Introduction
AI is transforming banking information, customer service, and business operations. At the fore are Generative AI and Agentic AI which play key roles in improving performance and quality of financial services. Generative AI produces or condenses content from what it is given in terms of info, with agentic AI which instead is able to put together a series of actions toward a defined goal within set parameters. As banks research in to what agentic AI has to offer we are seeing it become a key area of study. Also it is to note that both methods are very good at what they do which is to reduce repeat tasks, make information easily accessible and respond better to customer requests. In the world of finance we see that they have issues with handling private data, meeting regulatory requirements, security issues and the need for human input which all play a role in the adoption of these in the banking space.
Basics Generative AI in Banking
Generative AI is what produces new content like text, summaries, explanations and reports which it forms from patterns it has learned in from data and from instructions given by a person. In the banking sector we see these systems’ role in that of assistant to staff which is to make sense of large financial reports, to create customer communication, to sum up changes to policies, and to access relevant information from internal databases. For example an AI which is ok to use may put out a summary of support documentation to an employee as they are putting in a loan application which before that may have gone into a long winded detailed assessment. Also we see Generative AI used by customer support to come up with answers to much asked questions on issues like transfers, login info, and banking processes. This may save staff time in research for information or in the creation of routine docs, but what is put out by the AI may be used in large scale financial transactions so it has to be checked for accuracy, fullness and compliance.
Generative AI in Real-World Applications
1. Support Team, Internal Knowledge.
There is a great deal of customer inquiry in banks and we see that prompt and consistent customer support is very important for operations. We note that service agents can use generative AI to put together the best answers which also include summaries of transactional info, and to craft clear answers out of data from approved resources. An employee may have the option to ask an AI assistant to go over what the procedure is to finish off a transaction, instead of sorting through a few pieces of paper at a time. We can present to the agent a first draft from the system to then be polished and sent out to the customer. AI is useful for internal processes, products info, and compliance information, all which is put forth as a natural language question. Also these systems can be tuned up by connecting them to very accurate and current info, in turn this will increase productivity and consistency. It is so that banks must put in place measures to prevent assistants from sharing out private information and to also see that when assistants do reply to something they have that which supports the response.
2. Accounting Finance Document Analysis
Financial companies work with contracts, audits reports, account statements, risk reports and regulatory documents which require review. We see AI in this space to do summary of these docs, extract key messages out of them and to clarify technical jargon. Also AI can put together first drafts of reports. For example a finance team may use a given tool to present monthly operational insights in a condensed report for management review. Also we see AI’s role in helping compliance officers to zero in on what parts of the new regulations require more attention. These tools do save on prep time and get the professionals back to analysis which they are better at as opposed to report writing. But also it is noted that AI does have issues with financial terminology, may miss important details and at times reports out with wrong info. Before going to print a report writers should check the numbers against primary sources and also see that each major point is supported by data and we aren’t leaving anything out.
Agentic AI
Agentic AI refers to systems which plan actions out, use a set of tools or resources assess results and carry out a series of actions with varying levels of independence and autonomy in pursuit of a particular goal. An agentic system may interface with the bank’s apps to push a workflow along; whereas a simple generative AI will only put out an answer or recommendation. For example it may see that a customer application is incomplete, determine what docs are missing, alert staff for information that is allowed, tag the status of a workflow, and notify staff when it needs to go in for a review. Also this is important because different risks are present in taking action as opposed to producing text. Agentic systems must have well defined parameters and limits on what they do, keep reliable records of their actions, and have in place what to do in the case of atypical situations. Also their degree of independence should be a fit for the value and import of the task at hand.
Application of Agentic AI in Banking Operations
1. To Resolve the Transaction Issues.
Transaction reconciliation is a process which we put our financial data through to make sure that all transactions are recorded. As issues come up we may have to look at payment references, dates, settlement information in accounts etc. We may have an agentic AI which coordinates these checks which also goes out to approved systems to get info, does the record comparison, classifies what is a discrepancy and which one requires investigation. Also we may put some exceptions into regular processing if we have set out certain rules, but for large or out of the ordinary issues we refer to authorized personnel. The same processes we use for payment investigations we also use for monitoring operations. Agentic AI may put in place which will do many of these tasks thus speeding up issue resolution by way of running through many steps at once. Also we do large financial adjustments only after we have proper bank authorization and we keep very detailed information on the transaction for audit which will in turn allow us to trace, review and correct as needed.
2. Support in Customer Onboarding and Compliance Reviews
Identity check, document gathering, screening and due diligence which is put in place to prevent financial crime and fraud is a usual part of the customer on boarding process. Agentic AI may run all of that, it will monitor application progress, determine what is missing in terms of docs, see that all required steps are taken care of and pass apps to the proper team. In performing compliance reviews agent may collect relevant docs, put together and present supportive information and bring to notice inconsistencies to an investigator. These features are put in to play to reduce delays and to simplify complex processes. Also it’s not true that any program should go ahead and approve anyone that applies to it or that which flags everything. Sensitive issues like identity issues, possible sanctions hits, suspicious transactions etc. may require expert input. Banks must see to it that auto work flows are in line with the relevant regulation, are based on authenticated data and that there is transparent report for escalations and actions taken.
Key Differences between Generative and Agentic AI
Generative AI and Agentic AI perform different roles, but at the same time do work well together. What is key is that Generative AI is used for creating content and information it’s a what whereas Agentic AI is into the what and the how ,it plans and uses tools to achieve. A generative assistant may take in a customer’s loan docs and put together a set of loan related questions for an analyst. If the agentic system is made available it may coordinate doc verification, see if the application is complete, update workflow records, and pass the case to the approval process. Also this is not black and white as some GPTs will be able to call out to external tools and agentic systems will in fact be built on top of generative models for reasoning and communication. The more telling difference is that some systems just produce output and others also do actions. Banks must be aware of the application’s real capabilities and access rights which may differ from its labeled function.
Operational Efficiency and Business Benefits
Either of these options is put to use the increase of banking productivity is what you get, or what you see from it will depend on how well it is put into practice. With Generative AI you may save time in the search for info, report writing, and creation of customer communications. Agentic AI does a great job at what it is to do repeatable tasks across many systems and in doing away with unneeded hand off, it empowers staff to do in depth analysis and do so more consistently. These techs may work well together to improve response times, in making operations more efficient and also to get qualified professionals back to complex issues. Banks may look at what they put out in terms of results via metrics like processing time, error rates, customer satisfaction, issue escalation rates and case resolution costs. Also it is not a given that automation will save you money. There are large scale issues with integration, staff training, model monitoring and compliance which may in fact raise the cost. We recommend starting out with very defined and simple use cases which have lower risk, and to measure performance against present processes before you scale up.
Cybersecurity, Privacy, and Human Supervision
The issue of security in the implementation of AI in banking is a key issue which must be dealt with for banks to handle sensitive information and perform certain actions. Poor implementation of access controls with Generative AI may cause2 the release of private information out in the open and if Agentic AI is plugged into other systems which a hacker can play with that is a recipe for trouble. Also when a system has too much permission it may put out private info, change information around, or carry out unauthorized doings. That is why we see least privilege access, encryption, identity management, activity logging and continuous monitoring as policies which banks should put in place. Also they should look out for unapproved tools, leaks, quick responses to prompts and any attempt to get round the security features. For very important decisions like looking into strange transactions, setting account terms, issuing loans, and large scale fund transfer human supervision is still very much required. Also having clear out of band notification and emergency shutdown options in place for when the system does something out of the blue can help staff intercede in a crisis.
Testing and Responsible Implementation
Institutions to put in place structured tests as a step in to deploy AI in sensitive banking settings, to run through attacks and historical cases where relevant and realistic scenarios. In terms of generative AI systems’ performance evaluation see that they are accurate, do not make claims which are not supported by the data, protect privacy and are of high quality when information is sparse. More study is required to see if Agentic AI honors permissions, does well in managing exceptions, prevents duplicate actions and stops when permissions are required. Also banks to look at if the product is biased, how reliable it is, performance in atypical situations, and that it meets legal requirements and regulations. Pilot rollouts to determine what works and what doesn’t before wider scale use among customers or staff. Once go live institutions to review, assess and report out on results also to look at model changes and reevaluate risks. Staffs need to be made aware of what the AI can and cannot do rather than replaces good judgment.
AI’s Role in Today’s Banking Industry
As banks move into the digital age, we see Generative and agentic AI to play a larger role. Generative AI which may be used to present financial information in a more digestible and accessible way for employees to perform knowledge intensive tasks, and agentic AI which may be used to coordinate go ahead of approved actions between connected workflows. The combination of the two may help to speed up the on boarding process, improve the structure of compliance reviews, manage reconciliation and also provide more prompt service. But it will take in hand data which is to be trusted, secure tech, very clear accountability, effective testing and proportionate human supervision for adoption to truly benefit the community. Technical independence should not be the reason for banks to go it alone. Instead they should determine what tasks may be supported by these technologies, which actions if automated are safe and which decisions must still be made by people. If used responsibility these AI tools may improve financial services’ operational efficiency without at the same time diminishing customer trust and protecting their interests.
Conclusion
In the field of today’s banking we see that generative and agentic AI is complementary solutions which do improve the experience for banks and their customers. Banks may use generative AI for information production and interpretation, and agentic AI for the automation and execution of multi-step processes, or to carry out what is bank authorized. This may in turn reduce repeat tasks, improve service delivery and we see greater efficiency in operation. But in this space financial institutions must address issues of cyber security, privacy, inaccurate results, unauthorized action and regulation compliance. With these techs banks may put in place responsible use through much defined permissions, in depth testing, continuous tech watch and meaningful human input. The role of AI in banking won’t be determined by the number of tasks it takes on but by its performance in safe, secure and transparent delivery to bank staff and customers.
Get more well researched information about Generative AI and Agentic AI in Modern Banking here.
