Interview: Using Data and SupTech in Supervision of Digital Financial Services

This interview strongly points out one fact that data is a foundation for effective supervision. The interviewee's most memorable insight is that SupTech is like having an expensive car without roads if the underlying data quality is poor. His further highlights that the real barriers to adoption are not technological but institutional ie poor planning & data governance, skills gap among staff and high cost which are challenges Bank of Uganda faces today.

The interview highlighted the global shift by financial authorities, especially central banks, toward using granular data to improve efficiency and enable proactive, risk-based supervision. While granular data offers significant supervisory benefits, its adoption is often costly.
The discussion also emphasized the role of SupTech in supporting digital financial services inclusion, alongside challenges to its adoption, including limited data availability and capacity constraints such as insufficient technical expertise among some supervisors.

The interview clearly highlights how data and suptech are transforming DFS supervison. Leveraging realitime reporting and analytics allows supervisors to move from reactive to proactives oversight, identifying risks earlier and more accurately.

Really insightful interview. I like how it breaks down the importance of good, granular data in a way that feels practical, especially for emerging markets where strong internal controls aren’t always in place. The points on SupTech were also spot on, it’s clear these tools can genuinely boost supervisory capacity, even though the implementation challenges are very real.

The interview shows that data is essential for supervisors to carry out their duties effectively. SupTech depends on high-quality data because its operation functions like a luxury car that requires road access. The actual obstacles that prevent widespread adoption stem from institutional problems, which include inadequate planning, together with data governance issues, skills shortages, and excessive expenses. The combination of SupTech and data enables DFS supervision to enhance proactive monitoring systems, which allow for earlier risk detection and improved monitoring capabilities.

This interview clearly brought out the move towards granularity of data to improve our supervisory capabilities and identification of risks. In DFS supervision, this is critical as emerging risks need to be understood from existing data, analysis of different data types to improve the existing risk based approach as we incorporate suptech tools

The interview underscores that high quality data is the foundation of effective supervision, stressing through the analogy of SupTech being like an expensive car without roads that a poor data undermines its value, while highlighting that real obstacles to adoption are institutional, such as weak planning and data governance, skill gaps and high costs, as seen in the Bank of Uganda, even as global financial authorities increasingly shift toward granular data to enhance proactive, risk-based supervision, support digital financial services inclusion and leverage real-time reporting and analytics to move from reactive to forward-looking oversight despite persistent challenges of limited data availability and constrained supervisory capacity.

Excellent points. Matei correctly highlighted that SupTech implementation should be gradual and experimental. Regulatory sandboxes or pilot projects can help authorities test technologies in controlled environments before full scale deployment. This approach allows regulators to refine governance frameworks, validate analytical models, and build internal confidence in data driven supervision.

In Pakistan’s case, the success of SupTech will depend less on acquiring technology and more on strengthening institutional capacity, improving data governance, and transforming supervisory culture toward evidence based and technology enabled oversight.

In emerging economies, financial systems may be fragmented, underbanked, or heavily reliant on mobile and digital platforms, which increases the risk of fraud, exclusion, or systemic shocks if supervision is weak. High-quality data enables regulators to monitor usage patterns, assess credit risk, track financial inclusion, and ensure fair treatment of vulnerable groups. Without reliable data, supervisors may make decisions based on incomplete or misleading information, which can undermine financial stability and hinder inclusive growth.

High-quality data is essential for effective supervision, poor data leads to poor decisions and crises. In emerging markets, it’s even more critical because internal controls, risk management, and audits at financial institutions are still developing.
High-quality data means, right frequency i.e. high for fast risks like liquidity/cyber, lower for slower ones like credit, sufficient granularity for proactive risk detection.
Reliable accuracy reflecting true risk profiles: Global trend: Shift to granular data since the GFC for better risk-based supervision, prioritized in emerging markets despite challenges. Granular data advantages, Proactive risk identification, prevents crises, enables effective supervision.
Challenges: Data availability/digitization costs, legacy tech, processing/analysis capacity. Sup Tech benefits for inclusive DFS in emerging economies, Boosts supervisor productivity amid sector growth, enables fast analysis of massive datasets, trend/bubble detection, proactive work across all supervision areas.
Main Sup Tech obstacles: Not one tool requires long-term planning for integrated solutions, useless without good data quality, Lack of internal IT/project expertise, High costs (infrastructure, training, maintenance), Needs strong governance and leadership support.

The Interviewee emphases the need for good quality data or information as a basis for DFS supervision. He says that good quality data makes good decisions and supervisors have to adopt to advancing technologies and proactiveness of identify risks or lapses in processes. There is need for continuous training of the supervisors.

It is interesting look at data from the perspective of frequency, granularity and quality. All these are mutually re-enforcing which collectively contribute to effectiveness of supervision.