How AI Is Changing the Way Financial Technology Works

By Debasis Panda

Artificial intelligence is changing the way financial technology companies and financial teams approach everyday work. What once involved manually reviewing large amounts of information, reconciling records or monitoring transactions can increasingly be supported by systemsgg that identify patterns, summarize information and help people make decisions.

For businesses and professionals working with financial technology, the change is not simply about adopting another software tool. AI is beginning to influence how financial systems are connected, how information moves through an organization and how people interact with technology.

That makes the discussion relevant not only to large banks. Financial institutions, technology companies, accounting teams and other organizations that manage financial information are all considering how AI can fit into their existing operations.

From AI Assistance to AI Agents

Much of the early discussion around generative AI focused on tools that answer questions, summarize documents or create written content. Those applications remain useful, but another development is attracting attention: AI agents.

An AI agent can be designed to work through a series of tasks rather than simply provide a response. Depending on how it is configured and controlled, an agent could gather information from several systems, analyze it, recommend an action and send the task to an approved workflow.

Consider a treasury team monitoring a company's cash position. Traditionally, employees might collect information from banking systems, accounting software, spreadsheets and other sources before determining whether action is needed.

An AI-enabled workflow could help bring those sources together, identify an unusual change in cash requirements and prepare a recommendation for review.

The important distinction is that the technology does not necessarily replace the person responsible for the decision. Instead, it can reduce the amount of manual work required to reach that decision.

Deloitte's 2026 research on institutional banking identifies treasury, payments and securities services as areas where AI-native products could increasingly become part of financial institutions' core offerings.

Fraud Detection Is Becoming More Adaptive

Fraud is another area where AI is changing financial technology.

Financial institutions have traditionally relied on rules and statistical models to identify suspicious activity. Those systems remain important, but modern fraud attempts can involve rapidly changing patterns, manipulated documents, synthetic identities and increasingly convincing social-engineering techniques.

AI can help financial organizations examine large numbers of signals and identify unusual behavior more quickly.

For example, a transaction-monitoring system may consider factors such as transaction history, account behavior, device information and other available signals when determining whether an activity requires additional review.

At the same time, AI introduces another challenge. The same technology can potentially be used by criminals to create more convincing phishing messages, deepfakes and other fraudulent material.

The Bank for International Settlements has identified cyber risk, model risk, data quality and the use of AI in fraud among the issues financial institutions and authorities need to consider as AI adoption expands.

The result is an environment in which financial institutions need to improve both their AI capabilities and their broader security practices.

Finance Teams Are Looking Beyond Automation

AI is also changing how finance departments think about routine work.

Reconciliation, forecasting, variance analysis, expense review and working-capital management have historically required substantial amounts of human effort. Automation has already changed many of these processes, but AI can add another layer by helping employees interpret information and identify exceptions.

For example, an AI-supported finance system could identify an unexpected movement in expenses and bring relevant information together for an employee to investigate.

It could also assist with cash-flow forecasting by analyzing historical information and current business conditions.

Deloitte's 2026 finance research found that AI adoption among finance organizations is widespread, although the level of measurable value and integration of AI agents remains uneven.

That distinction is important.

Installing an AI tool is relatively straightforward compared with redesigning a process around it. Organizations still need reliable information, appropriate controls, clear responsibilities and employees who understand how to use the technology.

Why Data and Enterprise Architecture Matter

AI systems depend heavily on the quality of the information they receive.

A financial organization may have years of valuable data, but that data can be distributed across accounting platforms, banking systems, customer databases, spreadsheets and cloud applications.

If information is inconsistent, duplicated or difficult to access, an AI system may struggle to produce reliable results.

This is where enterprise architecture becomes relevant. Connecting applications, data platforms, APIs, security controls and cloud services can provide the foundation required for AI-enabled processes.

The objective is not simply to introduce another AI application. It is to create an environment in which information can move securely between the systems that need it.

The Bank for International Settlements highlighted data quality, privacy, security and third-party dependencies as significant considerations for financial institutions using advanced AI systems.

For organizations considering AI adoption, that means data governance and technology architecture should be part of the conversation from the beginning.

Cloud Technology Is Part of the Picture

Many modern financial systems already operate across a combination of cloud platforms, enterprise software, APIs and specialized applications.

AI adds another requirement because some AI workloads can require substantial computing resources and access to large volumes of information.

A modern financial technology environment may therefore include an ERP platform, payment systems, cloud infrastructure, data platforms, cybersecurity tools and AI services working together.

The challenge is to connect these components without creating unnecessary complexity.

This is especially important for smaller organizations. AI adoption does not necessarily require replacing every existing system. In some cases, organizations may be able to introduce AI into a specific workflow while maintaining their existing core applications.

Starting with a clearly defined business problem can make that process easier to manage.

Responsible AI Requires Human Oversight

Financial technology operates in an environment where mistakes can have real consequences.

An incorrect fraud alert can inconvenience a customer. An inaccurate financial forecast can affect planning. An inappropriate automated action can create operational or financial risk.

For that reason, AI systems used in financial services need appropriate controls.

Organizations may need to establish rules around which decisions can be automated, which require human approval and which should never be delegated entirely to an AI system.

Audit logs, access controls, data protection, model monitoring and clear accountability can also become important parts of an AI implementation.

The BIS has emphasized the importance of explainability, data governance, privacy, model risk management and human accountability as AI becomes more deeply integrated into financial activity.

Deloitte's 2026 finance research also shows that finance leaders are increasingly involved in decisions about AI investment and governance, reflecting the growing connection between technology and financial management.

What This Means for Finance Professionals

The growth of AI does not necessarily mean that finance professionals will become less important.

Instead, some responsibilities are likely to change.

Employees may spend less time collecting information and more time interpreting it. Technology teams may spend more time designing controls around automated workflows. Finance leaders may become more involved in decisions about data, technology investment and AI governance.

This creates demand for professionals who can understand both financial processes and technology.

Someone working in finance does not necessarily need to become an AI engineer. Likewise, a technology professional does not need to become an accountant.

But understanding how the two disciplines interact can become increasingly valuable.

A Practical Approach to AI Adoption

For organizations considering AI, the most useful starting point may not be asking, "Where can we use AI?"

A better starting point can be identifying a specific business problem.

A finance team might begin with reconciliation. Another organization might focus on forecasting, customer service, fraud detection or document processing.

From there, the organization can evaluate the quality of its data, determine which systems need to communicate, establish appropriate controls and define how success will be measured.

This approach can also make it easier for employees to understand where AI fits into their existing responsibilities.

Looking Ahead

AI is likely to become increasingly embedded in financial technology rather than remaining a separate tool that employees occasionally use.

Payments may become more context-aware. Fraud monitoring may become more adaptive. Finance teams may receive more continuous analysis of financial information. Treasury professionals may use AI-supported forecasts and recommendations. Routine reconciliation and document-processing tasks may require less manual effort.

But technological capability alone will not determine how successful these changes are.

The organizations that benefit from AI will also need reliable data, thoughtful system design, appropriate security measures and clear accountability.

For local businesses, financial professionals and technology teams, the broader lesson is straightforward: AI is becoming part of the infrastructure of modern business, not simply another productivity application.

The next stage of financial technology will therefore be shaped not only by what AI can do, but by how responsibly organizations connect it to the systems, people and processes already running their businesses.

About the Author

Debasis Panda is an enterprise architecture and digital transformation professional with more than 19 years of experience across financial technology, finance and treasury, SAP ERP, cloud architecture and enterprise technology transformation. His work has focused on connecting business requirements with scalable technology architectures and helping organizations navigate complex technology transformation programs.