Synthetic demonstration data

Auditable analytics for collective financing

MarkovIQ, the early warning your reserve fund never had.

See which participants are likely to cancel, how that hits your cash flow, and which groups need attention — before it happens.

Explore the interactive demo See how every result is checked

Behavior-to-cash-flow traceVersioned model evidenceBase and stress views

Executive portfolio

Reserve pressure overview

Run 5F1C5433

Covered groups

7 / 22

Fund balance

COP 10M

High-risk queue

567

G-006 · Priority 01

watch · 46

On track to run short of reserves within 2 years.

Healthy comparison

Health Score 86

0% reserve pressure

Projected Effect to Group

46.9 expected exits

Lost contributions + refund pressure

Product walkthrough

Watch how MarkovIQ turns expected participant behavior into forward-looking cash flow and liquidity insight.

Beyond traditional risk views

See financial pressure before it appears in today's KPIs.

MarkovIQ extends your existing risk framework with forward-looking behavioral and liquidity signals.

Traditional risk view

Participant → Credit risk → Current score / KPI

With MarkovIQ

Behavior forecast → Expected cash flows → Liquidity and reserves → Stress scenarios → Management decision

One connected analytical chain

Don't just predict participant behavior. Demonstrate why!

MarkovIQ connects behavioral uncertainty to the economics of a financing group, with a visible evidence trail at every step.

Predict participant behavior

Estimate cancellation risk from tenure, payment behavior, recent cure, and financial indicators.

Propagate expected states

Translate participant-level probabilities into expected group behavior over time.

Project group cash flows

Connect contributions, refunds, allocations, and obligations to a reconciled fund forecast.

Prioritize a decision

Show where reserve pressure may emerge, why, and which queue deserves a policy-compliant review.

Methodology, not recordkeeping

Start with the participant, then follow the money.

A cancellation probability is useful only when leaders can see what it means for expected contributions, refund pressure, future obligations, and minimum reserve coverage. MarkovIQ applies a governed analytical chain instead of adding another customer-management workflow.

Identify participants with the greatest risk and understand why
See how participant behavior affects expected cash flow
Understand when group liquidity and reserves may come under pressure
Know what the analysis covers and where data may be incomplete

Follow the featured G-006 story

Participant evidence

See the current value, reference group, direction, and probability-point effect of every driver.

Cash Flow Analysis

Connect expected exits to lost contributions and refund pressure before viewing group reserves.

Transparent Group and Participant Health

Inspect each reading, floor, target, sub-score, weight, and contribution to the index.

Easy to Audit Calculations

Keep AI out of the financial math. Every output comes from a versioned deterministic engine.

Base case and stress scenario

Make assumptions visible before discussing outcomes.

Compare the expected path with one disclosed stress: cancellation +20%, delinquency +15%, obligations +10%, and advance-payment participation −15%.

Compare the scenarios

Base case

Expected path

Forecast using observed behavior and current assumptions.

Stress Scenario

Test Different Conditions

Change key assumptions and see the impact on cash flow, liquidity, and reserves.

Built by QED Analytica

Financial engineering meets behavioral AI.

The company brings together quantitative finance, risk, machine learning, and production technology—with banking experience across the United States, Mexico, and Colombia—to build specialized forward-looking decision systems.

Alex Restrepo, MSEE

Co-Founder * Quantitative Finance, Risk and Technology

Hands-on quantitative finance and technology leader with deep expertise in cash-flow modeling, valuation, risk analytics, stress testing, and building analytical systems.

Alejandro Correa Bahnsen, PhD

Co-Founder · AI, Credit Risk & Technology

Data and AI executive, entrepreneur, professor, and researcher focused on behavioral modeling, credit risk, explainability, and production AI products.

Follow participant behavior all the way to its financial impact.

Explore a fully synthetic, reproducible workflow with no client data and no black-box claims.

Open MarkovIQ