ElectrifAi | Insurance

Insurance

Navigate disruption and risk, stay competitive, and achieve growth with ElectrifAi's machine learning solutions.

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Highlights

Revenue

Get enhanced speed-to-market, reduced loss ratios, higher conversion rates, improve quote accuracy, and increase cross-sell potential.

Efficiency

Automate traditional processes to better detect fraudulent claims, reduce churn, and boost spend and contract management operations.

Insights

Gain a better understanding of the market to help with market selection, underwriting, pricing, and managing claims.

Use Cases

Use Cases That Make Us Different

  • Churn Mitigation

    Insurance companies depend on long-term customers to maintain a flow of revenue. But with so many options available, customers can easily shop around for lower prices each year. Mitigating churn (keeping existing customers from going to a competitor) is crucial to ensure long-term customers remain loyal. Machine learning helps to determine what marketing campaign will work for each customer by accurately analyzing patterns of behavior, payment history, demographics, etc.

  • Dynamic Pricing

    Leveraging pricing power through dynamic pricing is a great way to achieve business goals and remain competitive. For insurance companies, this means policies are cheaper for lower risk customers and more expensive for higher risk customers, based on a wide variety of potential factors. Such as those customers who commute 30 miles to work every day are higher risk than those who work from home. Manually processing these factors, however, is time-consuming and tedious. Machine learning can help process the data used to assess a customer’s risk and generates a score to determine how much to quote the customer, thereby greatly decreasing the insurance company’s risk.

  • Fraud Detection

    Fraud happens every day, whether intentional or accidental. From people lying after an accident to those unknowingly giving false information, fraud can cost an insurance company millions of dollars. Auditors can process perhaps 10% of all claims. Machine learning, however, can analyze 100% of claims to spot inconsistencies in the data and those claims are then marked as requiring review. The auditor can then review the suspicious claims, increasing the chances of catching fraud and saving the company money.

  • Marketing

    Marketing to potential customers and cross selling to existing customers allows insurance companies to increase profit margins. Personalizing offers increases the likelihood people will be interested in that offer and sign up. Such as sending a promotion for opening a new account to a newly married couple or sending a new homeowner an offer to bundle insurance for home and auto. But sending those offers to people likely to sign up without incentives is throwing away money. How do you accuratelysend personalized offers? Machine learning identifies specific marketing techniques ideal for each person’s unique circumstances by analyzing thousands of data points faster than analyzing manually.

  • Spend and Contract Management

    Insurance companies process tons of contracts each day, with many departments dedicated to manual invoicing and review. Machine learning can take much of the work that goes into invoicing and contract review by automating tedious tasks, significantly increasing the department’s efficiency. Machine learning can help extract key terms to indicate when contracts expire, renew, or when they should be renegotiated. Machine learning can also help to spot invoice inconsistencies, such as a suspicious amount or a wrong due date based on the terms in the contract.