessary preparations must be made before implementation. Data should be carefully collected, summarised and reviewed to ensure its quality and security.

The integration of GPT-4 and similar advanced language models into your own business processes offers enormous potential to increase brand consistency and create impressive customer experiences. However, these technologies should not be viewed in isolation, as their effectiveness is directly dependent on the quality of the underlying data. They must therefore be continuously fed with company-specific data. Only through a solid data organisation and an adaptable infrastructure can we prevent false information from being disseminated and seamlessly base customer communication on correct information.

About the author:

Björn Lorenzen has been Regional Vice President EMEA Central at Yext, a leading digital experience platform that powers both owned and third-party experiences, since the end of 2020 and in this position is responsible for the company’s strategic new business, among other things. Previously, the IT specialist spent seven years at Facelift, a social media management provider – most recently as Head of Enterprise Sales. His other positions include Actito and Mail Select AG.

AI will enable contact centre agents ‘to become true brand guardians’

960 640 Stuart O'Brien

A new report has highlighted the essential role of hybrid and remote contact centre agents, the expected impact of artificial intelligence (AI), and agents’ readiness to act as brand guardians in the face of evolving consumer demands.

Calabrio surveyed 400 contact center managers from across 10 countries, 4 age groups, and 6 industries. While there is much debate in the market, this report’s response is clear: AI won’t be used to entirely replace agents.

In fact, over two-thirds of contact center managers predict an increase in the number of agents over the next decade and believe AI’s greatest promise is its ability to make agents’ jobs easier and more productive.

However, managers expressed that agents are not yet ready to meet the demands of an AI-fueled future. If contact centers are not giving agents the skills to adapt and develop, they are already falling behind.

“The role of technology, including AI, is poised to gain even greater momentum in the contact center—we’re already seeing customers embrace automation and AI-fueled analytics to maximise their operations,” said Kevin Jones, President and Chief Executive Officer, Calabrio. “But when technology removes a large portion of the administrative tasks from humans, agents will need to adapt to embrace complex customer inquiries and become true brand guardians.”

According to contact center managers, AI has the potential to optimize business processes and create visibility and efficiencies. Managers ranked these features of AI as most impactful:

  • Augmenting agent and manager productivity (25%)
  • Optimizing forecasting and scheduling (20%)
  • Measuring and understanding contact center productivity (20%)
  • AI-driven chatbot services to customers (20%)

This focus on how AI can improve productivity is critical as customer experience (CX) organizations are looking for ways to boost productivity post-pandemic. Just 49% of managers believe that remote workers are meeting productivity expectations today, which is 24% lower than in 2020.

97% of consumers agree that customer service interactions have a direct impact on brand loyalty—which directly correlates to brand revenue. With the advent of AI, the significance of delivering an effective, efficient, and personalized CX has never been more attainable.

With automation becoming the new normal, contact center managers recognize a greater need for critical thinking (top selected) and adaptability to change (second top) among future agents. Yet today, these skills are most frequently identified as lacking, and the top skills impacted when an agent is stressed or disengaged. Managers must bridge this gap through targeted training and development programs, another area where AI can assist.

Training and skills development emerge as top strategies for attracting and retaining talented agents for both current (35%) and future (30%) success. While acknowledging the need for progress, the report reveals that only 45% of contact center managers believe their agents currently possess all the required skills. This significant gap underscores the urgency of investing in comprehensive training initiatives.

Photo by Petr Macháček on Unsplash

Generative AI set to ‘redefine’ customer experience offered by financial institutions

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In the evolving world of financial services, where rapid adaptation to customer needs is paramount, generative AI (genAI) is quickly becoming a game-changer. Its prowess in generating customised content and solutions is redefining how financial institutions interact with their customers and optimises their internal operations.

That’s according to analysts at GlobalData with Kiran Raj, Practice Head of Disruptive Tech at GlobalData, stating: “With the growing demand for personalised financial solutions, genAI has a pivotal role in curating bespoke customer experiences. It is not about generic financial products anymore but crafting individualized strategies that align with each customer’s financial goals.”

Saurabh Daga, Associate Project Manager of Disruptive Tech at GlobalData, added: “Harnessing the power of genAI to analyze intricate patterns in transaction histories and financial behaviors can create predictive models that not only anticipate customer needs but also craft targeted financial products and advisories. This has the potential to elevate customer service, optimize costs, and enhance overall user satisfaction.”

Diving deeper into the transformative potential of genAI, GlobalData’s latest Innovation Radar report, “Code to capital: generative AI meets financial services,” showcases the broad spectrum of genAI applications. From risk assessment and personalized advisory to cognitive customer care and fraud detection, the breadth of genAI’s impact is profound.

Traditional banks such as JPMorgan and HSBC have already developed genAI-based tools that offer personalized financial advice to their customers.

On the other hand, emerging fintechs such as Stripe and Cowbell are enhancing their end-user experience with genAI-powered tools.

Synthetic data is being used by financial service providers such as Provinzial Insurance and Wells Fargo to create predictive models to detect fraud as well as develop personalized offerings.

Deutsche Bank and Goldman Sachs are experimenting with genAI to automate some of their backend processes including banking software development and management of financial documents.

Daga concluded: “Implementing genAI in the sensitive landscape of financial services comes with its set of challenges. Issues surrounding data accuracy, ethical considerations, and privacy are paramount. However, with judicious governance, these hurdles can be surpassed. Forging strategic alliances can be a pathway to harness the transformative potential of genAI, ensuring they are equipped with the right technology, infrastructure, and talent to capitalize on this wave of innovation.”