Predicting Mortgage Risk with Machine Learning on AWS

Centizen’s advanced machine learning solution has completely transformed how we assess mortgage portfolios. Their use of AWS tools helped us improve accuracy, reduce manual workload, and make smarter decisions faster. Their expertise made all the difference.

– Vice President

A Leading Mortgage Insurance & Loan Servicing Company

Data Ingestion & Security Setup

Centizen kicked off the project by establishing a secure data pipeline. Client data from internal systems was anonymized and transferred to Amazon S3. To ensure data security and compliance:

• AWS Key Management Service (KMS) was used to manage encryption keys.
• IAM policies were configured for role-based access.
• AWS CloudTrail was enabled for activity logging and traceability.

Data Cleaning & Feature Extraction

Using Amazon EMR with Apache Spark, Centizen processed raw datasets to eliminate noise, correct inconsistencies, and standardize inputs. During this phase:

• Features related to property characteristics, geographic location, and peril types were extracted.
• A structured training dataset was created to support accurate model development.

Model Development & Training

Centizen used Amazon SageMaker to build and train nine predictive models focusing on:

• Claim probability
• Recovery estimation
• Peril classification

The team used SageMaker’s automatic model tuning and SageMaker Clarify to detect bias, validate predictions, and optimize model performance.

Workflow Automation & Portfolio Scoring

An end-to-end portfolio scoring pipeline was implemented:

• New mortgage portfolios uploaded to S3 triggered an automated pipeline.
• EMR prepared the data and appended the required attributes.
• SageMaker inference endpoints applied predictive models.
• Results were aggregated into actionable reports for investor review.

The client gained the ability to process large mortgage portfolios with greater speed and accuracy. Centizen’s machine learning models delivered high-precision predictions that improved operational workflows, eliminated manual bottlenecks, and supported data-driven investor decisions. The end-to-end pipeline ensured secure data handling, while automation enabled scale without compromising compliance or quality.

With scalable analytics in place, the client can now analyze thousands of properties in real time. Secure AWS tools like KMS, IAM, and CloudTrail provided full compliance and traceability. These outcomes not only improved investor confidence but also positioned the client as a technology-forward leader in the mortgage insurance industry, ready to scale with growing demand.

Within 8 weeks, the client partnered with Centizen to move from manual data handling to scalable, AI-driven automation, enabling confident, data-backed decisions across thousands of properties.