AI / ML
Artificial Intelligence. Machine learning. It sounds so promising, and can be. But there are challenges.
- Building an AI/ML competency is difficult. Talent is hard to find and expensive.
- It is one thing to come up with a model that works in a data science environment — it is another to make it robust, production ready and integrated with existing applications.
- It is not always appropriate to deploy AI/ML. Sometimes it is best to start with a traditional application and fold AI in later.
Data Integrity Challenge:
An enterprise data management firm was looking for a better way to find outliers in fixed income data. These included incorrect prices, ratings and other data fields. It had tried to manually build a series of rules to analyze the data, a time intensive process. Despite this, too many anomalies were slipping through. The firm thought that ML techniques could help, but did not have the expertise on staff to try them.
DataArt developed a state-of-the-art outlier detection platform based on machine learning technology. The system automatically ranks potential anomalies and brings the most likely outliers to the top of the list in real-time.
The resulting technology is easily adaptable to other uses, such as exception management, trends detection, fraud detection, etc.
- ML system detects a wider range of outliers.
- Outsourcing was quicker and cheaper than building internal ML team.
- ML system specifically designed to minimize maintenance costs.
- Enhanced customer experience due to higher data quality.
- Fewer data quality investigations.