Impact on Supply Chains:
Among the lasting effects of the coronavirus pandemic has been the realization that global supply chains can be alarmingly fragile. With or without disruption, manufacturers are acutely aware of how complicated many supply chains are to maintain and optimize.
Consider the day-to-day challenges faced by auto manufacturers. The first requirement is to accurately forecast customer demand to determine the number and types of parts to order — down to the various models and options buyers are expected to choose. Those predictions need to sync with the availability of parts from hundreds of suppliers, along with estimates of manufacturing efficiency and supplier risk.
Jaguar Land Rover (JLR) chose a graph database solution because it could span the many data silos that needed to be tapped for supply chain analysis — and explore the matrices of relationships among data elements. The primary goals were to increase the average profit per unit sold and to reduce aged inventory, along with minimizing the effects of supplier disruption. Some key supply-chain planning queries at JLR now take 45 minutes as opposed to weeks and, more importantly, management can answer questions it never had the opportunity to ask before.
Improving the accuracy of fraud detection:
We’ve all witnessed the evolution of fraud detection through our bank, credit card and telecom companies. Early rule-based efforts tended to miss dubious transactions or flag innocent transactions as fraudulent. When the financial industry adopted graph databases to augment their AI/ML efforts, however, the accuracy of fraud detection improved noticeably.
Graph databases coupled with AI/ML improve the accuracy of fraud detection, reducing false positives and detecting anomalies that might otherwise be missed. Machine learning must draw on many different data types to model a customer’s normal behavior — location, device, payment type, authentication method and so on. Plus, what’s defined as normal behavior patterns must be adjusted on the fly in response to legitimate change. Graph databases support that dynamism and enable AI/ML to traverse customer interactions to identify significant variances.
Financial services giants JP Morgan Chase and Intuit have both adopted graph databases to boost their AI/ML fraud detection efforts. JP Morgan Chase uses a graph database to help protect more than 60 million households in the U.S. According to Intuit, graph-based machine learning has enabled the company to detect 50% more potential fraud events and has reduced false positives by approximately the same percentage.
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