5 Considerations When Evaluating Fraud Solutions
The fraud prevention ecosystem is chock-full of solution providers and it can be a bit dizzying navigating the different technologies, industries and use cases in which each one specializes. Everyone knows that a “layered approach” is best when fighting fraud, but layered wrong and all you wind up with are stacks of inefficiencies and/or holes.
The devil is always in the details, but here are some guiding principles when procuring the best fraud technology for your organization:
Convergence is Key
- We are seeing many use cases converge: authentication is blending with fraud and fraud with AML. Blending the data and technology that addresses these use cases only makes sense.
- These centralized locations go by different names depending on the scope (authentication hubs, orchestration hubs, risk hubs, etc); however, the more important aspect is understanding the function they serve —> managing the multiples…
- Multiple data streams (ie: internal/external) and multiple use case (ie: payments fraud/AML) on the same platform across multiple teams. This is a significant undertaking, but the long-term dividends are well worth it.
Not All Orchestration is Created Equal
- Orchestration is a popular industry buzz word, as it insinuates an intelligent utilization of data and decisions. It’s important to dig a bit deeper and ensure strong analytics underpin orchestration hubs.
- Risk-based analytics are the point at which a basic aggregation hub evolves into actual orchestration.
- The hallmark of true orchestration is leveraging data and workflows in a more effective and efficient manner, not simply pooling data in one place.
Data is King (Sometimes)
- Generally speaking, “the more data the better” is useful when assessing the need for rich data in a machine learning model. However, there are some important caveats…
- Some data sources are redundant and provide the same insight and uplift.
- Additional data sometimes causes more noise to your machine learning model and actually hurts performance.
- To get the most bang for your buck, leverage experts to help understand the data sources and map them to a well-designed schema for each fraud use case.
Consider Impact to Operations Teams
- Management teams in operations should have input into how the tools and technology will fit in with their current workflows and processes.
- New data and technology must be evaluated from all angles and effectively utilized by the folks making the final decisions on the alerts.
Make them Prove their Concept
- Every POC will look different depending on the solution you are procuring (ie: data enrichment v. fraud platform), but it’s important to obtain quantitative validation for your organization.
- Case studies are nice, but remember the cases they are studying are for organizations with different technology, data and resources. These results don’t simply map over to your company.
- A well-structured POC serves as the best tool to understand potential performance and value.