How AI Agents in Finance Can Transform the Industry?

According to a report by McKinsey, AI technologies claim to have the potential to boost global banking profits by up to $1 trillion per year. This got the attention of the financial sector as well and made them adapt AI to automate processes and enhance customer experiences. Amongst multiple AI tools and techs, the one that is gaining maximum attention and delivering results in real-time is AI agents in Finance by Salesforce. One of its kind, Agentforce, has already started catering to the requirements of many sectors.

Do you want to know how it can turn the tables for the finance industry? Let’s explore how autonomous AI agents are transforming the financial industry by automating tasks, enhancing fraud detection, delivering personalized experiences, and more.

Tracing the Roots of Transformation of Financial Services

The AI transformation in financial services is not a new phenomenon it has been happening since the 1950s. The initial phase of the AI transformation included more emphasis on predictive AI, which later evolved, and here we are today in the “third wave of AI” the agent’s phase. Salesforce being the trusted pioneer, has always provided innovative solutions for the financial sector and also came up with a dedicated Financial Services Cloud. This is now further elevated with what is Salesforce Agentforce and AI agents.

Do you ask about the need for the transformation? Here’s an answer, there are large sums of data involved in the financial processes which are predominantly unstructured and siloed. Thus, it becomes tedious to manage that data, process it well, and deliver a quicker response time to the customers. Automating and revolutionizing financial services with the help of Agentforce can significantly impact the sector’s revenue streams and positively impact customer engagement models.

Let us learn a little more about the AI Agents for finance a bit more.

Understanding AI Agents in Financial Services

Financial AI Agents are advanced software applications that leverage artificial intelligence to automate and enhance various financial processes. They are specifically designed to meet the unique requirements of the financial industry, such as improving customer interactions or easing the customer onboarding process.

They also play a crucial role in ensuring that all the necessary rules and requirements are met without any delays.

The ability of financial AI agents by Agentforce to process vast amounts of data in real time enhances fraud detection and risk management, making financial transactions more secure and efficient. Overall, the integration of AI agents in finance leads to increased operational efficiency, better risk assessment, and a more personalized customer experience, all with the trust of Salesforce.

These intelligent agentic AI systems are specially designed to operate autonomously, this means they can make decisions and take actions with their reasoning capabilities and support of human supervision.

The vision behind the launch of Agentforce was to augment the AI agents’ to human teams, not replace them. By automating mundane tasks, AI agents enable human agents to concentrate on intricate, high-impact customer interactions. This approach could substantially enhance customer satisfaction and revenue growth for the financial sector.

How Do AI Agents for Finance Work?

Financial AI agents provide 24/7 customer support and automate transaction processing, reducing the need for human intervention and ultimately increasing efficiency. But how? Let’s find out.

The finance agents work based on a phased approach; here is a brief breakdown of the approach;

• Perception: In the financial services sector, the agent initiates by comprehensively understanding the unique needs of the financial institution. They then evaluate the importance of key areas that drive efficiency, productivity, cost reduction, revenue growth, and customer experience. Following this, they prioritize ensuring data quality and harmonizing the data. To enhance real-time integration, the agents proactively consider all available sources for gathering information, whether from the Data Cloud or BYO Lake.
• Decision Making and Execution: The next step involves using machine learning models to analyze the gathered data, identify patterns, and produce outputs to aid decision-making. This also involves implementing standardized AI use cases, such as policy and claim summarization, which are pre-built solutions. Therefore, the main focus of this step is automation and the existing capabilities of AI.
• Continuous Learning: Then, based on the upcoming outputs agents can learn and improve their responses making it easier and more contextual for the customers to understand. Plus, this also enables the agent to stay relevant and effective in a rapidly changing business landscape.

It is important to note that Agentforce is an AI-powered customer service platform built on Salesforce (native). It automates tasks like onboarding, insurance claims, wealth management, and IT modernization. Also, to protect data privacy, the Einstein Trust Layer prevents third-party AI models from accessing Salesforce information.

Applications of AI Agents for Finance For Different Providers

List of AI agent capabilities by Agentforce for Finance:

Banking

The AI agents in banking make it easier for customers to open accounts and get started. For instance, imagine a banking scenario where there are long chaotic queues for simple operations. Would one like to be a part of such a situation? Certainly not! Then some personalized agents assist the user at each step, understand their concerns, problems, and banking needs throughout, and suggest the most appropriate solution for them. Plus, strategically analyzes credit scores, financial data, and other factors to assess loan eligibility and risk.

Insurance

The agents simplify the process of filing and handling insurance claims. The agents augment the human teams by unifying data, providing the required information in seconds, and streamlining the entire process for them. Thus, with real-time updates and data accessibility, agents are significantly reducing turnaround time making customers feel more validated.

They also check for any fraudulent claims or discrepancies in data. Apart from this, agents can very well detect risks and proactively help human teams when it comes to claim adjustments or premiums by thoroughly analyzing the records.

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Enhanced Customer Experience in Insurance?: Impact of Agentforce on FNOL Processes

Improved FNOL Process time & Response time by

Claim adjuster’s productivity improved by

Customer satisfaction increased by

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Wealth Management

The matter of managing wealth revolves around much more accurate responses and recommendations to help clients manage their money more effectively; agents can speed up account transfers. They can also provide tailor-made recommendations to clients based on their risk-bearing capabilities and ultimate financial goals. AI agents can execute trades, manage portfolios, and optimize investment strategies, reducing the need for human intervention at every level of managing wealth.

IT

Agents can assist organizations in keeping their financial systems up-to-date and running smoothly. They can achieve this by improving their data quality and also by successfully modernizing their legacy software as and when required (i.e. by automating manual processes). Furthermore, agents are powered to quickly detect suspicious networking activities and inform the right teams at the right time.

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Benefits of AI Agents in the Financial Sector

These are some of the benefits of augmenting AI agents by Agentforce for Finance;

Improved Efficiency

As the AI agent automates routine tasks, this helps the human agents devote more time to focus on high-value tasks. Such tasks can be spending considerable time consulting clients or building detailed financial plans. This, in turn, improves the efficiency of the financial institution. For example, AI-powered chatbots can quickly answer common customer inquiries, while automated systems can streamline loan applications and underwriting processes.

Enhanced Customer Experience

The agent is designed to provide deeply customized support to the customers. The agents can provide support around the clock, offer tailored financial advice and product recommendations, and lastly proactively send alerts/ reminders regarding crucial dates and payment processes. This not only helps in reducing response times but also enhances customer satisfaction to a significant level.

Better Accuracy

The AI agent utilizes the power of advanced machine learning models (LLMs). This ensures the responses and suggestions provided by agents are precise and well-informed. Moreover, the agents can learn from their interactions with customers and improve their accuracy over time. And there’s no scope for any human errors as the tasks are automated by the agents.

Scalability

The AI agent can autonomously handle multiple customer queries. The agents can help financial institutions control costs and improve profitability, even during periods of rapid growth. Plus, as the business grows the agents also grow, they can learn to adapt and work through to meet the scaling requirements of the organization. This makes the Financial AI agent an ideal solution for large-scale financial institutions.

Uses of AI Agents in Finance

Finance AI agents can process vast amounts of data, learn from patterns, and automate tasks. They work as responsive agents first. For example, whenever the user initiates any conversations, an agent will respond accordingly based on their predetermined set of actions. In these different scenarios, let us understand how the agent will work.

Use Case 1: Streamlined Loan Processing

Whenever a user initiates a loan processing request, an agent will connect and carry out further proceedings.

Then the Atlas Reasoning Engine (the brains that power agents) reviews loan applications quickly by analyzing risk factors using past data and set criteria. Meanwhile, as per certain protocols, the agent would fetch the details from the user regarding the name, amount, account number, etc.

Upon getting the desired details the agent would guide the user with the next actions (the suggestions of prerequisite documents, etc.) they have to take to get the loan approval. Also by using customer data, AI can suggest personalized loan terms and interest rates, increasing the chances of approval. This helps make loan approval decisions faster. The platform also automates different steps of the loan processing process, from submitting applications to verifying documents and sending approval notifications.

However, there’s a catch: the agents will not automatically themselves sanction the loans without getting in touch with a human agent. The roles and responsibilities of the agents can be set and customized with guardrails to protect against any mishaps. Once the verification process is executed by an AI Agent, then a human agent will get engaged and take care of the rest of the process from there till the approval of the loan.

This further enhances the collaboration between human agents and AI agents.

Use Case 2: The Claim Adjustments Process

The agents transform how insurers handle the initial loss reports (FNOL) process that we’ve discussed above; one such report is about claim adjustments.

The agents begin the process of automatically gathering necessary information from policyholders during the query submission, including details about the incident, involved parties, and any supporting documentation.

So this is what happens at the backend. Suppose the user initiates a conversation with the agent “I’ve met with an accident and need to file a claim.” Agents will look for whether or not the necessary preconditions are met in this case, which are customers having an active insurance policy with this company and so on.

Then as programmed by the developers, agents would follow the instructions of getting the desired details from the customers to take the responses further. While the conversation with yeh customers proceeds in the front end, the Atlas engine verifies the information. It simultaneously creates a claim record making sure that everything goes as per the preset actions. And once everything gets verified the approvals become easier than ever for the customers.

These are just a few examples of how financial AI agents can be deployed to improve efficiency, reduce costs, and enhance customer experiences.

End Note

There’s no doubt that AI agents will be reshaping the financial industry and offering numerous benefits that can drive significant transformation from customer satisfaction through personalized recommendations to efficient problem-solving and data privacy concerns.

Thus, the future is here with more AI agents getting rolled out soon such as banking agents, claim agents, and advisor agents that will further revolutionize the industry. Don’t miss out on multiple opportunities to cater to customers with utmost precision and satisfaction.

And if you’re considering switching to financial AI agents powered by Agentforce to improve efficiency and reduce costs significantly, we can certainly help!

Our team of experts at Cyntexa with their years of experience in Salesforce AI consulting can help you create your own AI agents. For more information, you could schedule a meeting with our team today!