From avatars to compliance automation: how generative AI can reshape banking

By harnessing the power of generative AI, banks can unlock a multitude of opportunities to stay ahead in a highly competitive market. Let’s have a look at some use cases and their business potential.

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11 minutes to read

In recent years, the field of artificial intelligence (AI) has witnessed remarkable advancements, particularly in the domain of generative AI. Generative AI refers to the ability of machines to create original content, such as images, text, or even music, that closely resembles human-generated content. This transformative technology is making its way into various sectors, and one area where it holds significant promise is banking. Generative AI has the potential to revolutionise the banking industry by enabling financial institutions to enhance customer experiences, optimise operations, and mitigate risks. By harnessing the power of this technology, banks can unlock a multitude of opportunities to stay ahead in a highly competitive market. 

Generative AI, within the broader context of digital transformation, represents a remarkable advancement in technology with significant potential. However, it is crucial to recognise that generative AI alone, without considering other complementary technologies or holistic efforts, can become another fleeting hype.  As we already saw with Big Data analytics or process mining; if your data landscape is not in order, the technology will not fix it. If you do not have use cases with a clear business value behind it, it’s doomed to fail, and you cannot unlock its full potential.

So, let’s have a look at the top generative AI use cases for banking and their potential business value.

Customer support in retail banking

According to a survey conducted by Bitkom Research, around 58% of German consumers expect personalised financial advice and services from their banks but businesses are striving to reduce costs and automate whatever is possible. Generative AI can elevate the banking experience by creating personalised and contextually relevant content and helping navigate the complex world of forms and regulations. By adopting generative AI, banks can provide efficient and responsive customer support through intelligent chatbots and virtual assistants. These AI-powered interfaces can understand and respond to customer queries in a human-like manner, offering round-the-clock assistance, streamlining routine tasks, and freeing up human agents to handle more complex issues. Research by Gartner indicates that implementing AI-powered self-service platforms can reduce customer support costs drastically, by automating one in 10 agent interactions by 2026.

Digital relationship manager in corporate banking

Corporate and institutional banking can also benefit tremendously from advanced virtual assistants. These AI-powered interfaces, also known as avatars, understand and respond to customer queries in a human-like manner, offering round-the-clock assistance, streamlining routine tasks, and freeing up human agents to handle more complex issues. The job of a relationship manager for corporate clients can be a very time-consuming task and there is a limit to how many clients a relationship manager can adequately handle. By deploying digital avatars that are trained on customer data (with their consent), corporate clients can expect 24/7-availability and hyper-personalised customer service by their dedicated digital relationship manager. Additionally, an avatar can support the relationship manager via ad-hoc analysis of the conversation and offer individualised next-best offers or next-best questions which could increase efficiency during client interactions.

Regulatory compliance and governance

The German banking industry faces ongoing challenges in combating financial fraud and managing risks effectively. Generative AI algorithms can analyse vast amounts of data, including transaction records, customer behaviour patterns, and external data sources, to detect anomalies and potential fraud with greater accuracy. By adopting generative AI-powered risk management systems, German banks can enhance fraud detection capabilities and mitigate risks more efficiently, safeguarding customer assets and maintaining trust. Generative AI algorithms can significantly reduce fraud losses and operational costs associated with manual fraud detection efforts. A report by Capgemini states that AI-based fraud detection can reduce fraud losses by up to 25% and decrease false positives by 40-50%, resulting in cost savings. Germany, as a member of the European Union, operates within a stringent regulatory framework, such as the General Data Protection Regulation (GDPR) and anti-money laundering (AML) regulations. Generative AI can assist banks in automating compliance processes, ensuring adherence to regulatory requirements, and streamlining reporting. By leveraging generative AI technologies, German banks can reduce compliance costs, enhance accuracy, and avoid penalties resulting from regulatory non-compliance.

Investment and wealth management

The German market presents a significant opportunity for generative AI in investment and wealth management services. As customers increasingly seek tailored investment strategies and advice, generative AI can not only analyse vast amounts of financial data, market trends, and risk indicators to generate personalised investment recommendations; it can also prepare its results in a custom, ready-to-consume interface for the customer. German banks can leverage generative AI algorithms to provide more sophisticated investment strategies, portfolio optimisation, and tailored financial planning services, thereby attracting and retaining high-net-worth clients.

Personalized customer service

Customer service in the banking industry is undergoing a significant transformation. Through advanced natural language processing (NLP) and machine learning (ML) algorithms, generative AI offers exciting possibilities for introducing a new era of customer support.

At the heart of this transformation lies the advent of AI-powered chatbots that possess the remarkable ability to adapt to each individual customer. Picture a world where every customer is assigned a personal (chatbot) assistant, tirelessly dedicated to understanding their unique needs, preferences, and financial goals. These intelligent assistants engage in dynamic conversations, offering tailored solutions and personalised recommendations. By analysing vast amounts of customer data and transaction history, these chatbot assistants can provide real-time insights and guide customers through a myriad of banking services and products that align with their specific requirements.

By leveraging generative AI in customer service, German banks can reduce customer acquisition costs and improve customer retention, resulting in potential cost savings. According to a study by McKinsey, personalised experiences can drive a 5-15% increase in revenue and save up to 50% in customer acquisition costs.

Graphic how can generative ai can help in retail banking

Digital relationship manager for corporate and institutional clients

Imagine a world where customers are greeted by their own personalised virtual relationship manager (RM), revolutionising the way business relationships are nurtured. This concept combines the power of generative AI with the expertise of a human relationship management to create virtual avatars that serve as digital counterparts to human RMs, capable of understanding the unique business objectives, financial requirements, and industry dynamics of each corporate client.

In practice, corporate clients would interact with their avatar almost the same way they interact with their human RMs: via phone or via virtual meetings – with the notable exception that the digital RM is available 24/7. The avatars would possess a remarkably realistic appearance and voice, indistinguishable from a human counterpart, and can engage in natural and dynamic conversations. Through the analysis of client data, financial history, and market trends, the avatars possess an in-depth understanding of the client's business landscape. Through sophisticated NLP ML algorithms, the avatar would continuously learn and adapt to customer preferences, ensuring that the provided recommendations and service are constantly refined and personalised.

Digital relationship managers are adept at business development, actively seeking new prospects and collaborating with internal teams to deliver tailored banking solutions. Through cross-functional collaboration, they ensure the delivery of comprehensive services that address evolving client needs. Additionally, virtual RMs provide valuable market intelligence, staying updated on industry trends, market developments, and competition. Armed with this knowledge, they offer strategic insights and recommendations, empowering clients to make informed decisions.

Of course, this concept raises important ethical considerations. Transparency is crucial, so you’ve got to ensure customers are aware that they are interacting with an AI-powered virtual assistant. Clear communication about the nature of avatar technology is necessary to maintain trust. Data privacy and security must be prioritised, with customer information protected and used ethically. Addressing biases is vital. Algorithms and training data must be designed and audited to mitigate unintentional biases in recommendations. Providing customers with the option to opt-out or switch to human interaction is essential, and you’ve got to respect their preferences. By upholding transparency, data privacy, fairness, and customer choice, banks can deploy such avatars responsibly, harnessing the benefits of generative AI to enhance experiences while navigating the ethical landscape.

Highly automated regulatory compliance and governance processes

Over the last decades, the ever-changing regulatory landscape has had a massive effect on the strategy as well as day-to-day operations of banks. Stringent GDPR and AML regulations, just to name two, have and continue to put pressure to change and automate highly urgent processes. Generative AI offers immense potential for banks to improve their compliance processes and to overcome the challenges associated with meeting regulatory requirements.

By using generative AI, banks can automate various, yet often highly manual, compliance processes, such as data analysis, monitoring, and reporting. These technologies can efficiently analyse vast amounts of data, detect anomalies, and identify potential compliance risks, allowing banks to take proactive measures to address any issues. Automation not only increases the speed and accuracy of compliance procedures but also reduces the manual effort and associated costs. Automation through generative AI is also applicable to the creation of regulatory reporting. These technologies can extract relevant information from multiple sources, consolidate data, and generate comprehensive reports that meet regulatory standards. This automation not only saves time but also ensures the accuracy and completeness of reporting, reducing the risk of penalties resulting from non-compliant or incomplete submissions.

Generative AI can also enhance the accuracy of compliance activities by minimising the risk of human error. ML algorithms can continuously learn and adapt to evolving regulations, ensuring that banks remain up-to-date and in compliance with the rapidly adapting legal landscape. This capability significantly reduces the likelihood of non-compliance and associated penalties.

These technologies provide a robust framework for managing and monitoring compliance obligations, enabling banks to navigate the complex regulatory landscape with greater efficiency and confidence.

Hyper-personal and intelligent investment and wealth management

The rise of generative AI heralds a new era of intelligent wealth management. By combining sophisticated algorithms with human expertise, banks can provide superior services that meet the unique needs and objectives of their clients. This is particularly true for true wealth management activities: tailored investment strategies and accurate financial forecasting.

Generative AI enables banks to analyse vast amounts of financial data, market trends, and customer preferences to develop personalised investment strategies for wealth management clients. By leveraging machine learning algorithms, banks can assess individual risk profiles, identify unique investment opportunities, and optimise asset allocation to meet the specific needs and goals of their clients.

Moreover, generative AI empowers banks to enhance their financial forecasting and portfolio management capabilities. With advanced algorithms, banks can simulate various investment scenarios, conduct comprehensive stress tests, and optimise investment strategies to maximize returns and minimise risks for their clients. Banks gain the ability to make data-driven decisions, accurately predict market trends, and provide valuable insights to guide their clients’ investment decisions.

When implemented right, this could give banks a significant competitive edge and ensure that banking remains at the forefront of innovation.

Impact ranking

The following ranking is based on a generic estimate for effort and impact per use case. The effort represents the level of implementation complexity. The impact represents the transformative potential (i.e., cost reduction, efficiency enhancement, competitive advantage) and benefit associated with each use case.

  • Digital relationship manager for corporate and institutional clients
    • Effort: Medium
    • Impact: High
  • Highly automated regulatory compliance and governance processes
    • Effort: High
    • Impact: High
  • Hyper-personal and intelligent investment and wealth management
    • Effort: Medium
    • Impact: Medium
  • Customer support in retail banking
    • Effort: Low
    • Impact: Medium

How to approach generative AI in banking

In general, one must consider two main action areas:

  • Understand and learn

Business and technology leaders should learn about the new technology as much as possible. Only by obtaining a solid understanding of how it works and what is necessary to adapt, one can pierce the fog and form a strong hypothesis on how generative AI can shape your company. This includes technology, use cases from other industries, its limitations, and ethical or legal considerations. Only with a well-informed and trained leadership team you can avoid falling for the hype and creating uncertainty in your ranks.

  • Find and evaluate use cases with business impact

As with every new technology it’s a good idea to start your journey with tackling real business problems that need to be solved. Having a strong business buy-in and a lever hypothesis supports the business and combats the impression of just being a hype. Generative AI might be a solution to your problems, but you should avoid building a solution and ask for problems. Therefore, a set of well-known and trained approaches like the Zühlke ADM helps to ideate, prioritise, prototype, decide, and scale your use cases.

Based on your digital and data strategy, you might have already evaluated the areas where you could investigate generative AI use cases and shape the capabilities around them.

Graphic Zühlkes Data-Driven Company Framework

Generative AI as part of a comprehensive digital transformation strategy

Generative AI has the potential to revolutionise the banking industry by enabling financial institutions to enhance customer experiences, optimise operations, and mitigate risks. By harnessing the power of this technology, banks can unlock a multitude of opportunities to stay ahead in a highly competitive market. Generative AI is not the answer to every question. Other technologies like machine learning or simple automation can be more advantageous. And you must do your homework first. As mentioned in the beginning, if your data landscape is not in order, the technology will not fix it, as seen with Big Data analytics or process mining. If you do not have use cases with a clear business value behind them, they’re doomed to fail and you cannot unlock their full potential.

To fully harness the power of generative AI, it needs to be integrated strategically and synergistically with other digital technologies, such as data analytics, cloud computing, and automation tools. Moreover, organisations must foster a culture of innovation, invest in talent development, and establish robust data governance frameworks. By approaching generative AI as part of a comprehensive digital transformation strategy, organisations can unlock its true value, drive sustainable results, and realise long-term benefits for their operations, customer experiences, and competitive advantage in the ever-evolving digital landscape.

Björn Lehnhardt
Contact person for Germany

Björn Lehnhardt

Managing Director Financial Services Germany & Austria

Björn Lehnhardt heads the Financial Services Market Unit at Zühlke Germany. Björn has been responsible for innovation, software and technology, strategy and organisational consulting as well as business development in various management positions for over 15 years and has practical experience from numerous digitalisation projects. He is driven by actively shaping the digitalisation journey together with our customers and helping them to take responsibility for tomorrow. Björn stands for a healthier and better planet through technology and digitalisation.

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