AI Implementation Checklist: 45-Point Guide for Lending Platforms

Specific KPIs for accuracy, speed, cost reduction, and capacity improvement

Senior leader responsible for budget approval and organizational change

Cross-functional team with representatives from underwriting, operations, IT, and compliance

Weekly status updates, stakeholder meetings, and team training schedules

List all loan origination, document management, and servicing systems

APIs, data formats, authentication methods for system connections

Data encryption, access controls, audit trails, and regulatory requirements

Network capacity, firewall configurations, and performance requirements

Bank statements, tax returns, appraisals, title reports, credit reports, etc.

PDF, scanned images, digital uploads, and their quality variations

Specific fields to extract from each document type with validation rules

100+ representative documents per type for AI model training

Match existing system fields to AI extraction outputs

Rules for cross-checking extracted data against existing systems

Acceptable accuracy thresholds and error handling procedures

Procedures for data backup and disaster recovery

Step-by-step processes for loan application through funding

Tasks suitable for AI automation vs. those requiring human review

Optimized processes incorporating AI-powered automation

Procedures for low-confidence extractions and complex cases

Revised roles focusing on analysis vs. data entry

Clear escalation paths and decision authority matrices

Process for ongoing monitoring and accuracy validation

Individual and team KPIs aligned with AI implementation goals

Provide diverse sample documents for each document type

Review and correct AI outputs to improve model performance

Define accuracy levels for automated vs. manual processing

Underwriting criteria, risk scoring, and decisioning logic

Set up secure connections between AI platform and existing systems

Verify real-time data flow and error handling between systems

Encryption, access controls, and audit logging configurations

System health monitoring, performance metrics, and error notifications

User guides, video tutorials, and quick reference cards

Role-based training for underwriters, processors, and managers

Practical exercises using test data and simulated scenarios

Identify and train power users for ongoing support

Clear messaging about AI impact on roles and processes

Open forums for questions and feedback about the changes

Help desk, super user network, and escalation procedures

Process for collecting and acting on user suggestions

Test individual components and integration points

Complete workflow testing with sample loan scenarios

Compare AI extractions against manual verification results

Load testing with concurrent users and document volumes

Representative sample from each user role and experience level

Realistic loan scenarios covering common and edge cases

Structured feedback on usability, workflow, and system performance

Prioritize and resolve bugs, usability problems, and gaps

Big bang, phased rollout, or parallel run approach

Procedures for reverting to previous systems if needed

Select optimal timing considering business cycles and resource availability

Staff help desk and super users for launch support

Track accuracy, processing times, and user adoption metrics

Assess success against original goals and identify improvement opportunities

Schedule regular reviews and continuous improvement initiatives

Recognize team contributions and share implementation benefits