Complaints, queries, and claims are among the richest sources of market intelligence available to financial authorities – each tells a story about a consumer’s experience. They also provide a direct window into emerging risks, product failures, service disruptions, fraud patterns, and conduct issues.
Yet, most supervisory authorities only see these stories after they have been aggregated into monthly or quarterly reports, often relying on delayed, aggregated reporting that limits the ability to detect and respond to these signals early.
Ensuring fair treatment for financial consumers
Access to more timely and granular data would transform market conduct supervision from a reactive process focused on historical reporting into a proactive discipline centered on continuous market intelligence and early risk detection, ensuring consumers are treated fairly and with integrity.
More frequent data would allow supervisors to detect emerging consumer risks as they arise, while granular data would provide more comprehensive information about customer’s journeys and conduct risks, rather than information reduced to aggregated statistics reported on a periodic basis.
Concretely, supervisors could rapidly identify and alert institutions to systemic service disruptions, impersonation scams, or phishing campaigns affecting their peers. Financial service providers, in turn, could use that intelligence to protect their own customers, building trust and improving the overall service experience.
In Egypt, Ecuador, and Peru, for example, the World Bank Group is working with financial authorities to design and implement solutions for the collection of timely data from complaints, queries, and claims. The goal is to equip authorities with the granular and more frequent data they need to strengthen their supervisory capacities.
This is a critical step: the absence of such data, compounded by high levels of manual processing on both the regulatory and institutional sides, is one of the most significant barriers to effective market conduct supervision.
How AI is making a difference
Proactive, data-driven supervision requires high-quality information flowing continuously and at scale. Recent advances in artificial intelligence (AI) are making this transition increasingly feasible. Large language models (LLMs) can clean, classify, validate, and enrich complaints data, while preserving the context often lost in traditional reporting approaches.
Beyond data processing, AI agents can support supervisory workflows by continuously monitoring data quality, identifying anomalies, correlating signals across multiple datasets and escalating emerging risks for human review.
Rather than replacing supervisory judgement, these capabilities augment their ability to detect issues earlier, focus attention on where it is most needed and act before consumer harm becomes systemic. They also support in the process of guaranteeing better outcomes: they autonomously monitor data quality, provide timely feedback to financial institutions, integrate multiple datasets, and flag market-wide events as they emerge, without waiting for a reporting cycle.
This creates genuine feedback loops between supervisors and financial institutions: market-wide events can be flagged promptly, and poor conduct can be addressed the moment it surfaces. Sharing this market intelligence with financial institutions enables them to identify and mitigate conduct risks, respond to issues affecting simultaneously products across the market.
Of course, to mitigate AI risks such as hallucinations and misinterpretations, it’s important to work in parallel on a sound governance framework and ensure human decision-making is embedded at the appropriate points while harnessing the full capabilities of AI to automate and reimagine workflows. These use cases are also catalyzing broader modernization of financial sector regulatory and supervisory frameworks.
Innovative technology for the financial industry
The implementation of Supervisory Technology (SupTech) solutions is prompting authorities and institutions alike to rethink how data is collected, how dispute resolution mechanisms function, and how public-private collaboration can be structured.
The work in Egypt, Ecuador, and Peru has already led to improvements in regulatory taxonomies and dispute resolution processes. Proportionality, risk-based supervision, adaptability, and public-private collaboration are not just principles guiding this work, they are embedded in the design of every solution.
As financial systems become increasingly digital and interconnected, supervisory approaches must evolve as well. With around one-fifth of all regulators worldwide already using or exploring the use of AI for handling complaints (CCAF 2026), the future of market conduct supervision may not be defined by how many complaints are resolved, but by how quickly authorities can detect emerging consumer harm and intervene before it affects consumers at scale.
The work underway in Egypt, Ecuador, and Peru offers an early glimpse of what this future may look like: one in which complaint data is no longer treated as an administrative by-product of dispute resolution, but as a structured source of supervisory intelligence capable of informing risk assessments, prioritizing supervisory action, and ultimately contributing to financial customers well-being.
Related Reading:
- The Next Wave of Suptech: Innovation Suptech Solutions for Market Conduct Supervision (Technical Note, 2021)
- From Spreadsheets to Suptech Technology Solutions for Market Conduct Supervision (Discussion Note, 2018)
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