Synechron

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Data Science

Artificial Intelligence (AI) and Machine Learning (ML) are being looked at across the financial services industry to solve complex business challenges through analyzing correlations in massive amounts of data to test hypotheses and predict future outcomes. The goal is to discover meaningful relationships between events that impact one another (correlation) or perhaps even cause a future event to happen (causation).

Predictive Analytics has become more advanced as big data, high-compute, the cloud, and open source models make high-tech AI commoditized for businesses. However, understanding the right models to apply in what context and with what data set and training approach requires both technical and business knowledge, and can therefore be a challenge.

Synechron’s techno-functional data science experts are working with Banking, Financial Services, and Insurance companies to combine Data Science expertise with applied AI in BFSI. The approach is having a significant impact for our customers.

We recently worked with a major insurance provider that manages a supply chain of thousands of replacement items needed to service personal property claims. Synechron recognized macro-market events that historically impacted the supply chain could be used to analyze and predict current customer behaviors and therefore future inventory management needs resulting in enhanced customer intelligence, risk management, and inventory management.

In another project, we focused on trade anomaly detection, analyzing large data volumes to identify anomalous trades and prioritize queries within one hour of detection in order to address real-time regulatory remediation requirements.

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