Case Study: Optimize Claims Management Process and Actions on Fraudulent claims

Birlasoft | Databricks | Apurva Patil

Background:

An insurance company is facing important challenges with its claims processing. Handling claims efficiently is difficult, and the company struggles to detect fraudulent claims, leading to financial losses. The lack of proper IT tools and processes has caused increased operational delays, affecting productivity. This not only impacts the company's profits but also frustrates customers who experience delays in getting their claims resolved. To improve, the company needs to focus on adopting the right technology and streamlining its processes. By doing this, it can manage claims better and protect itself against fraud. A clear strategy will help the company overcome these issues and become stronger in the competitive insurance market.

What are the business problems?

• Increased Claims Processing Time: Slow claims handling leads to customer dissatisfaction.
• Difficulty in identifying fraudulent claims results in financial losses.
• Inefficient claims management processes lead to increased operational expenses.
• Customer Retention Issues.

Solution Scope:

The need is for a solution that can predict potential fraudulent claims, optimize claims processing times, and enhance risk assessment for better underwriting decisions.

Implementation coverage:

1. Gather data from claims databases, customer interactions, historical claims data, and external sources.

2. Data processing to clean and transform the raw data for predictive model building.

3. Build models to predict fraudulent claims and optimize claims processing.

4. Implement a system to monitor claims in real-time for anomalies.

5. Improve underwriting processes by analyzing historical data trends.

How Databricks tools addresses these business problems?

Data integration:

Databricks simplify ingestion of real-time claims data and large historical datasets.

Using Delta Lake, all types of data (structured, semi-structured, unstructured) are seamlessly stored and managed.

Processes real-time data from claims submissions.

Data Preprocessing:

Databricks Notebooks Enable data to build and test pipelines for cleaning and preparing claims data.

Databricks notebooks allow engineers to quickly build and test data pipelines for cleaning and preparing data from multiple sources.

Identify patterns indicating fraud (e.g., unusual claim amounts, frequent claims from the same individual) and image classification models.

Fraud Detection Models:

Machine Learning (ML) in Databricks is used to Build and train models for fraud detection and claims processing optimization.

Databricks MLflow helped to provide seamless integration for experiment tracking, model versioning, and model deployment.

Real-Time Monitoring & Alerts:

Continuously monitor claims data, detecting anomalies and triggering alerts for potential fraud.

Predictive models were deployed to identify and store data about fraudulent claim patterns and transactions for future analytics consumption.

Scalability and Collaboration:

Databricks scales efficiently with growing claims data, providing a collaborative platform for data scientists and analysts.

Delta Sharing options enabled for sharing insights to business stakeholders, underwriters, and claims adjusters.

Business Benefits: Reduced Downtime: Reduced Claims Processing Time: Streamlined processing leads to quicker resolution of claims. Claims processing time reduced by 20%.

Lower Maintenance Costs: Maintenance becomes more efficient, shifting to predictive analytics reduces the need for extensive manual reviews, decreasing costs. Maintenance costs reduced by 15%.

Enhanced Fraud Detection: Early identification of fraudulent claims resulted in financial savings from fraudulent claim transactions and increased in IT confidence score in the organization.

Improved Customer Satisfaction: Faster claims processing and resolution improve customer retention rates.

Better Decision-Making: Real-time analytics provide quick insights for business and management to take the right decisions at the right time.

Databricks features leveraged:

Delta Lake, Databricks Notebooks, MLflow, Databricks Runtime for ML, Delta Sharing, Databricks SQL and Power BI visualizations

Deployed Architecture