Deploy a Custom Credit Model in Under 4 Weeks
A custom credit scoring algorithm for an SMB lender costs $20,000 to $45,000. This fixed-price build includes data engineering, deployment, and initial model tuning.
Syntora develops custom credit scoring algorithms for SMB lenders. These systems would integrate various data sources and use machine learning to predict default probability. Syntora's approach focuses on building tailored, scalable solutions that enhance underwriting decisions.
The final scope depends on the number and quality of your data sources. A lender with clean historical data and API access to Plaid is a straightforward build. Integrating with a legacy loan origination system or processing non-standard PDF bank statements requires more development time.
Syntora specializes in developing data-driven systems that bring intelligence to complex decisions. For instance, we engineered the product matching algorithm for Open Decision, an AI-powered software selection platform. This system matches business requirements to software products using the Claude API for understanding and custom scoring logic. This experience in architecting intelligent systems and custom decisioning logic would directly inform the development of a tailored credit scoring algorithm for your lending operations.
What Problem Does This Solve?
Most small lenders start with the scoring module in their Loan Origination System (LOS). These are often simple, linear models based on FICO and stated revenue. They cannot process unstructured bank transaction data or incorporate industry-specific risk factors, and their logic is a black box.
A 10-person lender specializing in loans for construction contractors saw this firsthand. Their LOS rejected a contractor with a 650 FICO score. A manual review of bank statements showed consistent, large deposits from prime contractors, but the generic model missed this completely. This manual review process created a 3-day bottleneck for every single application, delaying good loans and frustrating applicants.
Trying to build a model with a generic platform like Google AutoML Tables also fails. These platforms do not perform the critical data engineering step. They cannot connect to Plaid to pull real-time cash flow or use OCR to extract data from PDF statements. You end up with an expensive tool that cannot access the most predictive data for SMB lending.
How Would Syntora Approach This?
Syntora's approach to building a custom credit scoring algorithm would begin with a discovery phase to understand your specific lending model and data environment. Syntora would connect to your data sources via API, integrating with platforms like Plaid for bank transactions, Codat for accounting data from QuickBooks or Xero, and your existing loan origination system (LOS) API for application history. Historical loan outcomes would be pulled into a Supabase Postgres database using Python-based data ingestion scripts utilizing libraries like `pandas` and `httpx`.
From this aggregated data, Syntora would engineer predictive features tailored to your business context. This would involve developing a Python-based pipeline to calculate relevant metrics such as cash flow volatility, average daily balance, and non-sufficient funds events, as well as categorizing spending from transaction narratives. This feature engineering process would be packaged to run efficiently on a schedule or on-demand, often leveraging services like AWS Lambda for scalability.
For model development, a gradient boosting algorithm, such as XGBoost, would be trained to predict the probability of default. The model would be wrapped in a FastAPI application, containerized with Docker, and deployed on cloud infrastructure like AWS Lambda to ensure high availability and responsiveness. When a new application is submitted, a webhook trigger would hit this API, which would then return a credit score and relevant reason codes.
The FastAPI service would be designed to write the generated score and key reason codes (e.g., 'High cash flow volatility', 'Recent NSF events') back into custom fields within your LOS. To ensure system reliability, structured JSON logs using tools like `structlog` would be implemented, and AWS CloudWatch alarms would be configured to monitor API latency and error rates, providing proactive notifications, for instance, via Slack.
What Are the Key Benefits?
Underwrite in Minutes, Not Days
Reduce application review time from hours of manual work to an automated score delivered in under one second. Free up your underwriters to focus on complex cases.
Pay Once for an Asset You Own
A single fixed-price build delivers the full source code to your GitHub. No recurring per-seat or per-API-call fees that penalize you for growing your loan book.
Score Applicants Using Cash Flow
Go beyond FICO. Our models use real-time cash flow from Plaid and accounting data from Codat to find creditworthy businesses missed by traditional bureaus.
Explainable, Not a Black Box
Every score is delivered with clear reason codes. Your underwriters see exactly why an applicant scored high or low, enabling better decisions and defensible compliance.
Monitored 24/7 After Launch
The deployed system includes health checks and latency monitoring using AWS CloudWatch. We configure alerts to ensure you are the first to know about any production issues.
What Does the Process Look Like?
Data & Systems Access (Week 1)
You provide read-only API keys for your LOS, Plaid, and historical application data. We perform a data audit and deliver a complete feature engineering plan.
Model Build & Validation (Week 2)
We build the feature pipeline and train the first model. You receive a validation report showing model performance on your historical data using AUC and precision-recall curves.
API Deployment & Integration (Week 3)
We deploy the scoring API on AWS Lambda and configure the webhook in your LOS. You receive API documentation and a test environment to run sample applications.
Live Monitoring & Handoff (Week 4+)
The model scores live applications while we monitor for 30 days. You receive the full source code in your GitHub repository and a technical runbook for maintenance.
Frequently Asked Questions
- What factors most impact the cost and timeline?
- The primary factors are the number of data sources, the cleanliness of historical loan outcome data, and the complexity of the LOS integration. A modern LOS with a well-documented REST API is much faster to integrate with than a legacy system requiring custom connectors. A data audit in week one clarifies the exact scope before the main build begins.
- What happens if Plaid's API is down or a bank connection fails?
- The system is designed for graceful failure. If a critical data source like Plaid is unavailable, the API returns a specific error code. Your LOS can then flag the application for manual review. No application data is lost. This is a more resilient approach than letting an automated system make a decision with incomplete information.
- How is this different from using a platform like DataRobot?
- DataRobot is an AutoML tool for teams that already have data scientists. It does not handle the data engineering: pulling from Plaid, cleaning transactions, or deploying a production API with webhooks. Syntora delivers the complete, end-to-end production system, from data ingestion to the live, monitored API that integrates with your workflow.
- What are the ongoing infrastructure costs after the build?
- The system runs on AWS Lambda and Supabase. For up to 5,000 applications per month, typical monthly hosting costs are under $50. This covers all API calls, data storage, and logging. You pay the cloud providers directly, so there is no markup. We provide a detailed cost breakdown based on your expected volume.
- How do we handle model drift and retraining?
- The initial build includes a Python script for retraining the model. We recommend running it quarterly on the latest 90 days of loan outcomes to prevent drift. Our optional flat-rate monthly maintenance plan includes us performing this retraining, validation, and deployment for you so your team doesn't have to manage it.
- What is the minimum amount of historical data needed?
- For a reliable model, we need to see at least 100 past loans that have defaulted and around 500 total loan outcomes. This provides enough data for the model to learn default patterns without overfitting. If you have less data, we can still build a system, but we will recommend a simpler rules-based engine as a starting point.
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