Risk management and data analytics survey (2026)

What it is about

Junot García Calderón, from PwC Interamericas, presented at the Fourth Annual Risk Conference (September 18, 2026) the results of the 2026 Risk Management and Data Analytics Survey, carried out by PwC Dominican Republic with the Dominican Republic Risk Management Club among risk officers.

The 33 slides analyze 30 institutions, 18 financial intermediation entities and 12 securities market participants, and answer five questions: how mature risk management is, which risks concentrate attention, where the management and measurement gaps are, how prepared institutions are for the risks to come and whether technology is raising management capability. They close with five priorities for the sector.

Objectives

• Measure the maturity of risk management in Dominican institutions, from governance to analytics.
• Identify the gaps between declared appetite and the quantification of non-financial risks.
• Assess preparedness for emerging risks, ESG risk and artificial intelligence.

Key takeaways

1. The foundation is built: 97% have a risk plan approved by the board and 87% assign analytical models to the Risk area; governance is the starting line and not where the race is won.
2. A total of 77% name artificial intelligence as the main emerging risk over five years and 93% expect a very notable impact, but 63% are only starting and 27% do not mitigate any AI risk.
3. Non-financial risks are in the appetite, but they need to be quantified: the gap between presence in the appetite and regular quantification reaches 44 percentage points among financial intermediation entities (compliance, reputational and cybersecurity) and 67 among securities market participants (suppliers and third parties).
4. Only 27% feel prepared for climate risk, and data quality is the common bottleneck of analytics and AI; 60% name IT and cybersecurity as the most vulnerable area.
5. The five priorities are to strengthen capabilities, quantify the non-financial, raise ESG readiness, invest in data quality and close the AI gap by moving from enthusiasm to governance.