Imagine if you could just open your banking app and ask, “What’s the best stock to invest in?” or “I have a big expense coming up; what’s the best way to manage this payment?” and receive clear instructions on what to do and why.

Today, with the advent of generative AI models, this scenario is possible. Generative AI models are evolving rapidly – starting with the tremendous uptake of ChatGPT based on GPT3 and then a much more powerful GPT4. Every industry is considering generative AI’s use cases, evolution, risks and impact, and financial services are no exception.

Why the hype around generative AI?
Unlike artificial intelligence models preceding it, generative AI can process and analyze huge volumes of data and create new, original content based on it. Based on large language models designed on billions of parameters, generative AI engines can understand and process large amounts of multi-modal data with remarkable accuracy and fluency. And most importantly, it can help humans make sense of that data in natural language, answering queries with remarkable human-like responses.

The success of ChatGPT lies in the fact that it puts the power of AI in the hands of people. There is massive potential in using this capability in banking, financial services, and insurance (BFSI) industries to augment human potential.

How BFSI leaders in Southeast Asia see generative AI in the enterprise
In a recent meeting with the CXO of a leading bank, the discussion turned to the possibilities and extent to which GPT3 could be used in the enterprise. Presently, the GPT models are only trained on external data and aren’t contextual to business use. What is needed is akin to a “privatized Open AI” that could also leverage the firm’s internal data. When that happens, generative AI will amplify productivity and experience by automating mundane processes to support complex scenarios.

● Simplifying employee support - Automating queries such as “How many leaves do I have,” “Which project code should I use for XYZ work,” and “My laptop screen is discolored on the top right; how do I fix it” can simplify employee support and response, reduce wait times, and drastically reduce support overheads.

● Amplifying sales productivity - Automating email responses, summarizing conversations and suggesting action items, pulling out the latest fund sheets, and more could help salespeople access the most relevant data, save time, and improve productivity.

● Managing risk, compliance, and ESG goals – OpenAI, when trained with adequate historical data, prescribed industry standards, parametrized datasets and templates, can effectively automate 40-50% of the mundane and repetitive reporting or filing tasks. It can analyze these massive datasets and trigger alerts if any parameters are not “green.”

● Improving customer service and satisfaction – Instead of pre-coded responses in existing chatbots, advanced OpenAI-based services will offer more intelligent and human responses to customer queries. They can offer more contextual, personalized and natural language support for interactions like account opening, query resolution, card issuance, basic loan approval or claim processing.

● Hyper personalized wealth management - Generative AI will be able to glean the individual needs of each customer and simulate them with economic scenarios to create situation-specific financial recommendations. Financial advisors could query the engine in natural language for these recommendations and improve their assets under management.

These use cases are just the tip of the iceberg. As enterprises find more ways to integrate OpenAI in their systems and workflows, it might just create whole new ways of working.

Stepping cautiously, and responsibly, into the future
While OpenAI has the potential to write a new chapter in financial services, we must proceed with caution. As with any new technology, and especially one that is this powerful, there is a need to carefully address the risks and ensure its safe and ethical use. Understandably, many CXOs are still skeptical about the technology and its use cases. Companies are still in the very early stages of exploring where generative AI can benefit their business. Questions around “humanization”, trust, bias, data security, governance, and ethics have yet to be answered satisfactorily.

At Avanade, we are taking a pragmatic approach to this technology - start small, understand the vastness and limitations of the technology, as well as its due evolution, and put in place governance and the right guardrails to use it responsibly.

Learn more about how generative AI will impact financial services in Southeast Asia here. In the second part of our blog series, we will delve into specific use cases as well as the challenges that BFSI players will need to address.