Artificial intelligence (AI) and Machine Learning (ML) are the new buzzwords for applications over the last decade. It's easy to toss around these terms when actually the applications are performing analytics and claiming the AI and ML tag without using the proven techniques used in AI and ML that provide continuous improvement in analytic results. When implemented correctly, AI and ML can be a significant product and market differentiator.
Optimize Marketing has collaborated with top data scientists in the industry to develop AI and ML models for healthcare, fraud, identity verification through ID Proofing and compliance industries. These data models utilize datasets of over 25 million records to create both supervised and unsupervised models that provide breakthrough insights that were previously unachievable.
Risk Scoring - Healthcare
When trying to detect "unusual" hidden patterns in billions of records, such as fraud, the most common Machine Learning method used is focused on anomaly detection. Two key aspects should be considered when devising anomaly detection. One is data segmentation, considering the entire data set whole or is it more meaningful to be in smaller datasets based on the data context. The other key aspect is data size, whether there is enough data to establish reliable norms.
In healthcare fraud detection, there are numerous key trends to review. One analysis is considering all the claims as segregated by procedure and diagnosis code. A different type of analysis is reviewing behavioral provider patterns, as segregated by specialty. Analysis for both analysis groups based on millions or even a billion data records and 300+ variables requires special consideration of software design to assure reasonable turnaround times. Analytic models to be considered as best fit for anomaly detection are XXXXXX. Both supervised and unsupervised models are valuable for best results.
Compliance Analysis - Financial
The key aspect of compliance analytics is the ability to verify validity based on multiple different distinct and disparate data sources. It is this independence of data reaching the same conclusion that can provide certainty to the analytic results. The merging of data is essential for putting all the "pieces of the puzzle" together to analyze the holistic view of a person or organization.
Performing this analysis requires a multitude of key technologies in order to be successful. One important technology is identity resolution. Since independent data sources use different attributes to describe entities, it is never straightforward to create linkages between sources. Identity resolution is the data management process through which identity attributes are matched across multiple disparate data sources. What appears to be multiple different entities based on different data sources are actually the same entity based on identity resolution. Key algorithms for identity resolution include XXXX
Another key technology to making compliance analysis manageable is using the right technology to store identity associations. Traditional data storage such as relational databases becomes very difficult in execution when the number of data sources grow beyond single digits. A more appropriate database technology for this type of compliance analysis is graphical databases. These more specialized databases, designed specifically for creating relationships between multiple different data sources. In addition, there is minimal performance degradation with graphical databses as the data size or the number of data sources grows exponentially.