Digital Automation Use Cases in Strategic Domains
Predictive statistical modelling and automation techniques applied on historic travel ticketing data can detect leakages in and recover revenues. Analysis of web searches and social networks to predict demand can lead to optimization of routes, destinations and journey time. This helps in making better decisions around handling disruptive events such as adverse weather, labor strikes and preventive maintenance.
Unstructured customer data extracted from social networks and travel databases and analyzed using textual AI technologies (like natural language processing and message tailoring) can help customize travel recommendations for customers and up-sell ancillary services.
A consumer may fall into different user profiles for different transactions. In such cases, pattern-matching algorithms identify appropriate profiles in real time for highly personalized offers based on past transaction history.
Raw transaction history and claims data across disparate systems can be extracted, aggregated, compared, and analyzed without disturbing back-end operations to detect underlying relationships. Statistical modeling techniques and pattern-matching algorithms can be leveraged to detect possible anomalies and avoid losses arising from fraudulent claims.
Data extracted from social media can be monitored and analyzed using techniques such as natural language processing and translation and user modeling. This gives a better understanding of consumer needs and behavior, which in turn helps insurers make more personalized and targeted policy proposals.
Cognitive technologies can be leveraged to verify claim forms, identity proofs and photographs for authenticity. Appropriate alerts may be raised to prevent fraud.