Predictive Claims Intelligence
Predict claims payout, Gauge subrogation potential, Anticipate claims fraud and Foretell reserving. All of this and more, thanks to the power of Predictive Claims Intelligence. Using multiple deep-learning based predictive models, our solution finds patterns in structured and unstructured claims data and provides propensity scores for key areas of the claims lifecycle making it easier to analyze and process claims.
Our solution overcomes the difficulty and challenges that adjusters face in analyzing patterns from millions of historic claims and related data to make intelligent decisions based on repeat behaviors.
Pattern Identification in Structured and Unstructured Data
Input data integration and curation are performed on the “claims transaction data”, “adjuster notes”, “claimant notes” and most importantly “photos/images of the damage”. The curated historic data is then fed to a deep learning neural network to train, producing highly accurate predictions.
Pre-Built, Pre-Trained Neural Networks
Our Neural Networks are pre-built and pre-trained to understand patterns in insurance and claims data in order to identify key signals. Our three-pronged approach (Unsupervised Learning, Supervised Learning, and Reinforcement Learning) to training our deep learning neural networks means that our network can deal with a wide variety of complex claims scenario. With increased usage and over a period of time the network goes through self-learning cycles to eliminate “False-positives” to become increasingly intelligent in predicting propensity scores.
Minimally Invasive API-based Integration and Consumption
A key consideration of integrating such a system into the existing claims process/systems is to ensure the disruption is negligible to the existing applications and infrastructure. Using our private cloud-based approach, our systems provide an API for the on-premise claims transaction system to connect and send the claims data (for a claim), our solution then returns (output) a prediction/propensity score for that claim. This approach requires minimal disruption of your existing infrastructure or requirement for additional infrastructure. Minimally invasive API-based integration means easy and fast deployment, rapid transaction system integration and lower costs of ownership.
Predicting Subrogation potential.
More accurate and fast case reserving based on historical data patterns.
Predict Payout amounts early to mitigate risks and opportunity for early intervention.
Segment claims based on historic data to optimize assignments to appropriate adjusters.
Predict which claims are genuine and can be fast-tracked to reduce expenses.
Detect potentially fraudulent claims for early investigation and better SIU results.
Client Success Stories | Blogs | White Papers
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.