Automated Credit Decisioning Guide for Lenders

Automation boosts the productivity and efficiency of the credit decisioning process. When lenders leverage automated credit decisioning, they can define lending criteria more precisely, approve creditworthy customers more quickly, and reject applications from customers who don’t fit their lending portfolio criteria.

Automated credit decisioning uses technologies and algorithms to assess the total lending package that is in front of the lender. The automated system analyzes and evaluates an applicant’s risk profile based on vast amounts of data, including financial history and credit reports. A credit decision is then generated based on predefined criteria and thresholds, allowing lenders to make rapid and consistent loan decisions.

According to a Forrester survey on behalf of Experian, lenders are already using automation across the credit lifecycle and plan to invest more in automation in the coming years. While there are numerous benefits to automated credit decisioning, the survey found there are also several challenges. Automation raises questions about transparency, fairness, and accountability in decision-making and could introduce unintended bias. And the results of automated decision-making could be difficult to explain to customers and regulators. Below is a table detailing the benefits and challenges of automated credit decisioning for lenders.

Automated credit decisioning offers advantages and challenges that lenders should carefully weigh, considering their business needs and the expectations of customers. Monitoring, testing, and updates are essential best practices to ensure the effectiveness, fairness, and compliance of automated credit decisioning systems.

Automated Credit Decisioning: 10 Best Practices

So, what best practices can lenders use to avoid the pitfalls of automated credit decisioning while still enjoying the benefits? Here are the top 10 best practices for lenders:

1. Objectives and Criteria

• Clearly articulate the objectives of the automated credit decisioning system.
• Establish the criteria used for credit evaluations based on risk tolerance and business goals.

2. Data Quality and Integration

• Ensure the accuracy and reliability of the data used in the decision-making process.
• Leverage advanced data aggregation tools and technologies to efficiently collect and integrate data from various sources, both traditional and alternative data.
• Regularly audit and clean data to remove inconsistencies and redundancies.

3. Scalability and Flexibility

• Use cloud-based solutions for scalable data storage and fluctuations in application volumes.
• Regularly update models based on evolving consumer trends and behaviors.
• Develop flexible decision models to accommodate lending criteria, regulations, or business strategy changes.

4. Explainability and Transparency

• Prioritize using explainable models that allow stakeholders, including customers, to understand the factors influencing credit decisions.
• Be transparent about the factors influencing credit decisions and avenues for recourse in disputes.
• Incorporate mechanisms for human oversight, especially in complex or exceptional cases.

5. Continuous Monitoring and Improvement

• Implement monitoring tools to track the performance of the automated system.
• Regularly review and update decision models based on performance analytics and emerging trends.
• Regularly audit automated credit decisioning models for bias.

6. Compliance

• Regularly update the system to ensure compliance with changing regulatory requirements.
• Establish clear guidelines for the ethical use of customer data, ensuring privacy and transparency in the decisioning process.

7. Security

• Implement robust security measures to safeguard customer data and protect against cyber threats.
• Regularly update security protocols to address evolving risks.
• Streamline identity verification processes to reduce false positives and prevent fraud.

8. Training and Skill Development

• Provide training for staff managing and overseeing the automated credit decisioning process.
• Encourage continuous learning and skill development for all key stakeholders in the credit decisioning and strategy process.

9. Documentation

• Maintain comprehensive documentation of the credit decisioning process for auditing purposes.
• Document any changes made to decision models and the rationale behind them.

10. Testing and Validation

• Conduct thorough testing before deployment to identify and rectify any issues.
• Regularly validate the performance of the credit decisioning models, especially when introducing changes.

Using these best practices, lenders can build a robust and responsible automated credit decisioning system that aligns with regulations, protects customers, and supports their overall goals. 

Technologies To Improve the Credit Decisioning Process

Lenders can use various technologies to improve efficiency, accuracy, and overall customer experience of their automated credit decisioning process. Below, we list some of those key technologies.