Generative AI in Financial Services

Generative AI will automate roughly half of the job tasks in the financial sector between 2030 and 2060 and will inject at least $200 billion into the industry At least that’s what McKinsey analysts are predicting relying on the current cost reduction data in strategic planning for banks and other financial institutions.

Banks and insurance companies are open to the variety of opportunities that Generative AI might present across multiple functions. McKinsey sees the biggest potential for financial organizations in AI use across four major areas, which are customer engagement, coding, concision, and content generation.

At the same time, EY notices that BigTech, Fintech, banks, and non-banking fin companies are at different stages of generative AI acceptance, admitting that the most common applications of AI are found in marketing, sales, risk management, and customer support areas.


In this article, we are going to explore the potential of leveraging the current landscape and leveraging Gen AI capabilities in financial services, study the current use cases of the leaders of the industry, and try to come to a conclusion about which AI products will be in the biggest demand in the nearest future.

Where Financial Services Players Use Gen AI Capabilities Potentially

Generative AI has quite an impact on the marketing strategies of companies providing financial services. It could be useful for the onboarding of new customers. It also finds wide applications in product development, financial advisory, customer support, and risk management.

Let’s explore each of the areas, and find out which areas are targeted by GenAI startups, and which are currently overlooked, but have the potential.

Marketing and Sales

If you are a client of Bank of America and have chatted with their virtual assistant Erica about the retirement plans recently, I’d expect your e-mail to be flooded with “the best offers for those who care about themselves in their after-work years”. Content generation is one of the key areas targeted by Generative AI tools.

AI virtual assistant Erica

Generative AI is also helpful with customer segmentation and sentiment analysis. Patterns in the transaction history and social media interaction leave a trace of data that could be used for the creation of marketing materials in the future.

Natural Language Processing allows AI to gauge customer sentiment, finding nuances in the words it is being interacted with. Some banks even use real-time sentiment monitoring to quickly respond to negative feedback. The companies like Brandwatch or Sprinklr are the leaders of the industry, providing financial services businesses with incredible insights.

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Speaking of different processes AI could be helpful with, we can’t overlook onboarding. Generative AI improves the onboarding experience for banking customers, from KYC and ID verification to delivering hyper-personalization using AI tools. MoneyLive presents a comprehensive webinar dedicated to the matter. 

New customer experience is provided with intelligent document processing and chatbot assistance at the very least. That way, customers can have a better experience and utilize their capabilities wider and in full.

Product Development

Generative AI streamlines product development. It facilitates synthetic data generation for customer profiles and model training, and that accelerates the development cycle. Generative AI can also help with interpreting and generating code, which reduces the number of errors and bugs in the product code. GitHub Co-Pilot provides 50% faster time-to-merge, for instance.

Supportive programming, developing, and documentation creation acceleration is very helpful in product development.

Financial Advice

In the investment world, it’s hard to predict even such subtleties as the best tools to get advice. However, we can say that AI can generate recommendations based on research and analysis. At the very least, AI can create customized reports specifically tailored to clients’ interests. Those reports synthesize relevant data and insights so that the client can take action on them. JP Morgan uses AI-driven analytics and tailored investment reports to support relationship managers and clients.

Customer Support

We have briefly touched on those aspects earlier in the text, and overall we couldn’t help but notice that Gen AI is amazing at creating top-notch customer support. Who else can help you better to build real relationships with real people, other than the bot?

AI is useful with customer service support interface and chatbot operations, it can synthesize the needed information on policies and contracts. Chat and email monitoring by Gen AI can suggest to the employees the needed course of action for the conversations that could potentially become “toxic”.

Risk and Compliance Management

Legal teams can utilize knowledge databases provided by Gen AI tools. AML monitoring, compliance monitoring, and document creation can also be covered by these tools. Early warning credit monitoring and automated credit decision-making processes are possible in the future, but rarely executed these days, notices

Gen AI Use in Practice

We’ve briefly covered the areas with the biggest potential in financial services that could be covered with Gen AI. Now it’s time to have a look at what industry leaders are doing in that respect, what the acceptance rate is, and real-life use cases that are applicable right now.

Goldman Sachs

Goldman Sachs Research states that only 5% of companies are putting Gen AI to use. Areas in which companies are applying AI at the moment include chatbots, marketing, speech text, data analysis, and automation.


The researchers suppose that the set of tasks automated by the generative will be broader with time, and Goldman Sachs could serve as an example in the area.

Reportedly, their developers are internally testing generative AI tools to assist their code writing. This information was confirmed by the top executives. Marco Argenti, CIO of the company, admitted to CNBC that they are doing that at “a proof of concept” stage. However, the tool has been actively used within the company to help coders with writing code lines.

As one would expect, that helps boost the company’s productivity, and, therefore, competitiveness in a stiff market.

JP Morgan Chase

The company uses the generative AI in more ways than one. For instance, DocLLM, developed internally, is tailored for understanding visually complex documents (i.e. forms, invoices, reports). At the same time, SpectrumGPT analyzes big volumes of documents and research to provide insights for portfolio managers.

As Arezu Moghadam, JP Morgan Asset Management Lead stated, they have to teach their analysts what generative AI can and can’t do. It has been reported, that the use of these two tools helped achieve a 15% performance enhancement over “standard Chat-GPT-like tools”.

Their most recent tool introduced to the general public is called JP Morgan Chase’s IndexGPT, and it aims to reignite interest in thematic investing. That is to be done with the help of an efficient and accurate approach.

It’s too early to judge its effectiveness as it is still a very recently introduced tool, but we can see that the company is not laying low on its attempts to use generative AI as effectively and intensively as possible.

Morgan Stanley

They have introduced a tool for the employees at the end of 2023. It’s called Morgan Stanley Assistant, and it allows financial advisors to have quick access to the bank’s database of about 100,000 research reports.

The software is to answer common investing and personal finance queries. The Assistant is powered to recap client meetings or draft an email follow-up to it. The tool is to increase the efficiency of employees, enabling them to serve the clients more promptly.


Gen AI has found a way to transform various aspects of operations in financial services. It is widely used by marketing, risk management, and customer support departments. AI technology is evolving at a cosmic speed, and rightfully so, the competition among the leading companies in the sector is very intense.

We could expect significant advancements shortly, as even the biggest companies, often criticized for the lack of innovations, are not afraid to experiment in the field at the moment. Goldman Sachs, JP Morgan Chase, and Morgan Stanley are using and developing their own AI-run tools to increase productivity and augment certain operations within the company.

It is worth mentioning that deploying AI in the Financial Services setting demands following a well-rounded strategy and reliable partnership. Therefore, be attentive when you select your software service provider. Elinext is a reliable partner with vast experience in the financial software development service and banking domain in particular. Contact us to discuss your AI-run project.

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