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Generative AI Use Cases for the Financial Services Industry

Generative AI emerged in early 2023 and is delivering great results, and the banking industry comes as no exception. Two-thirds of top finance and analytics professionals who attended a recent McKinsey seminar on generation AI said they expected the technology to significantly improve the way they conduct business.

According to the McKinsey Global Institute, across sectors worldwide, generation AI may bring $2.6 trillion to $4.4 trillion in yearly value across the 63 use cases evaluated. Banking is predicted to have one of the greatest prospects among business sectors, with an annual potential of $200 billion to $340 billion (equal to 9 to 15 percent of operational profits), owing mostly to enhanced efficiency.

The economic effect is projected to benefit all banking divisions and operations, with the corporate and retail sectors seeing the largest absolute advantages ($56 billion and $54 billion, respectively). Notably, although banks have properly prioritized efficiency in their first-generation AI trials owing to larger pressures on banking economics, the technology has the potential to significantly transform how particular occupations are performed and how consumers interact with banks.

In this post, we will go into detail about how banks can use generative AI in their practices. So keep reading to know how you can benefit from ordering gen AI development services from a professional agency.

How Can Banks Use Generative AI?

There are many different ways in which generative AI might help banks increase productivity and bring their service to a whole new level. So here is how that works.

1. Credit Approval

The most perspective use case for generative AI in the banking industry is to review and prove credit applications, namely:

  • Loan application — Generative AI-powered chatbots can help clients navigate the loan application procedure. Banks may also utilize generative AI to check consumer information;
  • Credit analysis — Credit analysts may use generative AI to evaluate creditworthiness by examining consumer credit ratings and financial records. Furthermore, it can assess the risk of a loan application by analyzing data from numerous unstructured sources.

2. Loan Underwriting

Once applicants are authorized, loan underwriters may employ generative AI to expedite the underwriting process. Lenders may use generative AI to automatically construct portions of credit notes, such as the executive summary, company description, sector analysis, and more.

3. Creating a Pitchbook

Investment banking is a highly competitive, fast-paced business in which banks must outperform to get projects. Pitchbooks are essential for obtaining business, but they are incredibly time-consuming to create. Junior bankers must search through a variety of unstructured internal and external sources, assess data, and put it into the appropriate forms. Generative AI may be used to quickly acquire, analyze, and summarize information, as well as prepare draft reports for usage in the final output.

4. Effective Lead Generation & Customer Service

Generative AI-powered chatbots may engage with prospective customers, learn about their desires and preferences, and provide tailored services. Additionally, generative AI can help with payment reminders, billing questions, and account administration. It may also provide personalized loan repayment suggestions based on a borrower’s financial history. What is more, with mobile app development services, banking solutions become even more effective in terms of lead gen and customer support as users can access them whenever they need help or assistance.

5. Debt Collection & Personalized Financial Suggestions

Generative AI may also help with debt collection activities. It may communicate with debtors to give repayment choices, assess delinquency tendencies, and suggest suitable collection techniques. Apart from that, while current Machine Learning (ML) techniques are ideally adapted to predicting marketing or sales offers for particular consumer categories based on accessible criteria, such insights are not always easy to digest. For example, producing marketing emails or in-app messaging with specialized financial suggestions might take time. Gen AI can aid with the creative process of one-on-one personalized communications at scale utilizing conversational language. It may assist in increasing customer experience, retention, and cross-selling.

Putting Gen AI from Vision to Practice

Banking services leaders are no longer only testing gen AI; they are already developing and implementing their most creative concepts. Deutsche Bank, for example, is doing large-scale testing of Google Cloud’s generation AI and LLMs to deliver new insights to financial analysts, hence, increasing operational efficiency and execution velocity. There is a possibility to considerably cut the time required to complete banking operations and financial analyst activities, empowering personnel by enhancing productivity.

MSCI is also working with Google Cloud to expedite next-generation AI-powered products for the investment management sector, with an emphasis on climate analytics. Dun & Bradstreet has announced a collaboration with Google Cloud on next-generation AI efforts aimed at driving innovation across many applications.

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