The client sought to retain an at-risk multi-million dollar portfolio. In an effort to find a solution, the client needed help with insurance claims data analysis identifying risk drivers and preventing losses. In particular the client wanted to utilize their massive amount of unstructured adjusters’ notes to bring more detail-rich information to the risk consulting team aiming to prevent loss and reduce costs, and to identify additional risk drivers to support ongoing pricing strategy.
The key challenge was the inability to process massive amount of unstructured data (i.e., adjusters’ notes), limiting opportunities to quickly generate insights.
- We performed Natural Language Processing (NLP) on their unstructured adjusters' notes data including Term Frequency-Inverse Document Frequency (TF-IDF) and topic modeling (LDA)
- We enriched the input data to the pricing models through text mining that led to increased prediction accuracy
- We developed an interactive risk analytics tool to deliver loss and cost insights
Fulcrum helped the client with enhanced coverage insights and pricing inputs through the introduction Natural Language Processing on unstructured data.
The client was seeking to apply predictive models to improve its rating structure, moving away from subjective rating. Key challenges included:
- Highly subjective rating process based on individual underwriter's experience, without data-driven guidelines
- Lack of experience and resources evaluating external data sources
Furthermore, the client wanted to explore options to incorporate and build machine learning into the rating structure
- We identified pricing key drivers through LASSO regression
- We developed a statistical model (using GLM) that was implemented for predictive ratings scoring
- We used advanced techniques (including GBM, Random Forest, etc.) to improve model performance
- We created benchmarking models with SVM and NN to ensure model performance (i.e., how close the model can perform when compared with a simulated one without restrictions)
Fulcrum’s data-driven rating structure standardized future underwriting and increased pricing transparency.
The client was seeking to transform a labor-intensive pricing process to a seamless, sustainable and transparent operation. Key challenges included:
- Lack of standardization in reporting across types of policy (e.g., auto, GL, etc.) which made it difficult to analyze account-level performance at a glance
- Manual data pulling, and copying/pasting during the data preparation created high human-error risk
- Difficulty tracking changes and updates to quotes
- We mapped out user stories to better understand various user needs and experiences
- We developed a platform-flexible front-end pricing tool that accounted for user needs (e.g., metrics, reports, etc.) utilizing real-time data ingestion
- We developed automated scripts for consistent data pulls and to reduce labor hours
- We built a job management framework to provide pricing governance and monitoring
Fulcrum’s development of a reliable and sustainable process resulted in an 80% reduction of manual labor hours, increased pricing transparency, and yeilded faster quote turnaround.
The client needed help flagging fraudulent medical insurance claims. The goal was to:
- Determine logic to flag suspicious behavior and identify providers of interest
- Leverage billing data to analyze suspicious providers
- We performed fuzzy matching to link multiple claim records to the same provider(s)
- We used geocoding to create physical distance calculations between providers and between providers and claimants
- We applied statistical methods (e.g., logistic ridge regression with sparse matrices, T-Score calculation, bootstrapping, etc.) to billing data to build propensity scoring models to rule claims as valid or invalid
- Based on our modeling results, we created the fraud flagging logic to identify suspicious behavior and providers of interest
- The team collaborated with the investigative unit, billing unit, and legal unit to create a provider fraud action plan
Fulcrum's data preparation procedures and predictive analytical models provided the quantitative basis for the provider fraud action plan.
To perform exploratory analysis on the impact of IoT devices on homeowners insurance claims, the client sought to merge claim data with IoT information (provided by a third party).
The challenge was third party and the client not being able to share data across systems for data security reasons.
- Fulcrum facilitated IoT analytics by acting as a third party to join proprietary information between the two companies
- We collected sensitive data in isolation in our highly fortified security infrastructure, and performed matching at the home address level
- Then we provided an anonymized joined data set to the client and developed a framework to repeatedly supply the data transformation between the two companies
Fulcrum quickly and securely provided the neutral data matching required to support various insight needs across the organization.
The client was seeking to streamline their multi-step and labor-heavy scoring process of a pricing model to improve efficiency. Key challenges included:
- Labor intensive monthly/quarterly updates that involved manually updating hard-coded parameters
- Because it was a multi-step scoring process, it was prone to errors and difficult to troubleshoot
- Required underwriters and actuaries to performmanual steps that were tedious and time consuming
- Model inputs and performance were not monitored for anomalies and/or population shifts
- We parameterized and centralized configurationfiles to reduce turnaround time and errors
- We built QA checks within scripts to provide quickdiagnosis for unexpected results
- Then we streamlined the feedback process and standardized the input template to minimize room for error
- Lastly, we developed a model tracking report that refreshed upon each update to track population shifts and performance changes
Fulcrum developed an automated model scoring and monitoring process that enhanced efficiency, improved decision making, and increased confidence in results.
The client was seeking a streamlined method to organize their disparate data sources into a single location to start generating business insights. Some of their challenges included:
- A massive amount of disjointed information including internal and external data
- The lack of a unified view of the customer because the source systems are managed by different siloed teams
- The internal IT team was backlogged, and analysts needed support testing the latest big data tools
The client partnered with Fulcrum to utilize our Agile Analytics Lab to experiment with big data solutions
- We created reusable data processing pipelines to allow easy data loading into Hadoop and data preparation for analysis
- After establishing the central repository, we then cubed the data using Hive and Spark for fast querying of metrics
- Output tables are stored in HBase and Impala is used for queries to gain rapid response while Tableau is utilized for dashboard reporting
Fulcrum’s lab enables a holistic view of the data to unlock insights and allows rapid testing of various open source tools. Our data experts guide the process allowing the clients to modernize their analytics team.