This article has been written by Arnab Ganguly pursuing a Training Program on How to Use Generative AI for Career Growth for Senior Professionals course from Skill Arbitrage.

This article has been edited and published by Shashwat Kaushik.

Introduction

In the past few decades, the financial services sector has been at the forefront of technological innovation, leading to ground breaking innovations like online banking, mobile payments, robotic investment advisors and algorithmic trading. As we venture deeper into the 21st century, a new paradigm where Artificial Intelligence (AI) partners with humans to redefine the future workplace will likely emerge. This synergy is in line with a trend that is increasingly observed across industries and is driven by the rise of AI. According to a report on the future of work by the World Economic Forum, around 85 million jobs will face displacement by 2025 as AI matures rapidly, while approximately 97 million new roles will also be created. This will create a division of labour between humans and AI and a partnership at the workplace that promises to enhance efficiency, drive innovation, and create unprecedented value for both customers and businesses alike.

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Evolution of AI in financial services

The journey of AI in financial services has evolved from simple automation to complex decision-making. Early adoption saw the automation of repetitive tasks, but today, AI is making inroads into areas requiring cognitive abilities such as financial analysis, risk assessment, investment advice and customer service. The AI revolution in finance is driven by the need for ever increasing efficiency in the face of increasing volumes of data. The need for AI as a partner is also driven by the need for real time analysis and decision making as analysis in this sector has immense temporal value; one has only got to observe the domain of trading, where trillions of dollars are traded daily, to appreciate the value of real time analysis and action based on such analyses.

The potential of AI within the realm of financial services has been substantiated by researchers, who maintain that FinTech transcends mere e-banking and digital consumer interfaces; it pivots today on the advent of novel, technologically-facilitated instruments that cater to user demands. Within this framework, AI emerges as a dual-beneficiary agent, revolutionising the industry by meeting ever changing customer demands and propelling business value for firms.

Synergistic collaboration : enhancing  human potential

The potential of AI as a collaborative partner lies in its ability to analyse vast quantities of data, recognise patterns, and provide actionable insights — tasks that would take humans significantly longer to perform. For instance, AI can help financial analysts by sifting through global economic data to identify investment opportunities or risks. This ability to analyse and take action in real time can also provide solutions to long standing issues in global finance, such as the timely prevention of fraud and curbing money laundering.

This partnership allows human employees to focus on strategic tasks, relying on AI for data-driven decision-making.

Payment systems that can amaze

Payment systems have evolved from the all too familiar Cash to Credit Cards and the Unified Payment Interface (UPI), which enable customers to make payments with ease. One of the latest developments in this domain is made possible only due to a combination of AI, Blockchain and other advances in computing and storage. An innovation by Ant Financials, China, in 2017, named “Smile to Pay,” leverages deep learning algorithms for facial recognition, allowing customers to execute payments with a mere smile at a vending machine. This technology, supported by robust security protocols and an enabling legislative framework, has the potential to revolutionize payment methods, positioning the consumer’s facial biometrics as a proxy for traditional payment instruments.

This will soon lead to a situation in the future where human tellers and cashiers manage payments side by side with fully automated AI agents.

Loan disbursement : revolutionary speed with AI

Fintech companies are already employing a synergy of technologies—Blockchain, AI, Security, Internet of Things (IoT), and Computing (BASIC)—to facilitate systems that can make near instantaneous decisions on lending with zero human interaction. Moreover, the default risk in such loans is relatively low, as they are underwritten based on data collected from social media, ordering and repayment patterns on e-commerce sites and order integrators.

It is likely that the underwriting and disbursement of small retail loans will be completely AI driven, while expert humans will work on larger, more complex loans. This partnership between humans and AI will likely benefit all stakeholders.

AI and customer experience : a new paradigm

In the realm of customer service, chatbots and virtual assistants have already made an impact. They provide the possibility of customer self-service as well as the feature of temporal and spatial independence, that is, the availability of service at any point in time, anywhere in the world where an internet connection is available.

Over the last couple of years, we have witnessed the advent of intelligent customer service agents leveraging the abilities of Large Language Models (LLM) such as Chat GPT, LLAMA and many other similar platforms. These agents have the potential to provide personalised financial advice round-the-clock, enhancing customer experience and satisfaction. Deep learning and Natural Language Processing (NLP) are integral to automating customer query handling, with an astounding 97% of such interactions occurring without human mediation. In 2017, the efficacy of such a system was recognised as surpassing human performance in terms of customer satisfaction.

In the future workplace, these AI systems will work in tandem with human advisors, with only the most complex requests being escalated to expert human beings, offering a composite of personal touch and machine efficiency.

Hiring, training and adaptation : preparing for an AI-driven workplace

The integration of AI in the workplace necessitates a shift in skills and training for the existing workforce. The process of working for a new-world human-AI partnership company will likely begin with candidates being assessed automatically for suitable aptitude.

Since relatively simpler decision making will be done almost entirely by AI, human professionals will, in general, need to have a higher level of expertise than that existing today. Financial professionals will not only need to acquire new competencies in data analytics and AI technology but will also need additional expertise in their specific domain of financial services. Human employees will need to learn to delegate tasks to AI agents and will need to “trust” decisions made by intelligent agents.

While AI excels at processing information, human judgement remains indispensable, especially when dealing with complex, nuanced situations. Financial professionals bring contextual understanding and emotional intelligence to the table—qualities that AI has yet to replicate. The interplay between AI’s analytical prowess and human judgement will define the future of financial decision-making, balancing efficiency with empathy.

The need for humans and AI to work in close partnership in the work-world will require changes in both human behaviour and the way AI operates today. Human-AI partnerships will form new socio-technical systems that can leverage the unique capabilities of either mentioned above.

Issues and bias mitigation

As AI becomes more prevalent, ethical considerations come to the fore. We have already witnessed and researched cases of bias against African Americans in underwriting loans. The financial services sector must take the lead in establishing ethical AI frameworks, focusing on transparency, accountability, and fairness. This involves training AI systems with unbiased data and regularly auditing algorithms to prevent discrimination in lending, hiring, and customer service.

Since the data required for training algorithms comes from human beings, who are both users and makers of AI systems, it may be that in the long term, AI agents will align with the same biases that human beings have. There are numerous studies being carried out and mechanisms being put in place that will help guard against this; however, the evolution of these can only be observed over a period of time. 

Moreover, human-AI partnership systems are vulnerable to breakdowns due to humans not being able to understand the ‘Black-Box’ nature of AI algorithms, not being able to act as fast as AI systems or, sometimes, placing too much trust in decisions made by AI systems. Indeed, in the domain of autonomous cars, we have already witnessed fatal accidents due to human drivers placing too much trust in the AI based driver and it is not unlikely that the financial services sector too will see similar issues.

The future of employment

Concerns about AI leading to job displacement are prevalent, with recent studies by McKinsey suggesting that approximately half of current occupational activities are susceptible to automation, potentially resulting in up to 15% of the global workforce being displaced. The reality, however, is likely to be more nuanced. AI will automate certain tasks, but it will also create new roles and opportunities. Jobs will evolve, and a new cadre of financial professionals who can work effectively with AI will emerge. The human-AI partnership will not only redefine existing roles but also pave the way for new career paths within the financial sector. In the future workplace, human-AI partnerships are expected to play a crucial role in transforming the nature of work and creating new possibilities for businesses and employees alike.

Collaborative intelligence

Collaborative intelligence represents a paradigm shift in the relationship between humans and artificial intelligence (AI). Rather than viewing AI as a replacement for human workers, collaborative intelligence embraces the idea that humans and AI can work together to achieve greater outcomes than either could on their own.

In this collaborative model, AI augments human capabilities, enhancing strengths and compensating for weaknesses. Humans contribute their unique abilities, such as creativity, problem-solving, and strategic thinking, while AI provides computational power, data analysis, and pattern recognition.

The benefits of collaborative intelligence are numerous. For businesses, it can lead to increased productivity, innovation, and efficiency. In healthcare, it can enable personalised medicine and improve patient outcomes. In education, it can provide individualised learning experiences and support lifelong learning.

Enhanced productivity

The integration of Artificial Intelligence (AI) into various industries is revolutionising the workplace. AI’s ability to automate repetitive and mundane tasks has unlocked unprecedented opportunities for enhanced productivity. By taking over routine and time-consuming activities, AI frees up valuable human time and resources, allowing workers to focus on more strategic, creative, and fulfilling tasks.

This dynamic collaboration between humans and AI leads to several benefits. Firstly, increased productivity is achieved as AI streamlines processes, reduces errors, and optimises workflows. Secondly, efficiency is enhanced as AI automates tasks with precision and speed, leading to faster turnaround times. Thirdly, innovation is fostered as human workers, relieved from the burden of repetitive tasks, can dedicate their time and creativity to problem-solving, brainstorming, and developing innovative solutions.

Personalised experiences

Artificial intelligence (AI) is rapidly changing the way businesses interact with their customers, employees, and partners. One of the most significant ways AI is impacting the business landscape is by enabling the creation of personalised experiences.

AI can analyse vast amounts of data to understand individual preferences and needs. This data can come from a variety of sources, such as customer surveys, website behaviour, and social media activity. By analysing this data, AI can create a detailed profile of each individual, which can then be used to tailor products, services, and marketing messages to their specific needs.

There are many benefits to providing personalised experiences. For customers, it can lead to increased satisfaction and loyalty. For employees, it can lead to a more engaged and productive workforce. And for partners, it can lead to stronger relationships and collaboration.

Here are some specific examples of how AI is being used to create personalised experiences:

  • Retail: AI can be used to recommend products to customers based on their past purchases, browsing history, and social media activity.
  • Travel: AI can be used to create personalised itineraries for travellers based on their interests, budget, and travel preferences.
  • Healthcare: AI can be used to develop personalised treatment plans for patients based on their medical history, genetic data, and lifestyle factors.
  • Education: AI can be used to create personalised learning experiences for students based on their individual learning styles and needs.

As AI continues to develop, we can expect to see even more innovative and personalised experiences being created. Businesses that are able to effectively leverage AI to create personalised experiences will be well-positioned to succeed in the future.

Skill transformation

The advent of artificial intelligence (AI) is rapidly transforming the workplace, automating routine tasks and creating a demand for new skills. Employees who wish to remain competitive in this evolving landscape must embrace skill transformation, a process of acquiring new skills and adapting existing ones to meet the demands of the future workplace.

This skill transformation necessitates a proactive approach to ongoing learning and development. Organisations must invest in comprehensive learning and development programmes that equip their employees with the necessary skills and knowledge to thrive in the digital age. These programmes should focus on imparting skills such as critical thinking, problem-solving, creativity, adaptability, and emotional intelligence, which are less susceptible to automation.

In addition to formal learning programmes, organisations should also encourage a culture of continuous learning where employees are empowered to take ownership of their development. This can be facilitated through the provision of online learning resources, mentorship programmes, and access to industry experts.

Skill transformation is a collaborative effort that requires the active involvement of both organisations and employees. By investing in ongoing learning and development, organisations can prepare their workforce for the future workplace and ensure their continued competitiveness in the digital era.

Here are some specific examples of how skill transformation can be implemented in the workplace:

  1. Upskilling: This involves providing employees with training to enhance their existing skills and knowledge. For example, a marketing professional may be trained in digital marketing and social media management to keep up with the changing landscape of marketing.
  2. Reskilling: This involves providing employees with training in entirely new skills. For example, a manufacturing worker may be trained in robotics and automation to prepare for the increasing use of these technologies in the manufacturing industry.
  3. Lateral skilling: This involves providing employees with training in skills that are transferable across different roles and departments. For example, a customer service representative may be trained in sales skills to expand their career opportunities.

By implementing skill transformation initiatives, organisations can create a workforce that is adaptable, resilient, and ready for the challenges of the future workplace.

Ethical considerations

The rapid rise of human-AI partnerships has introduced a complex set of ethical considerations that organisations must navigate. Ensuring the responsible use of AI is of paramount importance, and this requires the establishment of clear guidelines that address transparency, fairness, and accountability.

Transparency

Organisations must be transparent about their use of AI, including the purpose, limitations, and potential risks. This includes providing clear information to users about how AI-powered systems make decisions, the data they use, and any biases that may exist. Transparency builds trust and enables users to make informed decisions about their interactions with AI systems.

Fairness

AI systems should be designed and deployed in a fair and unbiased manner. This involves addressing potential biases in the data used to train AI models, ensuring equitable access to AI technologies, and mitigating any discriminatory outcomes. Fair AI systems promote social justice and prevent harm to vulnerable populations.

Accountability

Organisations must be held accountable for the actions of their AI systems. This includes establishing mechanisms for redress and recourse in cases where AI-powered decisions result in harm or unfair treatment. Assigning clear responsibility for AI systems ensures that organisations are proactive in addressing ethical concerns and mitigating potential risks.

Additional ethical considerations:

  • Privacy: AI systems often process sensitive personal data, so organisations must adhere to data protection regulations and ensure the privacy of individuals.
  • Safety and security: AI systems should be designed with safety and security in mind, preventing unauthorised access and ensuring the integrity of AI-powered decision-making processes.
  • Environmental impact: The development and deployment of AI technologies should consider their environmental impact, such as energy consumption and carbon emissions.

Addressing these ethical considerations requires a multi-stakeholder approach involving collaboration between organisations, policymakers, researchers, and civil society. By working together, we can ensure that human-AI partnerships are guided by ethical principles and contribute positively to society.

Conclusion

The partnership between humans and AI is set to be the cornerstone of the future workplace, especially within the financial services sector. As we embrace this partnership, it is essential to navigate the challenges thoughtfully, emphasising ethical practices, continuous learning, and adaptation. A number of challenges need to be overcome in order to embed a well-functioning socio-technological system of humans and AI agents working in a seamless fashion in financial services in the near future.

The human-AI collaboration holds the promise of a smarter, more efficient, and more personalised financial world. The future of finance is not just about technology; it’s about the synergy between technology and the people who wield it.

References

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