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Generative AI in Banking and Financial Services: Case Studies

Updated: Apr 8

Generative AI in Banking and Financial Services: Case Studies

Banking and financial services have undergone digital transformations in the past few years, improving, efficiency, convenience, and security. Generative AI is now making a significant shift, the surveys conclude that 78% of banking and financial institutes are planning or already implementing Gen AI. About 61 percent foresee an impact on the value chain, empowering responsiveness and efficiency. Globally, the BFSI sector predicts a 5 to 10-year timeline for strategically investing in areas such as customer service and cost-cutting. 

Among the BFSI sector, banking is going to have the largest opportunities with a potential of $200 to $340 billion annual growth. The economic impact is likely to benefit the banking sector and function, with outright gain in the retail and corporate sectors. 

Morgan Stanley adopts AI Assistant for Financial Advisors

Morgan Stanley, an investment bank and wealth management firm has recently unveiled their internal Gen AI model for financial advisors and support staff members. This AI model is built using Open AI’s GPT-4, which helps thousands of wealth managers find answers quickly from a massive internal knowledge base; also condenses the client meeting contents and creates follow-up emails. 

Banking giant Morgan Stanley has already implemented an artificial intelligence-powered assistant, ‘AI @ Morgan Stanley Assistant’ for its financial advisors and support staff members to access over 100,000 documents and research reports. 

In a recent memo, Morgan Stanley's co-president Andy Saperstein said “Financial advisors are the center of Morgan Stanley wealth management universe” also he added, “ Generative AI revolutionizes client interactions, bring efficiencies to financial advisors, and ultimately helps free up time to do what you do best: serve your customer.”  

The AI model tends to save the time and effort of financial advisors and customer support staff on administrative and research tasks questioning related to markets, and internal processes, so the advisors focus more on engaging clients effectively.

As a result, the bank reported that ‘the time to produce an investment is cut to more than 90 percent (from 9 hours to 30 minutes)’ using gen AI. 

Morgan Stanley also planning to unveil more AI tools, which include running a pilot on an AI program called Debrief, which will summarize client meetings and create follow-up emails automatically.

ABN Amro Bank Trialing Gen AI across 20 Contact Centers

ABN Amro, a leading Dutch bank is trialing generative AI in 20 contact centers, disclosing that the bank is expanding the technology to more than 200 employees as part of the co-pilot. 

Traditionally, the bank agents make notes during a customer call to create a summary of a conversation. Now, the bank is using Open AI’s ChatGPT to create these summaries. By summarizing and analyzing customer conversations, gen AI lets center agents save time and effort on administrative tasks, instead focus on providing personalized customer support. 

Annerie Vreugdenhil - an executive board member at the bank said “The bank advances when it comes to generative AI, it might have a competitive advantage for a short period”. She added, “The organization’s agents are now happy as the technology makes them productive.” 

Citigroup benefits Generative AI to Read 1,089 Capital Rules Pages

Citigroup Inc. gave access to generative AI to their 40,000 coders as Wall Street continues to hold the growing technology. The bank uses generative AI to produce sentences or essays based on the user’s commands or prompts. The technology was trained first with vast quantities of pre-existing knowledge resources. 

Bank executives use this generative AI technology to train their staff to be more efficient. When federal regulators published a document of 1,089 pages of capital rules for the US banking sector, Citigroup Inc. thoroughly read the entire document word by word using Gen-AI. 

The compliance department of the bank used this technology to evaluate the impact of the plan, which determines the capital the lender has to set to safeguard against future losses. 

Citigroup has also been using large language models to summarize the regulations and legislation in all countries they operate so they comply with those rules. As a global bank, enduring regulations in each jurisdiction can be difficult. 

HSBC to Develop ESG Virtual Experts Using Generative AI

HSBC, a European bank is using generative AI to develop environmental, social, and governance (ESG) virtual experts that extract and synthesize unstructured information from long documents. The model responds to complex questions based on a prompt, extracting information from visuals and tables. 

HSBC reported the significant cost reduction associated with the back-office operations. The bank has over 100 use cases that are currently running. The use case includes content optimization to personalized marketing to the customers. More importantly, the bank is investing in security and resilience, primarily using Gen-AI to empower fraud detection.

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For organizations looking for the best in-house training model to ease the onboarding and training of employees, is the perfect solution for them. 

Efficient Product Knowledge Dispersal

Train on your organization-specific information stored in various resources like PDFs, Articles, Emails, Manuals, or YouTube videos. Once the model is trained, the sales agent now has access to the entire information about the product and will be able to respond to any complex query received during a client call.  

Handle Customer Queries and Enrich Customer Experience

The traditional training does not educate them to deal with complex queries and how to respond to them. To this, the reps have to hunt the knowledge resources which is of course a time-consuming process and the customer does not hold the conversation for this long. 

Here, serves as a virtual assistant that responds to customer queries. The sales agent needs to give a query as input, and the model provides the solution quickly, and the agent can simply answer the same to the customer. 

Update the Model with On-demand Training

Updating is as easy as sending a WhatsApp message or an email. Update the model simply by uploading the updated resource or giving a prompt with updated information in the trained model. The updated information will be saved in the model and available to respond to any query.


Faster Sales and Servicing Team Onboarding

On average, the time required to onboard and train a Sales Executive or agent is 50-60 days (2 months) which also requires resource usage. Even after the training, productivity would be a concern that would ultimately impact the business growth. Therefore, organizations should shift the traditional training method, and is one of the effective approaches. The solution offers vernacular capability in all major languages.

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Get the highest level of data security, deployed as a separate instance. Organizations don't need to worry about service interruptions or AI models using their internal knowledge for training some other model. 


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