Generative AI has immense potential to change the way we live and do things. Generative AI has the potential to impact things like our work, banking, and investment. Many speculate the impact of this technology is as significant as the advent of mobile devices or the internet. More than 80% of organizations consider using or already have generative AI in their workplace. The financial industry is no exception here as many believe generative AI personalizes financial services.
The first thing anyone should be aware of is the immediate boost in productivity and operational efficiency generative AI brings to the table. This boost is especially noticeable in financial services. Here, every service or product begins with a contract, agreement, or terms of service. Generative AI or Gen AI is especially talented at discovering and summarizing complex information. The benefit of Gen AI helps in areas like mortgage-backed securities, customer holdings, or contracts across multiple asset classes.
The first thing anyone should be aware of is the immediate boost in productivity and operational efficiency generative AI brings to the table. This boost is especially noticeable in financial services. Here, every service or product begins with a contract, agreement, or terms of service. Generative AI or Gen AI is especially talented at discovering and summarizing complex information. The benefit of Gen AI helps in areas like mortgage-backed securities, customer holdings, or contracts across multiple asset classes.
However, the uses do not end there. Foundational models like LLMs are trained on language or text. This training helps them gain a contextual understanding of human language and conversations. These capabilities are very helpful in automating, speeding up, improving, and scaling the marketing, sales, and compliance domains. Companies are also learning how generative AI personalizesd financial services, making it another avenue worth pursuing.
The best way to imagine Gen AI is to see it as an assistant or a coach to employees. This tool helps them be better at doing their job. It also helps them to hone in on high-impact and strategy activities. Coders for example benefit from using Gen AI like Codey. Codey is a family of code models built using PaLM 2. This helps drastically increase programming speed, comprehension, and quality.
Gen AI also addresses some of the acute talent issues in the industry. These issues include things like software developers, compliance experts, call center employees, risk experts, and front-line branch employees.
Role of generative AI in the financial sector
Generative AI in the financial sector is a big deal. After all, AI is powered by data and Finance draws upon vast quantities of data. Naturally, this sector has everything needed to take full advantage of generative AI. However, significant time and investment is required to make this possibility a reality.
Making sound decisions requires the leader to consider the use of Generative AI for the enterprise as a whole. They must have a clear understanding of which areas the technology will impact. CFOs and finance leaders are pivotal in driving strategic collaboration among top C-suite leaders and enabling greater success and ROI. The journey required to reach this success should start with a sound strategy and a few use cases for testing. The whole thing doesn’t require protection and instead requires control.
Implications for Finance Talent
The interesting potential to automate and augment processes through Gen AI is a tantalizing prospect. However, human talent remains a necessity as generative AI can’t reach its full potential without it.
Despite the benefits and need for human talent, many workers need to be more confident and trust the technology. Finance leaders should consider leaning into areas that support generative AI. This approach turns the technology into an essential co-pilot that all employees need.
There will be a change in the current roles and responsibilities. It also leads to new needs and requirements from the workforce. Financial professionals looking to add Gen AI to their infrastructure should consider enhancing skills such as
- Prompt engineering
- The ability to recognize possible bias
- Confirming the quality and validity of the generative output
- Monitor the model’s performance over a long time frame
Generative AI practical uses in the financial industry
At a glance, generative AI has three main features that are a boon to institutions and businesses. They are
- Turning online interactions into conversations. Examples of this include customer service automation, knowledge access, conversational journeys, and more.
- They are turning complex data into accessible information. Examples here include product discovery recommendations, enterprise search, business process automation, and others.
- Creating content with minimal effort.
Selecting a single use that solves a specific business problem is a great starting point when using Gen AI. The tool should have an impactful benefit to the business and be grounded in the organization’s strategy. That approach helps you measure results better when the time comes. We’ve compiled a list of use cases to help you understand the fundamental benefits of Gen AI
Search and synthesis of financial documents
Banks often spend a lot of their time looking for and collating information and documents internally among their employees. That much time spent means less time spent with their clients and no one wants that. Generative AI can personalize financial services here in that it helps the employees find and understand information in the contracts. This information includes things like underwriting, trading, policies, credit memos, and more.
Imagine this scenario, Gen AI helping a bank analyst accelerate the speed at which they generate reports. Here, the AI researches and summarizes vast quantities of economic data and other statistics found around the world. The tool also helps corporate bankers prepare for upcoming customer meetings. Here, the AI creates intuitive and comprehensive pitch books and other presentation materials. These documents all drive engaging conversations.
Better virtual assistants
Often, customers require help in finding answers to a very specific problem only they are facing. These problems are unique and hence, not programmed into existing AI chatbots. The problem isn’t available in the knowledge libraries that most customer support agents typically rely on. Imagine helping a customer resolve an instance of fraudulent transactions. That type of information isn’t readily available in the regular AI chatbots or knowledge libraries.
Here is where generative AI personalizes financial services. The tool excels in finding answers among vast quantities of data, collating them, and summarizing them before sending them to the agent or chatbot. Take it a step further and Gen AI-powered chatbots make the whole interaction look conversational. These skills help improve the customer service experience. Imagine Gen AI speeds up the process of credit card fraud resolution. That sounds like a benefit for the customers and the agents involved.
Capital markets research
Investment firms typically analyze company filings, reports, transcripts, and other complex data in multiple formats. This work is done to get a grasp on global markets and risks. That way, they can query the data and fill their knowledge bases.
Here, Gen AI tools act like research assistants for investment analysts. These assistants can sift through millions of transcripts, earnings calls, company filings, macroeconomic reports, regulatory filings, and other sources. Then, the tool identifies and gathers the key information before summarizing it and presenting it to the analysts.
Regulatory code change consultant
The financial services industry is one where new regulations emerge every year. It is also one where existing rules change often. These changes translate to a lot of time and effort spent on interpreting the new requirements to ensure compliance. Developers have to understand the underlying business changes and change their code accordingly. They must also cross-check their changes against a repository, and provide documentation of the same.
Gen AI here gives developers the necessary context they need about the underlying changes. The AI provides answers or links to specific pages that have the answers and helps them understand the underlying business changes. It also assists in automating any coding changes and helps in cross-checking code against a code repository.
Presently, developers have to make sweeping coding changes to meet international banking regulation requirements. This process normally involves going through documents that are one thousand pages long at a minimum. Gen AI can summarize the relevant areas of the documents and help the developer understand the context. It can also identify the parts of the framework in need of a code change and cross-check the code with an existing repository.
Personal financial recommendations
Here, we see the best example of how Generative AI personalizes financial services. The existing ML tools are excellent at predicting the marketing or sales offers for specific customer segments. However, it is not easy to quickly act on those insights.
Imagine creating in-app messages or marketing emails with specific financial recommendations. That task is very repetitive and time-consuming. Generative AI personalizes financial services here through conversational language. The tool can improve the customer experience, cross-sales, and retention.
Generative AI use cases
So far, the article covered the potential uses of generative AI in the financial industry. Now, here are examples of Generative AI personalizing financial services in the field.
Capital one
Capital One is an American bank holding company that specializes in credit cards, savings accounts, auto loans, and banking. The company utilizes Gen AI algorithms to improve its fraud detection capabilities. Here, the AI analyzes transactional data and customer behavior. The company’s system can identify possible fraudulent activities and take action right away. This approach goes a long way to mitigate risks. Also, this proactive approach greatly reduced instances of fraud while enhancing the customer’s account security.
PayPal
PayPal is an American multinational financial technology company in charge of an online payment system in countries that support online money transfers. The company uses Gen AI algorithms to combat fraud and money laundering schemes. The AI constantly monitors transactions and analyzes patterns taking place on PayPal. That way, it can detect suspicious activities and flag potentially risky transactions. This powerful fraud detection system is a boon to customers and the financial ecosystem’s integrity.
Generative AI use cases
Financial service leaders are constantly looking for innovative ways how Generative AI to personalize financial services. Big companies like Google, Capital One, and PayPal have the investment and teams needed to integrate Gen AI into their infrastructure. However, they don’t have to be the only ones reaping the benefits of generative AI.
Any company looking can partner with or contract the help of generative AI development companies. These companies understand the intricacies involved in creating a generative AI and have the technical expertise to develop the technology. Companies like Ruah Tech are among the best when it comes to creating generative AI models that help you harness the full power of AI to your benefit.
