7 Steps to Replace Manual Underwriting with Automated Decision Logic

Manual underwriting plays an important role in the credit approval process, especially for applicants with unusual or complex financial situations.

But it takes time and adds cost for both lenders and applicants.

Advances in data and platforms now automate large parts of credit approval, reducing manual work. In this article, we cover how to select a rule engine and implement rules to streamline the credit approval process.

1. Identify and document existing manual underwriting rules

The first step in automating credit approval is to identify and document the rules used in manual underwriting. Include guidelines for evaluating credit history, income, debt levels, and other factors that affect creditworthiness. Clear documentation makes the rules easier to understand and ready to translate into an automated system.

2. Categorize and prioritize rules

Once you document the rules, group them by importance and relevance to approval outcomes. This helps you decide which rules to implement first, and which to modify or replace during automation.

3. Select a rule engine that fits your needs

With the rules defined and prioritized, choose a rule engine that can process and evaluate your criteria. Consider ease of integration, scale, adaptability to changing regulations and industry standards, and the ability to handle complex rules and conditions.

4. Implement rules in the selected rule engine

After selecting the rule engine, translate the documented rules into its format. Build tests, validate outcomes, and confirm the rules behave as expected before moving forward.

5. Integrate the rule engine into the credit approval process

With rules implemented in the rule engine, integrate it into the credit approval flow. Include collection and analysis of relevant data, such as credit scores, income statements, bank statements, employment history, and other financial inputs. The rule engine then evaluates the applicant's creditworthiness based on the established rules.

6. Ensure data quality and accuracy

Accurate automation depends on clean data. Use data cleansing and validation tools, and run regular audits to identify and fix errors or discrepancies.

7. Continuously monitor and update rules

Keep rules current to maintain relevance and effectiveness. Add new data sources, adjust the rule engine's logic, and reevaluate the factors used to assess creditworthiness.

In conclusion, by selecting a suitable rule engine and implementing existing manual rules, lenders can streamline the credit approval process while maintaining the quality and accuracy of credit decisions. This approach supports a practical transition to an automated system, benefiting both lenders and applicants.