Generative AI in Finance: Supercharge Your Workforce

The continuously evolving and developing generative AI makes people ponder over the elements of proper usage of this technology. The finance industry is not behind, as it has started making changes with the inclusion of generative AI and finance. With the use of generative AI in finance, it’s no longer about what we can do, instead now it’s about ‘what we should do’.

The encoder takes the input sequence such as the text data of financial records and generates a contextualized vector for each element. These representations are then decoded to output sequences; this is common when used in tasks such as translation or text summarization.

If you are seeking a technology companion, Biz4Group is your reliable friend; who offers generative AI development services and advisory for the finance industry and helps to build your innovatively advanced business. Being proficient in the implementation of several state-of-the-art LLM models and solutions, Biz4Group provides you with the initial boost or the continued advance in your AI experience.

Generative AI has potential to be used as an assistant to the human employees in various aspects. The first one is an AI coding assistant that, for example, aids program developers in creating financial software and detecting defects. Generative AI is used by Goldman Sachs for coding help to the programmers.

Also, some multinational institutions which are using generative AI in banking and finance industry are Morgan Stanley, Deutsche Bank and Mastercard.

According to Statista, generative AI in the finance industry was around $0.85 billion in 2022, and it is expected to grow to $9.4 billion in 2032, with a CAGR of 28% for the forecast period 2022 to 2032.

Also Read: Create Future of Shopping with Generative AI in eCommerce

Generative AI Use Cases in Finance

There are multiple generative AI applications in finance. Let’s explore them:

1. Generating applicant-friendly denial explanations

AI is extensively used in the banking industry, especially in loan approval processes. It aids the banks and similar organizations in evaluating the customers’ credit reliability, deciding the right amount to lend, and the right interest rates of the loans to charge.

Nonetheless, decision-makers and applicants of a loan require explanations that feature the underlying AI-based decisions, such as the rejection of an application, and customer awareness for the future loan applications.

2. Personalized Customer Experience

In the modern world, where the competition has increased tremendously, offering a personalized experience to a client is a major strategy that most financial institutions adopt. Advancements of generative AI in finance are shaping the customized advice customers receive and the clients’ account portfolio. It does so by integrating an individual customer’s attributes and his/her specific characteristics.

With the help of the customer databases, which contain different details of the client’s operations, including the general spending data, personal objectives, and statistical data, AI generative models can create an individualized list of recommendations applicable to each customer.

Being a generative AI development company, we have provided custom chatbot development solutions to one of our clients, which helped their business to enhance customer service while bringing the operational costs down in a long time.

3. Business Risks and Credit Rating

Generative AI in finance helps in the elements such as risk assessment and credit scoring since it is expanding the interests of financiers and banks. Thus, the use of generative AI models will extend the effectiveness that possesses the credit risk analysis and, thereby, contribute to more effective approaches in the processes of loan approval.

The benefits of applying the generative AI for finance in the assessment of risks and credit rating are not only more accurate results. By automating the analysis of borrowers’ financial history and current data, generative AI models can:

• Define possible threats and learn how to assess them adequately.

• Reduce on paper-based methods of record taking as this may end up being tiring and thus would take a lot of time.

• Help in the decision making particularly in the lending activities of the financial institutions especially the granting of loans and mortgage lending.

4. Chatbots and Virtual Assistants

Sophisticated chatbots and virtual assistants fall under the cognitive and conversational AI through which customers can interact with brands and companies amusingly and on their own in a spoken language based on NLP and relative to the customer’s previous or current activities.

With the help of generative AI in finance, virtual agents can provide more natural and relevant answers to the questions of customers which an increase in the latter’s satisfaction and loyalty can facilitate.

Adoption of AI chatbots in banking has the following advantages such as;

Some of the customer service processes implemented disrupt frequently routine processes; financial institutions can save time that would otherwise have been used on routine processes, hence offering more value than merely fulfilling simple customer service needs of their clients.

5. Trading and Investment Strategies

The use of generative AI in finance has started changing trading and investment in the finance and banking industries. Due to the historical market data analysis and generative AI models’ ability to perceive patterns and generate trading signals, trading execution quality can be adjusted to satisfy clients and adapt to market conditions.

Most trading systems used earlier involve the application of technical and fundamental analysis, which has a weakness of taking a longer period to make a call and yet the market conditions change from time to time.

The implementation of generative AI in finance can only use predetermined algorithmic patterns commonly as turned in previously successful or prevailing strategies, the generative AI models can learn from a data set and bring fresh strategies that can adapt to real time or new conditions in the trading and investment activities.

With generative AI in finance and banking industry, there are multiple benefits of chatbot in banking like chatbots 24/7 availability, handling high volume tasks with a chatbot and linguistic flexibility.

Also Read: AI in Real Estate Investment: Trends and Insights

Case Study- Worth Advisors Developed by Biz4Group

Biz4Group developed ‘Worth Advisors’ for one of its clients, a fintech software solution which works as a centralized tool for a person to create or update their financial budgeting or financial forecasting plan. The specific user-oriented input data is used by the platform to analyze the user’s profile and even provide reports and financial advisors to help them fulfill the monetary objectives. The functional modules include form entry, data processing, report generation, and advisory services to reduce the gap between application and user.

Benefits:

Automation of Processes: The flow of data collection till the report generation is fully automated and manual errors can be minimized.

Enhanced Security: Security of data means that the data is protected through technological techniques such as encryption.

Task Management: Through the implementation of this idea, users will be in a position to allow their colleagues to work on various tasks and at the same time monitor their workflow.

Efficient Document Transfer: It saves time to complete tasks due to the fast and simple transfer of the documents necessary for operations.

Targeted Data Delivery: The software has tools for delivering the most accurate data with reporting functionality.

Increased Productivity: Efficiency is increased by automation and properly structured workflow.

Improved Decision-Making: Reports generate more details on clients, and analysis assists in coming up with sound decisions.

Enhanced Communication: Greater coordination and peer-to-peer communication by incorporating the features within the software.

Also Read: Generative AI in Education – In-Detail Insights by Biz4Group

Key Features:

Client, Employee, and Admin Dashboards: Organizations will have some panels for one role, and others for the differing roles of the people working in the organization.

Notifications: Users receive notifications on their tasks, and changes that occur in relation to the tasks.

Reports and Document Management: Leveraging AI development services, Worth Advisors allows document and report upload, view and download accessible for the users in a hassle-free manner.

Task Management: Tools to enable creation of tasks and distribution and monitoring of tasks.

Reusable Technical Components: Reduces complexity and offers higher accuracy of the application package and the deliverables.

Quality Assurance: They achieve the best coding practices as well as undergoing thorough QA checks to guarantee performance and security.

Conclusion

Generative AI in finance offers multiple advantages as it helps in resolving many challenges within the finance industry. Where traditional solutions are often limited and prone to errors, financial institutions can automatically analyze data with machine learning and significantly increase the accuracy of the result. Specifically, it can be used to define and deliver specific customer communications, manage risks, and analyze multiple chunks of data to make adequate decisions.

Indeed, it will be interesting to watch generative AI advance function by function and become steadily more effective at redesigning the landscape of finance for the interaction of institutions and their clients. The acceptance of AI in fintech enables organizations to work more efficiently and develop superior services to their customers thereby enhancing market competitiveness.

Meet the Author