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.