Why RFC, CIEC, and Predicting 'Success' Will Define Risk in 2026

At CRiskCo, we see a clear trajectory: by 2026, a credit risk model that relies solely on "Probability of Default" will be obsolete. We are witnessing a rapid market shift in Mexico where direct SAT access (RFC and CIEC) is becoming the baseline standard for lending, not a competitive advantage. The real race for risk officers and CFOs is no longer just about filtering out bad actors, it is about algorithmically identifying the future market leaders. 

For the last five years, the "holy grail" of Mexican fintech was simply getting the data. Now, as big banks prepare to start working with SAT extraction services through 2026 (with an estimated 80%+ market adoption by 2027), the market is fracturing into three distinct speed groups:

Group 1 (The Lagging): Relying on manual reports for SAT data, operating with slow, traditional processes.

Group 2 (The Adopters): Using automated tools to pull specific files (Constancia Fiscal, Opinion De Cumplimiento, P&L, and balance sheets) but still requiring manual review and interpretation.

Group 3 (The Leaders): Fully integrated systems that access standardized, clean data, spreading instantaneous risk parameters directly into their underwriting models.

The trap is clear: If your institution is not Group 3, you are already falling behind.

The Economics of Risk: Why Adoption is Inevitable

The move toward full integration isn't ideological; it's financial. The costs of maintaining the Group 1 or 2 systems are no longer justifiable. We witness tangible ROI in lenders who fully automate SAT data access:

Decision Time: Reduced from three weeks to under 24 hours.

Operational Cost: A 70% reduction in time and manual effort per application dramatically lowers the cost of customer acquisition.

Data Acquisition: Time to data reduced from days/weeks to mere hours.

Such a massive reduction in cost and time creates market conditions that any financial institution (FI) not adopting this technology will be quickly priced out of the market.

The Innovation: Probability of Success

Even for the "Leaders" (Group 3), full integration is just the price of entry. The most significant shift in modeling isn't happening in how we access data, but in what we predict with it.

To win in this new environment, lenders must adopt a Dual-Engine Model—moving beyond the defensive Probability of Default (PD) to include the offensive Probability of Success (PS).

PD: The defensive engine. It analyzes tax gaps and dipping margins to answer: "Will they fail?"

PS: The offensive engine. It analyzes revenue velocity, client diversity, and supply chain consistency to answer: "Will they grow?"

The 'Success' Logic

Traditional risk models often reject high-growth SMEs because rapid spending looks like "risk." Success modeling, fueled by standardized SAT data, recognizes that spending as investment in growth.

 Above: A visual of the ideal target zone: low default risk combined with high growth probability (Probability of Success).

Final Takeaway for the C-Suite

For risk, compliance, commercial officers, or CFOs, this shift is critical for portfolio yield. When you model for success (PS), you increase your Lifetime Value (LTV) by identifying borrowers who will need larger lines of credit and are most likely to accept cross-sold products.

By 2027, the lenders who lead the market won't just be those with the lowest default rates. They will be the ones who used standardized SAT data to bet on the right horses — the ones most likely to succeed.
 

Erez Saf is CEO of CRiskCo and president of Pymes Capital, companies using data and AI to empower smarter, more inclusive lending for SMEs in Mexico and Latin America.

Read Erez’s previous article in Mexico Business News:
"Credit Intelligence Next Revolution SME Financing"