See how BGL BNP Paribas was able to improve fraud detection and democratize the use of data across the organization while maintaining their high standards for security and data governance.Learn More
Every day, thousands of claims, diverse data, and customer queries are produced in the insurance industry, which makes it a perfect environment for AI-driven systems (including both data science and machine learning). Indeed, the application of data science in insurance is a must for providers to stay ahead of fraudsters, reduce losses, and provide the best risk-adjusted solutions to their customers.
Premiums, risk projections, claim adjustments, customized financial advisory, and underwriting are all derived from the provider’s ability to make accurate data-driven predictions. And it’s all connected; inaccurate forecasting leads to misaligned premiums, inaccurate risk projections, and ineffective balance sheet management — all of which can cripple an insurance provider’s bottom line and, ultimately, increase costs for the consumer. Plus the price of mistakes is high, so minimizing risk is critical.