If you’re here, your company is probably looking to integrate generative AI into its digital transformation strategy. But with so many possibilities, it can be tough to know where to start. This article covers the most practical and impactful use cases for AI in financial services.
In 2025, AI automation of interactions and workflows is the standard that your clients, employees, and partners expect. Your competitors are probably already working on these innovations, so starting now is crucial to staying ahead.
Gen AI for financial services can and is already significantly impacting core operations. It helps teams improve speed and accuracy in analysis and reporting, reduces manual work, supports compliance efforts, and speeds up policy updates. It also enhances communication, both within your organisation and with your customers.
Generative AI-powered voice bots are changing customer service by making phone support faster and more efficient. These bots can simultaneously handle hundreds of calls, process real-time requests, manage interruptions, and maintain a natural, human-like tone.
Call centres often struggle with high call volumes, scheduling challenges, and routine enquiries, leading to long wait times and inefficiencies. Meanwhile, customer expectations are higher than ever – people expect quick, tech-driven solutions. This is where the generative AI use cases in financial services can potentially transform the customer support landscape.
AI-powered voice bots for inbound call automation help manage routine calls, such as PIN reset, balance enquiries and other common requests, so human agents can focus on more complex issues. This improves response times and ensures a smoother customer experience.
But the real advantage isn’t automating simple tasks. It’s freeing up teams to handle conversations that require empathy, problem-solving, and a human touch.
Key functions:
- Responds to common customer enquiries
- Confirms security & identity related questions
- Records relevant data
- Can manage payments and notify of overdue payments for debt collection.
- Hands over to human agent or notifies agents via email if required
- Can handle FAQs
- Establishes and confirms next actions
Results:
- Faster call management leads to shorter wait times and better customer satisfaction
- First-line support efficiently handles routine inquiries, boosting operational efficiency
- Seamless integration with customer service systems improves information flow and service quality
- Overall, the call centre becomes more productive
Imagine insurance claims being processed quickly and accurately. No long phone calls, no frustrating delays – just a smooth experience for customers and a more efficient workflow for insurers.
An AI-powered claims bot provides 24/7 support across multiple channels, guiding customers through the process and improving the chances of resolving claims on the first attempt. Customers can update their claims anytime, receive clear and timely status updates, and get connected to an agent when needed.
The bot also handles claim tracking and notifications, keeping customers informed at every step. This not only speeds up processing but also builds trust in the system.
According to McKinsey, implementing generative AI insurance use cases increases productivity across the risk and compliance function by 10 to 30 per cent. It’s transforming traditional insurance operations, giving underwriters, claims managers, and distribution leaders powerful new tools to work smarter and faster.
Key functions:
- Automates First Notice of Loss (FNOL), allowing a client to fill a claim after an asset’s loss, theft, or injury
- Conducts damage appraisal assessing the extent and nature of the loss
- Processes claim to determine the reimbursement amount
- Confirms the customer’s insurance coverage and benefits
- Ensures claims settlement disbursing the insurance payout to the customer
Results:
- Faster claims settlement, significantly reducing processing time
- Improved fraud detection, boosting security and customer trust
- Increased cost efficiency, lowering operational expenses
- Fewer legal disputes, minimising litigation and associated costs
Sales chat and voice bots use customer data to personalise offers and sales strategies, ensuring each interaction is tailored to the individual’s needs. They profile customers, customise offerings, and align sales strategies to meet specific preferences.
For example, a leading insurance provider in Brazil uses a Tovie AI-powered WhatsApp bot to upsell policies and offer personalised renewals. When a customer’s policy is about to expire, the bot sends tailored messages with special offers, encouraging them to renew or upgrade their coverage.
WhatsApp was the platform of choice because of its widespread use in Brazil, making communication easy and accessible for customers. As a result, they’ve increased policy renewals and reduced the workload for human agents.
This approach helps boost revenue by retaining and expanding business with existing customers. While this is one of the most effective Gen AI use cases in insurance, it can also be applied in other financial services.
Key functions:
- Profiles customers to personalise interactions and product offerings
- Uses data-driven insights to align sales pitches with customer needs
- Gives responses and product suggestions, making interactions more relevant
- Operates both independently with propensity models and within the IDP platform
Results:
- Higher engagement through personalised service and greater customer satisfaction
- A more tailored user experience with customised offers and strengthening brand loyalty
- Improved customer retention with more meaningful interactions
- Enhanced brand perception and a stronger competitive edge in the market
Debt collection is often slow and costly for financial services and insurance companies. It also presents ethical challenges – there’s a delicate balance between recovering payments and showing empathy to customers facing economic hardship.
Today’s economic climate has left many people struggling with job losses or reduced income, making it harder to meet payment deadlines. While these customers often need extra support, they frequently avoid collection calls due to embarrassment or anxiety about their situation.
An AI-powered voice bot helps ease these interactions. Because it’s automated and non-judgmental, customers may feel less pressure and embarrassment when discussing repayment options.
AI-driven debt collection automates key tasks, improving efficiency while maintaining a human touch. These bots use behavioural insights, machine learning, and customer data to personalise interactions, answer questions in real-time, and negotiate payment plans. They can operate 24/7, ensuring consistent follow-ups while allowing human agents to focus on complex cases.
The technology also helps ensure legal compliance – a crucial aspect of debt collection. While human agents might struggle to consistently verify defaulters, document non-payment reasons, and confirm payment commitments at scale, AI handles these tasks efficiently.
A smart AI voice bot streamlines debt recovery by handling routine tasks like lead management, reminders, and payment negotiations. This allows human agents to prioritise cases that need a more personal approach. Among various generative AI use cases in finance, debt collection automation stands out for its ability to cut costs and save time while maintaining high service quality.
Key functions:
- Automation of routine tasks, reducing time and costs
- Consistent follow-ups for improved collection success
- Precise, personalised support, maintaining positive relationships
Results:
- Higher efficiency and better recovery rates
- Reduces reliance on human agents for routine interactions
- Higher satisfaction rate and fast and clear interactions with clients
Financial services employees often face a common challenge – answering the same questions repeatedly. This is where AI-powered FAQ chatbots can make a difference. These tools can pull information from multiple sources to handle routine enquiries about customer accounts, transactions, accounts, and billing.
Tovie Data Agent is an AI chatbot that works like an internal ChatGPT specifically trained on your company’s documentation. What sets it apart is its ability to follow regulations and internal policies while generating quick, accurate answers to employee questions and providing a wide range of generative AI use cases in banking.
This tool is particularly valuable in retail banking, where sales teams often struggle to access up-to-date product information quickly. Instead of searching multiple sources, employees can simply ask Data Agent to search through databases, internal portals, or preferred messengers. The result? Quick responses, document summaries, correct and consistent information retrieval, and fewer missed sales and customer service opportunities.
The chatbot serves as a central knowledge hub, making it easier for all departments to access the information they need when they need it. Whether it’s checking product details, verifying, regulation and compliance policies, or preparing responses for prospects, Data Agent helps streamline these daily tasks.
Key functions:
- Searches across company knowledge bases, product documentation, and internal wikis
- Provides detailed, factual answers beyond simple keyword search
- Extracts information with relevant context using Retrieval-Augmented Generation (RAG)
- Integrates with platforms like Slack, Teams, and WhatsApp for seamless communication
- Connects to CRMs to track customer preferences and purchase history
Results:
- Faster responses for improved customer satisfaction
- Easier information access and boosted confidence in recommending products
- Less time spent searching for information with more focus on sales
- Ensuring all salespeople have access to the latest, most accurate product details
A tutor bot can act as an interactive learning platform, enhancing the work of companies’ trainers and streamlining the training process. It guides employees through learning modules, reinforces knowledge with tests, and provides feedback through satisfaction surveys. Employees can turn to the bot whenever they need to refresh their knowledge, making the training more efficient and reducing the need for on-site specialists.
A Tovie AI client in Japan uses a tutor bot to improve the training process for sales staff at partner banks in mortgage insurance distribution.
Traditional training for partners’ sales teams requires on-site specialists, which can be time-consuming and resource-heavy. This approach is no longer sustainable for the current workforce.
Key functions:
- Streamlines training for sales trainers
- Guides employees through training modules
- Reinforces knowledge with interactive tests
- Redirects employees to the correct information when needed
- Collects feedback via satisfaction surveys
Results:
The tutor bot effectively trains employees by:
- Guiding them through training modules
- Reinforcing knowledge with interactive tests and providing the information when needed
- Collecting feedback through satisfaction surveys to improve future training
Want to learn more about how generative AI can be used in financial services? Check out our free AI use case library, where we’ve compiled the most valuable applications for banking and insurance.
AI-powered bots enhance wealth management by automating client interactions and providing personalised financial advice. They analyse market trends and customer data to offer tailored recommendations. By handling routine tasks, these bots free up advisors to focus on complex client needs while building trust with timely updates.
Traditional wealth management faces a common problem: advisors struggle to give personalised attention to all their clients while staying on top of market changes. This often results in delayed responses and frustrated clients.
That’s where AI steps in. AI bots can help financial advisors handle routine research tasks like fund analysis, market insights, identifying at-risk clients, and creating customised portfolios and proposals.
Key functions:
- Provides personalised investment advice
- Delivers real-time insights on market trends for informed decision-making
- Automates routine tasks, allowing advisors to focus on complex cases
Results:
- Higher client satisfaction
- Greater efficiency and advisor workload reduction
- Enhanced investment decisions
- Better scalability
Ensuring security, compliance and trust
Security and privacy of sensitive information are primary concerns for businesses wanting to implement generative AI. Implementing Gen AI use cases in finance should take place with an eye on legal and financial risks, risks of data misuse, inconsistencies, and non-compliance.
The ideal GenAI solutions should prioritise security, data privacy, and compliance while seamlessly integrating into existing workflows.
1. Data security and privacy
Deploying GenAI on-premises can ensure the proper safeguarding of sensitive data and maintain control over database access levels. Leveraging AI models on-premises in your private cloud allows you to harness their powerful capabilities while retaining complete control over how your data is handled, stored, and managed.
At Tovie AI, we offer the flexibility to deploy our solutions on our cloud or on-premise, supporting AWS, IBM FS, and dedicated server deployments of your choice. Our solutions are designed to safeguard sensitive information and comply with PII, GDPR, and internal policies.
Tovie AI solutions are compliant and validated by IBM Cloud for Financial Services. All our conversational solutions are now automatically compliant for FS organisations.
2. Tackling hallucinations
The quality of AI-generated answers heavily relies on the data they are trained on. Hallucinations in AI models can occur when input data is poorly processed, such as when documents are inadequately chunked or pre-processed or when the context is fragmented across different chunks. This can lead to confusion and incorrect answers from the AI model.
To prevent hallucinations, documents must be pre-processed correctly to ensure all necessary context is included. While uploading PDFs or CSVs directly would be ideal, additional pre-processing is often required, particularly for tabular or graph-based data. It may involve semi-manual or manual processing to achieve better results.
At Tovie AI, we offer services to assist clients with data pre-processing, enhancing the accuracy of AI-generated answers. Contact our team to learn more about how we can help you optimise your data for GenAI solutions.
Choosing your GenAI use cases in banking and insurance
This article is a starting point for exploring how generative AI can drive growth, improve productivity, and strengthen governance in financial services. It highlights the value AI can bring to your business and offers guidance on how to get started.
While GenAI can bring positive changes in financial services, it also comes with challenges, including fairness, privacy, and security. The best approach for banks and insurers is to adopt AI responsibly, integrating strong ethical practices and risk management strategies from the start.
If you’re looking to implement generative AI in your company but need guidance on the most impactful use cases and the right technology choices, Tovie can help. Our consulting services provide a fast track to AI adoption, helping you evaluate opportunities and deploy the best solutions with confidence.