Audit & Quality Control Analytics
Audit Analytics.
Gone are the days where a randomized sample is sufficient. As institutions merge and grow the volume of loans originated increases as well but, the staffing dedicated to the internal audit function does not grow.
Without leveraging data, things get missed, pervasive issues continue and as an institution we are exposed to more risk.
We believe that data should lead the sampling process and through the leveraging of advanced analytics you can test a sample size identify issues and quantify the pervasiveness of the problem.
Quality Control Analytics & Dashboards
When it comes to quality control, good enough isn’t “good enough” anymore. Why test 20% of originations when you can test 10% and have more meaningful results and better assurance over the detection process.
Why assume that dormant flags are functioning, when they might not be?
Why would you spend hours looking at file maintenance logs when specific items represent more or less high risk?
What do the following areas have in common:
File Maintenance
Lending Quality Control
Dormant Account Review
They tend to be a detractor of resources without meaningful results. Analytics can be incorporated into the quality control process that allows you to test items of concern and spend less time on less meaningful items. By leveraging AI we can incorporate CRM comments and notes left by employee’s into the review process for file maintenance, dormant accounts, and lending quality control as well and flag items based on key words. Advanced analytics allows us to centralize data that is decentralized.
Saving time and money as well as improving the quality control function can only help your organization.