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Generative AI is here to stay. The impulse to keep up with the competition (nevermind differentiation) means that failure to harness Generative AI will leave organizations falling behind.
This is as true in the financial services industry as any other, whether in a service, support or advisory context.
Why is Generative AI necessary?
Financial services organizations in the banking industry are facing a number of challenges.
Service and support
90% of companies use chatbots for case processing and complaint resolution (Deloitte).
72% of customers want instant service.
70% believe agents should have full context on previous interactions (Zendesk).
62% agree that personalized recommendations are preferable to general ones (Zendesk).
Advisory
58% of relationship managers said managing client expectations of 24/7 availability is challenging (Capgemeni).
45% of wealth manager executives said the cost per relationship manager is rising (Capgemeni).
The solution to these challenges – meeting customer expectations, process inefficiencies, or spiraling costs – lies in Generative AI.
Contact center
Boost agent productivity and offer the agent consistent, accurate responses.
Virtual agents and chatbots
Automate responses to customer queries by providing better support.
Real-time advice
Improve efficiency and consistency when responding to lower value queries during live conversations, enhancing the overall quality of the advice.
Generative AI risks in the banking industry
Organizations in the banking sector are understandably cautious about adopting Generative AI. It is still a new technology and has shown itself to be fallible in certain situations, and could even lead to fraudulent activities.
Shadow AI: Samsung employees risk confidential source code
Shadow AI is when a company’s staff take the initiative to improve process efficiency and productivity through the unsanctioned use of Generative AI. In fact, a Salesforce survey of 14,000 workers across 14 countries found that half use Generative AI tools without approval. This kind of use can put companies at risk of cybersecurity and data privacy leaks.
One example of Shadow AI was at technology company Samsung, where engineers leaked confidential parts of the company’s source code. It happened on three separate occasions, when the employees used ChatGPT to speed up processes. The mistakes led to a company-wide ban of ChatGPT among employees and served as a warning to other organizations.
JP Morgan has restricted the use of ChatGPT among its employees for similar reasons. According to The Telegraph newspaper, there were concerns that sensitive financial information was being shared with the chatbot, which could result in regulatory action.
Hallucination: Google blunders – and an attorney really drops the ball
The “hallucination” problem in Generative AI happens because the bots are programmed so that “no” can never be the answer to a query. If the bot doesn’t know the answer, it will automatically generate ghost answers, whether accurate or not.
An unfortunate example of this was when Google’s Artificial Intelligence chatbot Bard was being demoed to an audience. The presenters asked it, “What new discoveries from the James Webb Space Telescope can I tell my 9-year-old about?”
The bullet-point reply included the statement that the telescope “took the very first pictures of a planet outside of our own solar system.” A false-positive fact that was inaccurate, causing Google’s shares to plummet and losing $100 billion in market value in a day.
More seriously, an attorney named Steven Schwartz will be charged in New York after using fake citations in a legal research case, taken from Open AI’s ChatGPT. He cited castes that simply did not exist, resulting in the entire case being thrown into dispute.
What steps should financial services providers take?
These cases don’t mean that Gen AI should be avoided. Instead, organizations in the financial sector should take a strategic approach to minimize potential risk – and maximize reward.
Processes should be put into place to monitor the data you are using to train the model and redact any information that could be harmful to the organization or impact customer satisfaction.
Service, support, and advice: Leveraging Gen AI tools in context
We tend to think of AI as a single touchpoint – the chatbot for customer inquiries. After all, this is how it was used in the past.
How AI was used before
How AI is used now.
Now, the technology has progressed to make it a communication enhancement tool across a wide range of touchpoints and contexts. This enhances the customer experience and makes better use of artificial intelligence technology, while also allowing for personalized product recommendations.
What does this mean in service, support, and advisory use cases for the financial industry?
New and improved Gen AI capabilities
Improved customer chatbot (Service and support)
Decipher long-tail intents to produce more accurate bot responses for informed decisions, which are taken from the organization’s own knowledge database to ensure accuracy. Customers can self-serve on even complex issues that don’t require human support.
Data capture (Service, support, and advisory)
Specialist bots can be trained for specific data-capture scenarios, such as basic preliminary questions for a loan before meeting with an advisor.
AI Co-Pilot (Service, support, and advisory)
Agents and advisors will always have to converse directly to clients and customers, increasingly through convenient messaging interfaces. With Gen AI, the agent or advisor can get suggested responses to boost efficiency.
Gen AI in action
Service and support
What would a typical customer engagement journey look like? Here’s how Gen AI can be used for service, support, or advice.
The chatbot will now ask qualifying questions, allowing it to gain more information on Victor’s preferences.
The bot then offers Victor the option to chat with an advisor, schedule a meeting, or continue on his own. Victor talks to an advisor.
The bot now helps the agent during the Live Chat. The agent clicks “generate a response”.
Advice
With the mortgage progressing, Victor wants to see how his other assets are doing. He decides to contact his wealth manager via a Secure Messenger app.
Mary generates the report using the chatbot, drawing on information from the firm’s natural language processing LLM. She then shares it on the same channel, which is entirely secure.
The Gen AI impact with Unblu
With these capabilities, agents and advisors can increase efficiency while offering real-time support for issue resolution or when offering swift, accurate advice.
We are able to achieve this by including Gen AI as part of an overarching digital customer interaction platform experience, combining messaging, video & voice, and visual collaboration capabilities.
This has produced incredible results for our customers.
The banking sector is set to transform thanks to Generative AI. With the market conditions as they are, the time is now to implement the technology into banking services and banking processes to boost customer loyalty. It is the best way to ensure personalized customer experiences or offer financial advice in a cost-effective way.
While risk assessment and risk management activities need to be undertaken, the financial market trends show that this technology is not going away. Whether for report generation, investment strategies, or keeping on top of regulatory requirements, the mix of AI and human agents will give banking sector institutions that all-important competitive edge.
Interested in finding out more?
