Bryan McLachlan | Five uses of generative AI to enhance customer service in finance
Artificial intelligence (AI) and machine learning (ML) are no longer new to financial services companies, with banks and insurers already using the technology for applications such as underwriting, fraud detection, risk assessment, and marketing. Now, with generative AI breaking into the mainstream, many financial institutions are examining where they can put this innovation to work.
Generative AI refers to technology that enables machines to generate original content such as software code, art, music, video, and text in the blink of an eye. Generative AI technology has evolved in leaps and bounds, as anyone who has experimented with tools like ChatGPT, DALL-E and Stable Diffusion can attest.
Whereas most AI applications in financial services today leverage structured data to surface business insights, automate processes and make predictions, generative AI enables institutions to harness unstructured data such as text, video, and audio for a range of sophisticated applications. This makes generative AI a potentially powerful tool for a range of new use cases. Here are some examples:
- Customer service and experience
Chatbots and virtual voice assistants powered by large language models can be ‘trained’ to offer richer, more personalised digital customer experiences. They can tap into a financial institution’s knowledge base to answer questions in natural language, guide customers through loan applications or insurance claims, and make personalised product recommendations.
Conversational AI applications aren’t new, but more mature generative AI will enable banks and insurers to create chatbots and assistants that can offer less robotic, more complete, and more tailored responses to the customer. This can help to deflect calls and chats from human operators, so that contact centre agents can focus on the most complex or valuable engagements.
- Risk management and compliance
AI and ML are extensively used for applications such as assessing insurance risks, predicting where a customer will default on a loan, flagging KYC/AML risks, or identifying anomalies that suggest fraudulent activity. Adding generative AI to the mix can expand these capabilities by combing through unstructured data for further insight.
- Code generation
Many IT departments are adding low-code and no-code to their mix of tools to empower power users to build or customise apps according to their needs. Generative AI can help them to further reduce the burden involved in simple and even not-so simple coding tasks. They can use generative AI to quickly generate some code, then check and customise it to their need. Or they can use it to support quality assurance. Programmers can thus focus on the elements of their job where they can add the most value.
Generative AI can support a financial institution’s marketing activities with hyper-personalised text, visual and video content. This can enable a bank to reduce the costs and time involved in producing relevant content for its audience. It can also be used to generate proposals and customer conversations. Another application lies in using generative AI to analyse customer sentiment on social media or even in live conversations and to create appropriate responses for the customer’s need or emotional state.
- Ethical AI is as important as ever
As important as it is to amplify the powerful capabilities of generative AI, it’s essential to harness it in adherence with principles of responsible and ethical AI, along with sound data governance and regulatory compliance. It’s important to avoid biased training data and to ensure all outputs and decisions can be explained to the customer.
* Bryan McLachlan is Managing Director: Africa at CyborgIntell.