Banking is one of the industries that Generative AI will most impact. According to Accenture’s research, an astonishing 90% of working hours in this sector can be somehow impacted by Large Language Models. This revelation highlights the immense potential and numerous ChatGPT use cases for banks.
With such transformative applications, the question for the banking industry is not whether to integrate ChatGPT but rather how. The same research by Accenture reveals that approximately 54% of working hours in banking have a high potential for automation by AI. In contrast, other industries have a medium potential of around 40% for automation.
The prospects are promising, as Accenture predicts a remarkable 30% increase in employee productivity by 2028, thanks to AI technologies like Large Language Models (LLMs).
Generative AI is highly versatile and essential for improving banking operations and driving growth. From the front office to the back office, it can revolutionise how financial institutions interact with customers and manage their internal processes. Its implementation promises increased efficiency, enhanced productivity, and improved customer experiences.
We see several areas in banking where Generative AI may be of great use:
1. Personal effectiveness of employees, getting rid of routine, support in creative tasks.
2. Specific banking and fintech tasks.
3. Efficient and cost-effective customer interactions.
4. Creating new value for customers.
Let’s dive into details and see several important ChatGPT use cases for businesses.
1. Marketing: advertising and testing, PR and press releases, idea generation, and task setting.
Generative AI empowers marketers to scale and distribute hyper-personalised content, tailoring marketing messages to specific customer groups and individual clients. It can analyse customers’ preferences and online behaviour. From there, it will split leads into segments, for which you can create different buyer personas and plan targeted marketing campaigns.
The technology helps track conversion and customer satisfaction rates, enhancing marketing effectiveness and engagement.
Generative AI streamlines processes in recruitment by assisting with CV screening, creating job descriptions, and conducting initial web chat interviews.
Think of such ChatGPT business use cases as mass recruiting. AI can scan resumes for job description compliance and then create short lists of candidates to be contacted. Additionally, it aids in employee onboarding, training, and answering common queries based on the company’s documents and internal data.
3. Daily tasks: follow-up meetings, task setting, and creating presentations.
GPT is invaluable in daily tasks, including follow-up meetings, task setting, formalising ideas, and preparing presentations. It can summarise key points during sessions, write meeting minutes, and generate concise follow-ups, saving the staff time and effort.
Moreover, AI facilitates project management, creating timelines, task lists, and decomposition plans. It is handy for crafting data-driven reports, financial results, and business proposals for clients and stakeholders.
4. Legal and accounting: document preparation, essence extraction, risk analysis, filling, inputting, and classifying documents.
AI can create well-structured, coherent, and informative reports based on available data for financial reporting, ensuring consistency, accuracy, and timely delivery.
Imagine easily analysing and including balance sheets, income statements, and other documents in your reports.
According to Forbes, around 10% of a bank’s cost base is technology-related, including maintaining legacy applications and code. Generative AI accelerates development processes, acting as a developer co-pilot to generate, test, and document code more efficiently. This results in significant cost reductions.
Banks can fine-tune and customise AI models using their own data, enabling tailored solutions and improved performance.
However, alongside these advantages, banks need to prioritise responsible AI usage. Setting guardrails for acquiring, refining, and deploying data is crucial to maintain ethical practices. Managing regulatory and privacy risks becomes paramount, ensuring customer data and sensitive information are protected throughout AI implementation.
Specific banking and fintech tasks
1. Market analysis and investment activity support.
Generative AI, exemplified by BloombergGPT, showcases exceptional market analysis and investment support capabilities. By leveraging historical financial data, these sophisticated Language Models can effectively predict trends and risks, providing valuable assistance to financial traders and investors for well-informed decision-making.
The AI models can efficiently search, analyse, create reports, and derive actionable insights from the vast amounts of data available.
BloombergGPT, a powerful LLM with 50-billion parameters, has been specially tailored for finance and was introduced in March 2023. Its extensive training on diverse financial datasets makes it a valuable resource for traders, investors, and journalists. The model’s versatility extends to various Natural Language Processing (NLP) tasks, making it a valuable asset in the financial domain.
2. Fraud analysis, KYC, and risk management.
One of the most important fields for Generative AI applications is fraud detection. AI models can be trained to recognise legitimate and fraudulent behaviour, enabling the identification of suspicious activities and prompting alerts or automated measures.
For Know Your Customer (KYC) processes, LLMs personalise the customer experience and identify potential risks efficiently. However, it is vital to address challenges related to data privacy and potential biases that may arise.
3. Scoring based on customer history and data.
Credit analysts can leverage generative AI to assess creditworthiness by analysing customer credit scores and financial history.
By examining data from various sources, AI models can measure the risk level of loan applications and assist in debt collection efforts. AI solutions engage with borrowers to provide repayment options, identify delinquency patterns, and recommend suitable collection strategies, streamlining the credit evaluation process.
Generative AI’s capacity to summarise large documents finds valuable use in finance, especially for dealing with vast amounts of paperwork. LLMs can process, summarise, and extract crucial information from financial records such as annual reports, financial statements, and earnings calls.
Additionally, ChatGPT and models alike will remove mundane work from compliance and regulatory departments that are usually overloaded with tasks. AI can automate regulation assessment tasks and streamline operational processes while cutting costs.
Efficient and cost-effective customer service
AI-powered bank assistants are already automating everyday support tasks like card activation and money transfers, providing faster and higher-quality service to clients. They are also helpful with more complex functions like instant data entry, providing information, and setting budgets.
However, Generative AI takes it further by producing more natural and relevant responses, making customer interactions seamless and human-like.
In contact centres, Generative AI enables agents to take action through automated notes during calls, enhancing the customer experience with tailored insights.
Generative AI’s understanding of human language patterns allows it to answer customers’ financial queries accurately.
With proper training on specific datasets, AI chatbots can respond correctly to various financial questions, including those related to loans and other products. For example, according to the test results, BloombergGPT gives more accurate answers to some finance-related questions than other models.
This technology benefits customers seeking assistance and also empowers financial advisors to generate applicant-friendly denial explanations, making the process more transparent and helpful.
In the future, artificial intelligence models will be able to analyse social media and information platforms to track brand reviews and customer sentiment. Through sentiment analysis, AI models can already recognise emotional tones and suggest appropriate responses.
Generative AI assists personal managers in working with VIP clients by summarising their interactions and providing personalised insights and recommendations. This technology strengthens the bank’s relationship with VIP clients, offering a tailored and exceptional level of service.
The AI-powered system also helps with risk assessment and mitigation, providing real-time support, automated reporting, and scheduling assistance to ensure a smooth and seamless customer experience.
Generative AI-powered virtual assistants provide personalised financial recommendations by analysing customers’ spending patterns, income, and financial data. These assistants can offer advice on saving, investing, debt management and even guide customers through loan applications and repayment options.
Additionally, they excel in handling financial instruments, answering queries, and performing routine tasks, significantly improving customer interaction with the bank. They can help build a knowledge base and search for information across large documents, like different forms of financial agreements.
Imagine you ask a chatbot a question like “What are my conditions for early repayment of a mortgage?” and it will give you a correct response by analysing the documents.
2. Personalised news and content formats.
Banks are leveraging Generative AI to create customisable news feeds tailored to customers’ interests, risk tolerance, and financial objectives.
The AI can also assist in generating interactive infographics and animated explainer videos, enhancing the way customers receive financial information. Voice-assisted banking with Generative AI integrated into platforms similar to Amazon Alexa or Google Assistant further enhances content accessibility.
3. Smart up-selling and valuable services.
Generative AI utilises customer data to provide personalised product recommendations, including customised credit cards that align with customers’ spending habits, financial objectives, and lifestyles.
For instance, when a customer requires assistance determining their affordable mortgage amount, the LLM efficiently computes an accurate budget by considering essential details like interest rates, down payment amount, and credit score.
By tailoring services to individual needs, this approach draws in new customers and fosters customer loyalty, ensuring the delivery of relevant and valuable offerings.
Additionally, Generative AI is vital in educating customers on various financial literacy topics, expertly addressing queries regarding credit scores, loan procedures, and other pertinent subjects.
4. Education: courses and Q&A sessions.
Banks can gamify financial education using tools like ChatGPT, making learning engaging and interactive through quizzes, simulations, and challenges.
Infographics and interactive content explain complex financial concepts more effectively. Internal training and development also benefit from AI, keeping employees up-to-date on new financial products, compliance, customer service skills, and more.
AI-powered Q&A sessions facilitate interactions with employees and customers, addressing queries and providing relevant information.
The integration of Generative AI in banking comes with its share of risks that banks need to be prepared for the following:
1. Model hallucinations
LLM models tend to generate answers that sound authoritative, even when they lack accurate information. It can lead to misleading responses and misinformation, potentially causing confusion and harm to customers if not carefully monitored and managed.
Learn more about prompting techniques to avoid AI model hallucinations and get the most accurate results.
2. “Black box” thinking
Understanding the output of Generative AI models can be challenging due to their complex nature. The models operate as “black boxes,” making interpreting the reasoning behind their responses problematic. This lack of transparency may lead to difficulties in identifying errors or potential biases in the AI-generated content.
3. Biassed training data
AI models heavily rely on the quality and diversity of the training data they receive. If the training data contains biassed or skewed information, the AI-generated output can amplify these biases. It could result in discriminatory or unfair responses, impacting customer experiences and perceptions.
4. Data privacy and security concerns
Implementing LLMs on-premises for sensitive banking operations raises data privacy and security concerns. Banks must ensure strict protocols safeguard customer data from unauthorised access, breaches, or misuse.
To mitigate these risks, banks must establish rigorous monitoring and validation processes for the AI models and use tools like data masking.
Tovie AI can be a trusted partner for financial organisations seeking AI solution integration. As the sole Conversational AI provider with the IBM Cloud for Financial Services validation, we offer Banking and Insurance solutions now accessible on Red Hat Marketplace.
Generative AI, especially ChatGPT, has numerous use cases in the banking sector. It promises increased efficiency, improved customer experiences, and employee productivity growth by a third.
From marketing and HR to fraud analysis and customer service, there are various ChatGPT use cases for banks that can revolutionise banking operations.
However, responsible AI usage is crucial to mitigate risks like model hallucinations and private data security.
Tovie AI, a trusted partner with IBM Cloud for Financial Services validation, offers Banking and Insurance solutions. Contact our team AI’s full potential in the financial industry.
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