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10 Ways Generative AI Transforms Banking Industry

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Imagine a world where fraud detection happens in real time, where customer needs are predicted before they arise, and where every interaction with banking is an unparalleled experience.

In this article, we delve deep into the impact of generative AI on banking, unlocking its potential to reshape the banking sector and redefine our engagement with financial services.

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Table of Content

Use Cases of Generative AI in Banking​

Below, we've outlined 10 ways Generative AI could be used to improve productivity in banking.

1. Enhance Customer Interactions using AI Chatbot​

Generative AI platform is revolutionizing customer service through advanced chatbots. Many banks have embraced the use of Generative AI chatbots to handle customer inquiries. Generative AI enables bankers to utilize expansive language models that engage in conversations similar to humans. Instead of having to navigate through a set of options, customers can now express themselves in natural language, like asking 'What's my savings balance?' or 'Could you update my contact number?', and receive prompt and human-like solutions.

2. Optimize Portfolio Management​

Generative AI services enable banks to take a more granular approach when advising clients on portfolio strategies. First, the deep learning model is trained on large amounts of economic data, then the Generative AI technology were used by banks to forecast future developments based on various dynamic financial factors, such as exchange rates, inflation and policy dynamics, to create a suitable portfolio. This is done without the need for clients to reveal their financial positions, creating a more private and comfortable banking environment.

3. Streamline Compliance Management​

Nowadays, Compliance management is a constant challenge for banks, as they must adhere to strict rules and regulations. This includes monitoring transactions, consolidating pertinent data, and submitting it to the relevant departments in a timely manner. Implementing an Generative AI model that has been trained on these processes will reduce the workload associated with compliance management. For instance, bankers employ Gen AI for analyzing customer data and making sure they are compliant with KYC Act before they approve an account.

4. Empower Financial Analysis & Forecasting​

Banks need to plan ahead and stay ahead of evolving market conditions. Generative AI application allows banks to run simulations, forecast economic events and position changes. For instance, Generative AI modeling can be used to predict the inflation rate over the medium term and adjust the interest rate accordingly.

5. Personalized Financial Advisory Services​

Generative AI platform enables banks to deliver customized financial advice for individual needs, by analyzing customer historical data, spending behaviors, and risk appetite. This not only boosts product sign-up rates, but also fosters stronger customer retention through a more personalized banking experience.

6. Revolutionize Fraud Detection using Generative AI​

As data breaches become more prevalent, regulators are putting pressure on banks to protect their customers' interests and stop fraudulent activities. With the help of Generative AI modeling, banks can detect anomalies in large amounts of transactions and issue alerts right away. This enables banks to stop suspicious transactions and protect customer confidence.

7. Improved Loan Score Management​

There are several factors that bankers consider before granting or denying a loan. Generative AI technology assists in credit scoring by analyzing various criteria, including salary, age, occupation, and credit indicators. This enables comprehensive evaluation of loan applications, improving the accuracy of loan score management and aiding in better decision-making.

8. Automation of Back-Office Processes​

Integrating Generative AI platform into back-office processes automates regular tasks such as document scanning and personal identity verification. Natural Language Processing (NLP) software can efficiently scan, process, and categorize physical documents, reducing the workload on operational staff and enhancing overall efficiency.

9. On-Demand Financial Report Generation​

Generative AI equipped with machine learning models, can instantly generate financial statements on demand. Customers can request customized reports, which AI compiles the information into well-structured files in a matter of seconds, providing real-time insights into their financial standing.

10. Streamlining Legacy Software Maintenance​

Instead of rewriting code from obsolete programming languages, developers in the banking sector leverage Generative AI applications and large language models to generate code efficiently. This not only enhances coding efficiency but also reduces errors during the migration of software to newer programming frameworks.

Where Generative AI Should Be Used?​

Generative AI in banking sector emerges as a transformative force, reshaping traditional practices and generating progress in key domains. Its application is most profound in three pivotal areas:

  1. New Product Development
  2. Customer Operations
  3. Marketing & Sales

1. Accelerating Innovation in New Product Development​

Banks are utilizing the capabilities of generative AI in their latest product development endeavors to expedite software delivery. These generative AI tools have the capacity to interpret code, identify and rectify bugs, enhance the readability and testability of legacy code, and document outcomes. Entities such as exchanges, information providers, payment companies, and hedge funds routinely publish code.

In our experience, frequent users of these tools could potentially cut the time to market in half for numerous code releases. The influence of generative AI is especially noteworthy in critical domains like clearing systems, where it has the potential to save time and diminish the need for rework efforts.

2. Efficiency Revolution in Customer Operations​

In customer operations, Generative AI plays a pivotal role by extracting, searching, and summarizing unstructured servicing data. It then transforms this data into machine-consumable instructions. In post-trade services, Generative AI takes on the task of reading documentation related to corporate actions. Banks employ Natural Language Understanding (NLU) to assess the impact of these corporate actions across diverse customer profiles and products.

Generative AI becomes a valuable asset in automating manual workflows, streamlining processes for increased efficiency. Furthermore, Generative AI is continually enhancing its capabilities, now demonstrating proficiency in crafting technical documents, including those pertaining to Environmental, Social, Governance, and Governance (ESG) matters, as well as audit reports.

3. Revolutionizing Marketing and Sales Dynamics​

In the marketing and sales domain, Generative AI is revolutionizing the approach taken by bankers as relationship management. Generative AI possesses the capability to automate various client interactions, spanning voice calls, text messages, and face-to-face meetings. By integrating a Generative AI-powered tool into your relationship manager's desktop, the automation extends to encompass a list of tasks, from generating investment ideas to facilitating sales and implementing product policies almost instantly. This innovation has the potential to dramatically reduce response times, transitioning from hours or days to mere seconds.

In short, while the potential benefits of generative AI in banking are considerable, it also raises challenges and risks that need to be managed prudently. As banks develop new generative AI models, they'll also need to re-engineer model risk governance framework and implement a new set of controls. Financial institutions should approach the adoption of AI with a holistic approach that includes protecting data privacy, adhering to regulatory requirements, and ensuring transparency and accountability.

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