Data Analytics in Insurance
Data Analytics processes past and current data using traditional and modern techniques and technologies to make accurate predictions, manage risks and offer ground breaking insights into the industry.
How to get started with Insurance Analytics?
Insurance Analytics goes beyond the typical use of data to optimize processes and manage risks. When done right, Analytics can provide competitive edge to an organization from customer acquisition, underwriting, pricing to fraud detection and risk mitigation.
There are seven steps to get started -
- Problem Statement
- Solution Overview
- Data Collection & Cleaning
- Data Modeling & Analysis
- Reporting & Dashboard
- Conclusions & Evaluations
For Analysis, there are typical techniques that can be applied based on the problem statement and desired outcome. In order to understand a typical approach towards solving an analytical problem, we need to define the problem and the goal that the team is trying to achieve with the solution which provides clarity in choosing techniques & models.
Defining the problem statement
Lets understand the problem a typical insurance firm might face. A travel insurance firm works on providing insurance to travelers & tourists commuting from country to country.
The primary coverage should include any medical related incident, theft and issues related to airlines and local travels.
The users have a self serving platform that guides them through the selection of best possible policy which relies on scoring system as per user preferences and regulations of their residing country as well as destination country. For this process to be fully automated, the product should have air tight underwriting covering the insurance firm from any potential fraudulent claims.
There is a typical problem in types of claims processed by the firm.
The data in the organization repeatedly points to claims with large sums of settlements not aligned with the policy type. It also shows that some typical claims are processed almost instantly while some take its due course resulting in delays and frustrated customers complaining to the customer service team.
Here, we are faced with two problems -
- The misaligned claims with large sums
- The operational delay and overload
Solution Overview
Depending on the problem statement above, there is need to establish a robust data engineering pipeline for automated data insights to improve the decision making regarding the optimization of processes and product features in general.
In order to extract valuable insights from the data, it is imperative to understand the data and it can be done with Exploratory Analysis.
Exploratory Data Analysis allows us to understand the nuances of the data we have on our hands and also decide the best way to move ahead with different data analytics & modeling techniques.
Here, we are trying to find outliers in claims and identify any unusual operation overloads.
- Anomaly Detection - There are multiple ways to detect anomaly in the dataset. Statistical outliers, Isolation Forests, etc. where the data points that do not fit into with any dataset can be easily detected and analysed.
- Network Analysis - There are quite a few reasons to keep track of network data to analyze any clustering of activity happening from similar set of origins. Here we try to detect any relationships between these origin points (IP addresses) to detect any attack from fraud rings.
To implement the solution, there are multiple ways to approach it and the main considerations for any data team is to understand the frequency of reporting and level of monitoring needed for the system.
Different Ways of Implementing Analytics System & Models
Data Analytics systems work on principle of achieving the ultimate objective of providing insights for decision making to the stakeholders.
The question arises whether the information needed has to be in real time or in a diagnostic manner as reports & dashboards.
The data team will make the necessary considerations based on these requirements to develop -
- A Real Time Analytics System
- A Batch Reporting and Monitoring System
Here, in our example we are looking to identify anomalies but also monitor any network issues that may cause operational delays. We have a very obvious need for a real time fraud detection system.
The data engineering team will have to consider integrating a fraud detection system in the transaction pipelines so the stakeholders are notified in real time.
A Real Time Analytics system requires for the transactions to be transmitted to the analytics engine in real time. The streaming data pipeline should be able to handle in-memory processing even when the data load increases.
To handle scalability, the tools in use should be able to scale and handle the implementation of Machine Learning for analytics.
Tools and Technology
With the advent of AI, there is an obvious case for organizations and Data Teams to adopt the use of AI for generic as well as specific needs of the product and organization.
To gain insights from the insurance data, there are typical choices that need to be made about tools and technology. For a Data Engineering team to work without any friction, they need to narrow down the scope and type of data collection and analysis that needs to be done.
Tools like -
- DuckDB
- Estuary
- Snowflake
- Airflow
- Spark
- Python
- Jupyter
- Pandas
- Polars
In our example, any of these tools should be able to help us implementa the system.
Apache Spark provides real time analytics capability while integrating with any cloud based providers like Amazon S3, Snowflake, Azure etc. for data storage and training ML models for predictive analysis.
Benefits of Insurance Analytics
For an Insurance organization, the criticality of the matter is the need of their users is timely and the margin for error is almost zero. For any product to work well, it needs to be on the mark with almost zero downtime.
The use of data analytics allows the product team and stakeholders to make informed decisions backed by data.
It not only allows them to put better quality systems, avoid risk and implement preventative measures to avoid inconvenience to the users.
Some of the obvious benefits are listed below -
- Better Lead Generation
- Fraud Detection
- Risk Prediction
- Tailored Plans & Pricing for Users
- Operational Efficiency
- Automated Underwriting
In our example, we stressed about the specificity of objective and goal when implementing data analytics systems. It can be done by defining Key Performance Indicators for the system that allow for implementation of quantifiable measures to track the system.
KPIs
With advanced analytics system, the team can define and measure KPIs that closely resemble their objectives. These KPIs can be integrated in the data model and can be monitored and tracked in dashboards provided to the stakeholders.
Some of the prominent KPIs implemented in a insurance fraud detection systems are -
- Claims Settlement ratio
- Claims processing time
- Policy Renewal Rate
- Underwriting Accuracy
- Average Cost of Policy
- New Customer Acquisition Cost
- Net Promoter Score
As we talked about stakeholders and creating a reporting system to extensively help decision making for them, we need to look at the list of stakeholders that a data team may be working to help.
Stakeholders
The data team works with stakeholders at different levels but the ultimately it is the users that need to be at the consideration of every decision.
The biggest stakeholder for the data team is the leadership team as they will put forth the requirements for the analytics system.
- CEO & Leadership team
- Product Managers
- Data Stewards
- Data Governance Manager
- Chief Data Officer
During the course of designing and implementing the analytics system, the data team will work with multiple stakeholders for data collection, data preprocessing, modeling, designing and development.
Conclusion
The benefit of leaning heavy on Data Analytics for Insurance Organizations comes with short term insights to long term refined strategies backed by data. It makes the leadership efficient and their decision making more concrete for long term effects.
Designing an Analytics system requires considerations about stakeholders and their objectives, quality and quantity of data, governance and infrastructure and finally the expertise and cost of scalability.
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