There are two major components of an optimized fraud strategy— automation and manual review, with the latter proving to impose significant penalties in terms of operational cost, transaction volume, and customer friction. When it comes to automation and manual review, it’s important not to put the cart before the horse. While manual review will likely never become completely obsolete, the technical advancement of identity verification tools that leverage the power of machine learning during the automation portion of the process can and should be optimized first to drive 3 main goals:
- Increase auto-accepted transactions
- Increase auto-rejected transactions
- Optimize manual review queue
It’s a well known fact that the fraud industry is ever-changing, and therefore the tools and workflow processes used to battle fraud must evolve along with it. In the current state, 82% of companies are fully or very motivated to increase automation. To take it a step further, 71% of companies believe that machine learning can be leveraged to reduce manual review.
The manual review process is costly (ranging from $1-$6 per/transaction), time consuming, and can negatively impact the customer experience. Optimizing the automated portion of the workflow is imperative to streamlining the customer experience and driving revenue.
Read more in the full report: The State of Identity Maturity Verification