The global financial technology industry is entering a new era, one shaped not just by automation, but by autonomy. Across banks, payment companies, lenders, and investment platforms, a new class of artificial intelligence known as agentic AI is beginning to change how financial services are designed, delivered, and managed. Unlike earlier AI tools that mainly responded to prompts or followed fixed instructions, agentic AI can plan, decide, and act with far less human intervention. That shift is drawing attention from both innovators and regulators.
In simple terms, agentic AI behaves less like a calculator and more like a digital worker. It can observe a situation, analyse available data, choose a course of action, and execute tasks across systems. In fintech, that means an AI agent could verify documents during onboarding, flag suspicious transactions, answer customer queries, or help manage risk in real time. For an industry built on speed, precision, and trust, the potential is enormous.
One of the most immediate uses of agentic AI is in fraud detection and compliance. Financial institutions process millions of transactions every day, and criminals constantly look for new ways to exploit weaknesses. Traditional rule-based systems can miss emerging patterns because they rely on predefined logic. Agentic AI, by contrast, can adapt more dynamically, reviewing behaviour, detecting anomalies, and responding faster to suspicious activity. In a sector where minutes matter, this kind of responsiveness can reduce losses and improve customer confidence.
Customer service is another area being transformed. Many fintech companies already use chatbots, but agentic AI goes much further. Instead of simply answering basic questions, an AI agent can carry out a sequence of actions on behalf of a customer, such as checking account details, updating preferences, and recommending financial products based on spending patterns or savings goals. This creates a more personalised experience and reduces the burden on human support teams.
Lending and credit decisioning are also likely to change. Today, loan processing can be slow and fragmented, involving document collection, verification, risk scoring, and compliance review. Agentic AI can connect these steps into a smoother workflow. It can gather required information, assess creditworthiness, identify missing records, and move a case forward more quickly. For borrowers, that could mean faster approvals. For lenders, it could mean lower operating costs and better consistency.
There is also the question of jobs. Agentic AI will not eliminate the need for financial professionals, but it will change many roles. Repetitive, manual tasks are likely to decline, while jobs focused on supervision, strategy, customer relationships, and exception handling may grow in importance. In this sense, the technology is less about replacing people and more about changing how they work. Institutions that prepare their workforce early may be better positioned to benefit from the transition.
For fintech leaders, the message is clear: agentic AI is no longer a distant concept. It is becoming a practical tool with real business value. The companies that succeed will be those that adopt it responsibly, with strong governance, clear controls, and a human-centred approach. In finance, innovation has always moved the fastest when it solves real problems. Agentic AI appears ready to do exactly that.
If the first wave of fintech digitised payments and the second wave personalised finance, the next wave may well be defined by intelligent systems that act on behalf of users and institutions alike. The rise of agentic AI is not just another technology trend. It may be the beginning of a new operating model for finance itself.
Bangladesh stands at an important crossroads as artificial intelligence begins to reshape the global economy. Among the newest developments is agentic AI, a form of artificial intelligence that can plan, decide, and act with limited human input. For a country eager to modernise banking, public services, and business operations, the promise is significant. But the path to adoption is filled with practical and structural challenges.
Agentic AI could help Bangladesh improve customer service, speed up financial processes, and make public systems more efficient. In theory, it offers a powerful way to close service gaps and support faster growth. In practice, however, the country must first overcome deep barriers in infrastructure, skills, regulation, and trust.
One of the biggest obstacles is the weakness of the digital infrastructure. Agentic AI depends on clean, connected, and well-organised data. It also requires strong computing resources and reliable systems. In Bangladesh, many institutions still operate with fragmented databases and uneven technology capacity. Without better infrastructure, AI systems may struggle to function effectively or produce reliable results.
The shortage of skilled professionals is another major concern. Building and managing agentic AI systems requires expertise in data science, machine learning, cybersecurity, ethics, and governance. Bangladesh’s talent pool in these areas is still limited. Universities and training institutions are working to catch up, but the pace may not be fast enough to meet growing demand. If the country wants to compete in the AI era, it must invest heavily in education and workforce development.
Regulation presents a further challenge. Agentic AI is different from older digital tools because it can make decisions more independently. That creates questions about accountability, oversight, and liability. If an AI system makes a harmful decision in lending, fraud detection, or public service delivery, who is responsible? Bangladesh is still developing its policy framework for AI, and experts have warned that implementation will be just as important as policy design. Without clear rules, institutions may hesitate to adopt such powerful systems.
Language and local context also matter. Many global AI models are built mainly for English-speaking markets and may not perform well in Bangla or reflect the realities of Bangladeshi users. This can create problems in areas such as customer support, financial inclusion, and public communication. For AI to be truly useful in Bangladesh, it must understand the local language, behaviour, and social conditions.
Despite these challenges, Bangladesh should not view agentic AI as a distant dream. The technology is advancing quickly, and countries that prepare early will have a clear advantage. For Bangladesh, the real issue is not whether agentic AI will arrive, but whether the country can build the foundation needed to use it wisely.
That means investing in infrastructure, training people, improving governance, and creating AI systems that fit local needs. If Bangladesh can do that, agentic AI could become more than a trend. It could become a tool for national progress.
Hasan Zahidul Amin is a fintech specialist. He can be reached at [email protected].
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Views expressed in this article are of the author’s own and may not reflect the editorial stance of The Daily Star.
