¿Estás asignando recursos a leads que nunca cerrarán? ¿Cómo puedes tomar mejores decisiones crediticias al cierre del año si no anticipas quién va a desistir o ser rechazado por el banco?

🧠 Success Story: Predictive Model of Risk and Abandonment in the Housing Sector 

A scalable solution: from mortgage loans to other types of financing 

This success story is based on a model designed for credit risk in home purchase processes , but its approach, technology, and results can be easily adapted to other financial products : personal loans, automotive loans, consumer loans, or fintech lines. 

 

The challenge: hidden losses in the credit approval process 

In the housing ecosystem, the loan origination and housing unit allocation processes face silent but costly challenges. 

For financial institutions and fintechs: 

• High dropout rates : Thousands of families begin the process, but give up before formalizing their loan. 

• Unnecessary rejections : Traditional models fail to accurately identify customers who might pay, leading to lost business opportunities. 

• Resources poorly invested in leads that don't move forward. 

For construction companies: 

• Units assigned to customers are then rejected by banks , forcing the sales process to restart. 

• Opportunity costs : While a new buyer is sought, the unit is held up. 

• Accumulated financial costs , since the investment generates interest costs without immediate return . 

These inefficiencies affect the profitability, liquidity, and business planning of key players in the sector. 

 

Our solution: a predictive scoring model with machine learning 

At CFOcus we designed and implemented a machine learning- based system to predict in advance: 

• The probability that a customer will abandon the purchasing process 

• The real risk of default , long before formal approval 

Key components of the solution: 

• 🔍 Analysis of predictive variables : credit history, sociodemographic profile, savings level, income, number of dependents. 

• 🤖 Precision -trained models validated through confusion matrices to reduce false positives and improve business decisions. 

 

Results: precision, efficiency and tangible savings 

• 🎯 Over 85% accuracy in predicting churn and credit risk. 

• ⏳ Reduction in operating times by focusing efforts only on clients with a high probability of closing. 

• 💸 Direct savings for banks and fintechs in processing, analytics, and tracking. 

• 🏗 Lower financial costs for construction companies by avoiding assigning units to clients who will later be rejected. 

• 📈 Conversion optimization by prioritizing prospects with the highest closing potential and credit approval. 

 

What makes this model unique? 

This model is based on machine learning algorithms , an artificial intelligence technique that allows systems to improve their accuracy over time as they process more data . 

Models used: 

1. Logistic regression 

1. Decision trees 

1. Random Forest 

1. XGBoost (Extreme Gradient Boosting) 

Thanks to this approach, the system doesn't rely on fixed rules, but instead learns hidden patterns in historical data and adjusts to the real-world conditions of each lending transaction. 

 

What does this mean for your business? 

If your company: 

• Lose customers due to slow or poorly calibrated credit processes 

• You can't anticipate which leads are viable 

• Assign units or resources to people who are not going to close 

This model allows you to: 

✅ Identify customers with the highest probability of conversion
✅ Reduce operational and financial risks
✅ Increase sales efficiency without increasing your team 

Schedule your appointment here 

 

This isn't theory. It's execution.
And it's already helping companies in the sector make better decisions, reduce risks, and capture more value.