A customer can complete a financial application from their phone in minutes. However, the institution may still rely on emails, files, and manual data entry. The interface is digital, but the internal process is not always so.
This gap illustrates the scope of Fintech transformation. Its value lies in connecting applications, assessment, operations, and monitoring. Artificial intelligence can support this process by interpreting documents, identifying patterns, and explaining information, always with human validation and appropriate controls.
From a financial leadership perspective, this transformation should be assessed through three outcomes: ease of use, operational efficiency, and the quality of information for decision-making.
Technology creates value when it connects the customer experience with operations and decision-making information.
Interpret documents and identify missing information.
Identify anomalies and prioritize exceptions.
Analyze variances and explain validated data.
An ecosystem broader than a regulatory category
The Financial Stability Board defines Fintech as technology-enabled innovation in financial services that can produce significant changes in business models, applications, processes, or products.[1]
In Mexico, this concept must be distinguished from the legal categories established by the Law to Regulate Financial Technology Institutions. The law provides for crowdfunding institutions, electronic payment fund institutions, and a temporary authorization framework for innovative models.[2] This framework does not constitute a third type of financial technology institution (ITF).
The Fintech ecosystem also includes tools for customer identification, information analysis, financing administration, payments, transaction reconciliation, and reporting. Banks, SOFOMs, leasing companies, and other organizations can use them. Adopting technology does not in itself change an entity's legal status or replace its obligations.
Application: a more seamless customer experience
The service begins when a customer seeks to finance a purchase, manage funds, or make a payment.
The first stage must answer concrete questions: do customers understand which product they need, know the requirements, have an orderly way to submit information, and know the status of their application?
A digital form is only the beginning. The process improves when information can be reused, documents are reviewed against clear criteria, and customers receive understandable instructions.
For an SME financing application, an AI solution could extract data, detect inconsistencies, and flag missing documents. This would help assemble the application file. However, repayment capacity assessment and credit decisions must retain their defined criteria and accountable decision-makers.
The goal is to reduce unnecessary effort. Speed has value when accompanied by clear terms, well-founded decisions, and consistent communication.
Operations: connecting the processes behind the interface
After taking out a product, customers need to access funds, make payments, check balances, receive receipts, and resolve issues.
These events may involve treasury, credit, administration, accounting, and customer service. If each area maintains different records, discrepancies and resolution times increase.
Operations should function as a connected flow. An identified payment should update the corresponding record, feed into reconciliation, and become available to authorized teams. When an exception arises, the system should make it visible.
AI can identify unusual transactions or suggest matches between payments and obligations. These suggestions require validation, especially when references are ambiguous or payments are partial.
APIs help connect systems. The Basel Committee includes these interfaces, artificial intelligence, and cloud computing among the technologies transforming financial services.[3]
Before automating, it is useful to define responsibilities, rules, and exception handling. Otherwise, technology may accelerate a process that already contains errors.
Reporting: turning transactions into actionable information
A report should help customers understand their balances, transactions, and commitments. For the institution, it should provide insight into operations and support decisions.
Digital transformation should make it possible to trace data through its origin, update date, validations, and use in reporting.
A dashboard can clearly show originations, collections, or liquidity. Its usefulness depends on consistent, reconciled figures. It should also indicate when they were updated and their validation status.
An AI assistant could answer queries about authorized figures and draft explanations of variances. Each answer should show its source and period. Proposed causes must be distinguished from confirmed facts.
Financial leadership should monitor indicators such as response times, abandoned applications, cost per transaction, unreconciled items, incidents, and portfolio quality.
Boards, auditors, and funding providers need verifiable information, even if each requires a different view. Regulatory reporting must retain its criteria and deadlines.
Customer experience and controls must be designed together
A simple experience may require complex controls behind the screen. That complexity must be managed carefully.
Digitalization increases dependence on systems and providers. The Basel Committee identifies operational risks and risks associated with third parties.[3] Technology selection should therefore include security, continuity, and oversight.
It is necessary to define who can view or modify information, how changes are authorized, and what happens if a provider fails. There must also be mechanisms to correct errors and handle cases that require human intervention.
Artificial intelligence: analytical capability with accountability
Three tools should be distinguished:
- Automation executes defined rules.
- Predictive models estimate outcomes or identify patterns.
- Generative AI produces content, such as responses or draft analyses.
Each requires specific controls.
An assistant can write a convincing explanation and still be wrong. A model may lose accuracy when customer conditions change. Results must therefore be tested against representative data, potential biases reviewed, and the points at which a person must intervene defined.
The Financial Stability Board identifies risks related to models, data quality, governance, cybersecurity, and provider dependence in the financial use of AI.[4]
Adoption should begin with narrowly defined applications, clear permissions, and verifiable results. The institution retains responsibility for decisions and must measure whether the tool improves service and operations.
Transform with a financial rationale
The first step is to choose a measurable problem: incomplete application files, delayed reconciliations, repetitive queries, or reports that require manual work.
Next, establish a baseline and an expected outcome. This makes it possible to assess the investment through time savings, fewer errors, service capacity, and total cost. Integration, maintenance, training, and dependence on the provider must also be considered.
The institution must assess the effects on risk and liquidity. Handling more applications or originating business faster requires the capacity to analyze, fund, and administer those operations.
Fintech transformation can emerge from collaboration between institutions and specialist providers. Financial experience, customer knowledge, and technological capability can complement one another.
The goal is a clear application process, reliable operations, and reporting that supports informed decisions. When these stages function as one integrated process, technology becomes a core business capability.
Sources
Accessed on October 9, 2026. The examples and management criteria reflect the author's approach; they do not describe implementations at specific companies.
[1] Financial Stability Board, Financial Innovation.
https://www.fsb.org/work-of-the-fsb/financial-innovation-and-structural-change/financial-innovation/
[2] Mexico's Chamber of Deputies, Law to Regulate Financial Technology Institutions, Articles 4, 15, 22, and 80 onwards.
https://www.diputados.gob.mx/LeyesBiblio/pdf/LRITF.pdf
[3] Bank for International Settlements / Basel Committee, Digitalisation of finance, press release of May 16, 2024.
https://www.bis.org/media-releases/20240516-basel-committee-publishes-report-digitalisation-finance
[4] Financial Stability Board, The Financial Stability Implications of Artificial Intelligence, November 14, 2024.
https://www.fsb.org/2024/11/the-financial-stability-implications-of-artificial-intelligence/