Over the past five years, banks have invested heavily in conversational AI systems, hoping they would transform customer service and help reduce operating costs.<\/p>\n
It seemed they could do everything: reset passwords, report balances, and much more. But most banks have stalled. This technology was supposed to revolutionize customer service, but ended up merely increasing efficiency. The AI model failed to address the core objective\u200a\u2014\u200ato change how finance\u00a0works.<\/p>\n
Companies like Merehead are already developing such an infrastructure, integrating autonomous agents directly into the core of trading systems and payment gateways. This allows financial institutions to not only provide information, but also automate complex operations\u200a\u2014\u200afrom liquidity management to cross-chain transaction execution\u200a\u2014\u200awithout human intervention.<\/p>\n
It\u2019s a strange thing: <\/strong>banks use sophisticated language models that understand complex queries, but these systems do almost nothing themselves. They\u2019ll explain what translation is, but they won\u2019t do it. They\u2019ll tell you about investment strategies, but they won\u2019t buy or sell stocks. It\u2019s not that AI is bad, it\u2019s that we can\u2019t figure out how to use it effectively.<\/p>\n To improve financial technologies, we need to make not just chattier chatbots, but intelligent systems that can think, plan, and perform complex financial tasks on their own, without the need for constant assistance. AI integration in business has already reached 77%,<\/a> and further even more so effective are used accessible models.<\/p>\n THE INDUSTRY IS NOW THROUGH A COOL TRANSITION: <\/strong>from ordinary conversational AI to powerful autonomous AI agents. These guys can handle complex financial tasks on their own. It\u2019s as if the entire logic of fintech infrastructure is changing!<\/p>\n Chatbots usually work simply: ask and get an answer. You ask a question, the system figures out what you mean, searches for information, and returns an answer. But it\u2019s a fairly simple thing; you can\u2019t really do anything with it, and it\u2019s also secure because it\u2019s not connected to other\u00a0systems.<\/p>\n Autonomous agents are changing things. They don\u2019t just answer questions; they perform complex processes that span multiple systems. They make decisions based on data and perform actions that can impact finances. For example, An OpenAI powered agent<\/a> can do more than just advise on portfolio changes. It scans the market, assesses risks, executes trades on various exchanges, and generates reports to ensure compliance, all while recording its\u00a0actions.<\/p>\n Autonomous financial agents are based on three key principles: the ability to think clearly, tight integration with various systems, and robust security. Unlike chatbots, which simply understand what the user wants, autonomous agents are capable of logical thinking. They break complex tasks down into simple steps, monitor progress, and adapt their plans as new information emerges.<\/p>\n 1. Perception Layer (context and\u00a0data)<\/strong><\/p>\n This layer collects all the information: exchange rates, balances, risks, rules. It simply prepares the data for the next\u00a0steps.<\/p>\n 2. Reasoning Layer (interpretation and planning)<\/strong><\/p>\n Here, LLMs analyze the situation and figure out what to do. But they don\u2019t implement anything; they only suggest\u00a0options.<\/p>\n 3. Policy & Risk Engine (restrictions and controls)<\/strong><\/p>\n Here, every agent\u2019s decision is checked for compliance with the rules: limits, laws, client settings. Everything is clear here, no amateurism.<\/p>\n 4. Execution Layer (execution of\u00a0actions)<\/strong><\/p>\n Executed through specialized APIs: trading systems, banks, payment services. The agent doesn\u2019t touch the money directly, but simply issues commands.<\/p>\n 5. Audit & Observability Layer<\/strong><\/p>\n Every action is logged: the input, the reasoning, the rules applied, the results. So, everything is transparent and meets requirements.<\/p>\n 6. Feedback Loop (training and adaptation)<\/strong><\/p>\n The results of the agent\u2019s work are used to improve strategies, but everything is under control, without changing the business logic at\u00a0will.<\/p>\n When AI starts managing finances, everyone will naturally be a little concerned about security. AI can spin a few lies, pretending to be telling the truth when in fact it\u2019s nonsense, and if it\u2019s using these tricks to make financial decisions, that\u2019s dangerous. Therefore, engineers need to come up with something like a sandbox for AI\u200a\u2014\u200aa place where it can operate, but with a bunch of restrictions. To reduce the risk of hallucinations and abnormal solutions, useful rely on NIST AI Risk Management Framework<\/a> (AI RMF 1.0) and build control across the entire life cycle of the\u00a0model.<\/p>\n Before the AI even begins to figure out what to do with it, the request must pass several security checks. Any attempts to trick the AI with tricky queries must be weeded out. Almost all key threats to agents with insecure output processing are well laid out in the OWASP Top 10 for LLM Applications<\/a> (Prompt Injection, etc.). Plus, we need to make sure that people don\u2019t abuse the system or overload\u00a0it.<\/p>\n Top-tier specialists are constantly trying to hack the system to find weaknesses before the bad guys do. This is absolutely necessary now, because the stakes aren\u2019t just reputations, but also a ton of\u00a0money.<\/p>\n Inside the sandbox there is a thing called policy Engine. It ensures that the AI doesn\u2019t violate company rules and laws. Every AI action is checked against a multitude of rules. There are transaction limits to prevent the AI from doing anything wrong, and if a transaction is large or risky, it must be approved by a\u00a0human.<\/p>\n Everything the AI does is recorded\u200a<\/strong>\u2014\u200aevery decision, every action. This is necessary to ensure compliance and to be able to investigate if something goes wrong. If the agent touches crypto payments or operations with virtual assets, restrictions and monitoring need to design with this in mind FATF guidance<\/a> on Virtual Assets and VASPs (AML\/CFT).<\/p>\n SaaS solutions out there promising to quickly add AI to your finances. These features are easy to implement, inexpensive to start, and constantly updated by specialists. If you need a simple chatbot or something unrelated to finance, SaaS is fine. But if you want AI to manage your finances, it\u2019s not the\u00a0answer.<\/p>\n The main problem <\/strong>is control. When you use SaaS, your important data is shared with other people, and that\u2019s where the headaches begin: how to protect that data, how to comply with regulations, and how to generally verify that everything is\u00a0secure.<\/p>\n Imagine an AI making a million-dollar deal on its own, based on some clever market analysis. Every action needs to be explainable, verifiable, and legal. But SaaS is often like a black box. Nothing is visible, nothing is understandable. This is unsuitable for financial companies.<\/p>\n Manual configuration helps manage every detail of the agent\u2019s operation. Companies can select and customize language models to suit their needs. They can also create rule systems that take into account their own risks and requirements. Plus, all of this easily integrates with internal systems using familiar protocols and security standards.<\/p>\n Investment in such development pays off with operational flexibility. <\/strong>If regulations change, new threats emerge, or business takes a different path, companies with manual configuration can change the agent architecture without being dependent on vendors. In today\u2019s world of constantly changing competition and laws, this is\u00a0crucial.<\/p>\n The transition from conversational AI to autonomous agents isn\u2019t something for the future; it\u2019s already happening, driven by advanced language models, improved API structures, and growing competition in the automation of complex financial processes. Companies that understand this and invest in a strong foundation will reap significant benefits: greater efficiency, reduced risk, and happier customers.<\/p>\n To ensure success, a serious approach is needed. Companies need to hire experienced engineers who can create and maintain complex AI systems. Rules must be established to prevent over-innovation and maintain\u00a0control.<\/p>\n It\u2019s important for everyone to understand that <\/strong>AI is not a magic wand, but a powerful tool that must be properly configured, tested, and constantly monitored.<\/p>\n In the next ten years, the financial institutions that will succeed will be those that master the art of autonomous operations management. They will use AI agents to perform routine tasks, and do so with exceptional precision. This will free up people to focus on strategic decisions and complex problems. They will create systems that learn and improve with every operation.<\/p>\n The question is no longer whether AI will transform the financial system. The question is who will lead this change and who will be left behind. The decisions you make now will determine how successful your company will be in the\u00a0future.<\/p>\n Why Autonomous AI Agents Are the Next Layer of Fintech Infrastructure<\/a> was originally published in Coinmonks<\/a> on Medium, where people are continuing the conversation by highlighting and responding to this story.<\/p>","protected":false},"excerpt":{"rendered":" Over the past five years, banks have invested heavily in conversational AI systems, hoping they would transform customer service and help reduce operating costs. It seemed they could do everything: reset passwords, report balances, and much more. But most banks have stalled. This technology was supposed to revolutionize customer service, but ended up merely increasing […]<\/p>\n","protected":false},"author":0,"featured_media":154740,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[],"class_list":["post-154739","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-interesting"],"_links":{"self":[{"href":"https:\/\/mycryptomania.com\/index.php?rest_route=\/wp\/v2\/posts\/154739"}],"collection":[{"href":"https:\/\/mycryptomania.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mycryptomania.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/mycryptomania.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=154739"}],"version-history":[{"count":0,"href":"https:\/\/mycryptomania.com\/index.php?rest_route=\/wp\/v2\/posts\/154739\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/mycryptomania.com\/index.php?rest_route=\/wp\/v2\/media\/154740"}],"wp:attachment":[{"href":"https:\/\/mycryptomania.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=154739"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mycryptomania.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=154739"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mycryptomania.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=154739"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}They Used to Just Answer, Now They Do Action: How Architecture Is\u00a0Changing<\/h3>\n
Architecture of autonomous agents<\/h3>\n
How an AI agent\u00a0works:<\/h3>\n
Safety first<\/h3>\n
Input validation and prompt\u00a0security<\/h3>\n
Policy Engine and Transaction Control<\/h3>\n
Here\u2019s why your own custom management system is better than\u00a0SaaS<\/h3>\n
Straight ahead<\/h3>\n