Profound changes are taking place in the insurance industry both in personal and commercial lines of business, so how do you remain relevant with all this disruption? If you are like many insurance companies, you may be facing flat market growth today, falling customer retention rates, and the need to reduce operational costs and improve profitability.
We get it.
At the same time, your company has to offer products that help you stand out from the crowd. Your customers demand a more personalized experience, even customized offerings to meet their distinct needs. Yet data proliferation makes your job harder and harder.
We transform your analytics
Saama's Insurance Analytics combine significant experience in modern data architecture design and construction with advanced insurance analytics applications. We specialize in solving business problems like churn, fraud, underwriting profitability using data science. Our insurance customers achieve a competitive edge after gaining deeper levels of real-time business and operational insights, greater market speed, and customer intimacy.
Predictive Claims Analytics
Claims adjustment expenses make a significant part of the overall expense for an insurance carrier, and they are growing. Approximately 95% of all Insurance claims require extensive research and management by adjusters to settle the claim (Operating expenses in Insurance industry amount to as much as 30%+ of total revenue). Insurance carriers today don’t have an effective mechanism to adapt to the changing nature of claims because of changing consumer behavior and demographics, resulting in inefficiencies in segmenting claims.
Claims — Segment, Predict and Optimize
Our “Predictive Claims Analytics” use a machine learning based approach to more precisely segment claims using structured and unstructured data, resulting in reduction of the resources expended on processing the claims so that claims can be settled quickly and effectively (resulting in optimal expense outlay). Our solutions offer a deep learning based approach for segmenting claims, enabling insurers to settle claims faster and with less overheads.
Predictive Customer Intelligence
Direct to consumer is the most game changing trend we see in insurance industry today, necessitating the need for a deeper understanding of the customer behavior in order to provide personalized high quality service directly without the need of an intermediary (agency). Systems and process in insurance companies have been designed with the agency model in mind. Typically, there are several ‘systems of record’ with inconsistent customer data leading to audit/compliance issues, poor marketing/segmentation, and the inability to address the entire customer lifecycle.
Our “Predictive Customer Intelligence” brings all the silos in a conformed data platform including data from quote, policy, claims, billing, payment systems and from interactions via the website from desktop and mobile devices, IVR, chat, email and even takes into account survey data such as Net Promoter Scores (NPS), Customer Satisfaction, and Brand Tracker.
This single view then enables our machine learning based solutions to provide a deeper understanding of your customer behavior and buying patterns, leading to deeper market penetration, better retention, customer intimacy — boosting revenue growth, and resulting in higher levels of privacy, security, and regulatory compliance; thereby, accelerating the journey towards direct to consumer for insurance carriers.
Information Security Analytics
Insurance industry continues to use traditional techniques to deal with Information Security and threat detection, even though the threats are growing more complex, opaque and dangerous day by day. Most companies narrow their approaches towards examining network data flows, destination data, bytes of data transferred to detect the threats. These approaches overlook critical sources that cause the Information Security attacks.
We approach these threats using Saama's Information Security Analytics (SISA for Insurance). Saama uses a data centric approach that allows to implement and enhance multiple threat detection models using Deep Learning and Artificial Intelligence. Saama's Information Security Analytics is built on a data centric approach by ensuring the data acquisition and centralization of major security parameters including Entry point, Endpoint, Identity, Access, Vulnerability, Security Risks, Events, Cyber Resilience, etc. Our offering enables a comprehensive and dynamic view of the organization's network and related data by strengthening the basic health of Cyber Security.
Information Security Analytics integrates the Enterprise Data and marries to relevant logs (internal and external) with data acquired from third-party sources, social media, Information Security Governance and Regulatory board. Implementing Information Security Analytics facilitates continuous monitoring and enables making insights out of data through Deep learning techniques, thus enabling prediction of Information Security events, Threats and eventually Predict and provide insights to prevent them from occurring.
Claims adjustment expenses make a significant part of the overall expense of an insurance carries.
There’s a need for an efficient mechanism that adapts to changing nature of claims, evolving consumer behavior and demographics. Using a newer approach, if claims can be more precisely segmented with the help of additional data and techniques, it is possible to reduce the resources and cost expended on processing the claims.
In this white paper, we present a deep learning based approach for fast-tracking insurance claims by segmenting them, enabling insurers to reach settlements with fewer overheads.
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