Insurers are actively investigating how artificial intelligence technologies such as machine learning, natural language processing and predictive analytics can improve customer satisfaction.
Small commercial BOP customers value online interfaces with self-service features and the ability to track the status of interactions in real time instead of having to make inquiries by phone, email, or through their brokers.
By understanding the overall sentiment of employees. insurers and self-insured organizations can be tied to the experience modification factor to assist in the underwriting process and pricing considerations.
Customer retention is an important metric for any insurer and a straight-forward concept. But most insurers struggle with retention despite their best efforts.
So why is keeping customers continue to be a challenge?
We believe the key is understanding what the overall sentiment is for each customer.
It's a known fact that the more coverages you have with a policyholder the higher their retention rate. But cross-selling and upselling is tough.
So when is the best time to cross-sell or upsell a customer?
Customers buy an insurance product only when they need it. So it is critical for insurers to know where exactly a customer is in his
Household analytics provides the ability for insurance carriers and agents to view and understand the portfolio dynamics at a household level instead of just at the individual level. Household analytics allows you to:
One of the challenges for insurers when purchasing a book of business is determining the exposure that comes along with a new book of existing business.
One approach is to develop a customer risk profile to determine which claimants should receive the highest levels of review - before a claim is filed.
This approach of leveraging the power of machine learning and AI helps improve the customer experience for legitimate claimants while making it more difficult for dishonest claimants to go undetected.
For P&C insurers, there is no shortage of predictive analytics solutions available on the market. Many solutions can identify claims that have a high probability of being a fraudulent claim after FNOL.
The Aureus approach is to predict potentially fraudulent claims before they happen and with the large amount of data that insurers already possess.
Using machine learning to develop predictive models, somewhere between 10-13% probable fraud cases in significantly less time than manual identification processes.
This is a substantial improvement over manual identification of fraud cases with less time spent on finding data and more time spent on the investigation itself.
According to a 2017 Net Promoter Score Benchmark study conducted by the Temkin Group, the average NPS for insurance carriers was 33 with scores ranging from 10 to 66.
The report also highlighted that "On average, customer experience leaders enjoy an NPS over 18 points higher than customer experience laggards."
Our approach to helping insurers improve their NPS is by developing more comprehensive and actionable insights while reducing analysis time by nearly 80%.
According to a recent survey, the average NPS for P&C insurers was 33 with scores ranging from 10 to 66.
Use household analytics to understand the portfolio dynamics at a household level, instead of just at the individual level.
Our customer testimonial list is growing
"PULSE helped us improve our Net Promoter Score by 40 points thus leading to an improved overall Customer Experience."
Head Customer Service
“Aureus has been paramount in providing us with an instant solution to our predictive model using artificial intelligence/ machine learning on mortality experience. In-depth analysis from Aureus has not only helped us in proactively identifying fraudulent cases but also led to a large sum of cost avoidance in terms of claims payout. Aureus is surely the one to look out for when it comes to predictive modeling and Analytics”
Head - Fraud Prevention Unit
"Aureus' platform helps us in creating an important analytics asset - "HouseHold Id" which benefited us in our persistency management, surrender retention & fraud control programs. It also played a key role in helping us develop lapse block propensity models."
Senior Vice President - Online Sales & Digital Strategy
“Sentiment Analytics” is an Aureus statistical/distribution model which helped us to understand the true sentiment of any customer which further helped us to deliver the output of predictive analytics models.
Aureus has given us unambiguous insights into the customer's history and sentiment which led to an increase in overall customer experience and also helped us in improving the Persistency / Retention.
They provide us the data points, with projections, as and when we need it. We have drifted beyond simple management information generation and moved into the depths of business intelligence and analytics.
Vice President - Head Renewals at DHFL Pramerica Life Insurance
"Aureus Analytics' analytics platform has been deployed (by Tata AIA Life Insurance) for our risk assessment program. The Machine Learning platform is deployed for a variety of use cases on a real-time basis.
These have helped Tata AIA take decisions and prioritize further course of action. This has helped us to manage & mitigate risk proactively & efficiently."
Head - Risk Control Unit (Operations)
"Aureus Analytics has been a true partner in achieving our retention goals. Their solutions have a direct and meaningful impact on the business and helped us increase revenue by $10M just by improving retention by 1% point."
Chief Operating Officer
"We have seen about a 20% improvement in our NPS with the introduction of PULSE into our ecosystem. PULSE not only gives us more time to analyze customer feedback and implement close looping actionable quickly, it also helps in an easier dissection of the data to seek out finer customer sentiments."
Head Customer Services
"The claim fraud prediction the Aureus solution provides us allows our investigators to focus on top 10% of early claims for investigation and is able to identify 80% of all fraud cases."