Using Graph Databases to Uncover Fraud Networks, Personalize Customer Experiences, and Enhance Data Insights

Introduction

• In data analysis and management, graph databases that uncover fraud networks have emerged as a powerful tool for finding complex patterns, personalizing customer experiences, and deriving deep data insights. Unlike traditional databases that primarily deal with structured data in a tabular form, graph databases excel in handling highly connected data. This capability makes them invaluable in the insurance industry and beyond, where relationships between entities often hold the key to unlocking valuable insights.

A Detailed Use Case: Detecting Insurance Fraud

• For the described scenario, let’s construct a sample graph database query using a hypothetical graph query language similar to Cypher, which is used by Neo4j, one of the most popular graph databases. The scenario involves tracing potentially fraudulent insurance claims indirectly connected through shared device IDs or beneficiary accounts across different parts of the city. The query aims to find clusters of claims that may indicate fraudulent activities.

Hypothetical Graph Structure:

• Nodes:Claims, Devices, BeneficiaryAccounts, Locations
• Relationships:
• Claims SUBMITTED_FROM Location
• Claims MADE_USING Device
• Claims BENEFITS BeneficiaryAccount

Query Objective:

• Identify clusters of insurance claims that share the same device ID or beneficiary account but originate from diverse locations. These clusters may suggest a pattern of fraudulent activity.

Sample Query:

MATCH (c1:Claim)-[:MADE_USING]->(d:Device)<-[:MADE_USING]-(c2:Claim),
      (c1)-[:BENEFITS]->(b:BeneficiaryAccount)<-[:BENEFITS]-(c2),
      (c1)-[:SUBMITTED_FROM]->(l1:Location),
      (c2)-[:SUBMITTED_FROM]->(l2:Location)
WHERE c1 <> c2 AND l1 <> l2
WITH c1, c2, collect(d) AS SharedDevices, collect(b) AS SharedBeneficiaryAccounts, collect(l1) AS Locations1, collect(l2) AS Locations2
RETURN c1, c2, SharedDevices, SharedBeneficiaryAccounts, Locations1, Locations2
ORDER BY size(SharedDevices) DESC, size(SharedBeneficiaryAccounts) DESC
LIMIT 10;

Query Explanation:

• MATCH: Looks for patterns where two different claims (c1 and c2) are made using the same device (d) and benefit the same beneficiary account (b). Additionally, these claims are submitted from different locations (l1 and l2).
• WHERE: Ensures that the two claims being compared are not the same (c1 <> c2) and originate from different locations (l1 <> l2).
• WITH: Aggregates shared devices, beneficiary accounts, and locations for each pair of claims, allowing us to analyze the connections between them.
• RETURN: Returns the claims, their shared devices, beneficiary accounts, and the locations from which they were submitted.
• ORDER BY: Orders the results by the number of shared devices and beneficiary accounts, prioritizing clusters with more shared elements which could indicate stronger links for potential fraud.
• LIMIT: Limits the results to the top 10 pairs of claims with the most significant connections, making it easier for analysts to investigate.
• This query is designed to help insurance analysts quickly identify and investigate suspicious claim clusters that might indicate fraudulent activities, by leveraging the connected data nature of graph databases.

Scenario:

• In the insurance industry, there are lots of leads coming from different sources. There are multiple followups, re-marketing campaigns from different channels and engagement is taking place. Now, with graphdb we can used to analyse cross channel communications, retargeting and lead scoring to effectively predict how much to spend on user and efforts to be made.

A Detailed Use Case: Finding a pattern in customer engagement

• An insurance company uses various channels, such as email, social media, phone calls, and online ads, for lead generation and retargeting campaigns. Each lead interacts with these channels differently, displaying a unique pattern of engagement that can be tracked and analyzed. The company aims to maximize its marketing ROI by understanding these patterns, identifying the most promising leads, and customizing its follow-up strategies accordingly.

Graph Database Structure:

• Nodes: Leads, Channels (Email, SocialMedia, Phone, OnlineAds), Campaigns, Interactions
• Relationships:
• Leads ENGAGED_WITH Channels
• Leads PARTICIPATED_IN Campaigns
• Channels UTILIZED_IN Campaigns
• Leads HAD Interactions

Query Objective:

• Identify leads with high engagement scores across multiple channels and predict optimal marketing spend and effort for each lead based on their interaction history and engagement levels.

Sample Query:

Assuming a query language similar to Cypher:

MATCH (l:Lead)-[eng:ENGAGED_WITH]->(c:Channel),
      (l)-[part:PARTICIPATED_IN]->(camp:Campaign),
      (l)-[had:HAD]->(i:Interaction)
WITH l, collect(DISTINCT c) AS ChannelsEngaged, collect(DISTINCT camp) AS CampaignsParticipated, count(i) AS TotalInteractions
WHERE size(ChannelsEngaged) > 2 AND TotalInteractions > 5
MATCH (l)-[eng:ENGAGED_WITH]->(c:Channel)
WITH l, ChannelsEngaged, CampaignsParticipated, TotalInteractions, sum(eng.score) AS EngagementScore
ORDER BY EngagementScore DESC
RETURN l.id AS LeadID, ChannelsEngaged, CampaignsParticipated, TotalInteractions, EngagementScore
LIMIT 10;

Query Explanation:

• MATCH: Finds leads that have engaged with various channels, participated in campaigns and had interactions.
• WITH: Aggregates the channels each lead engaged with, the campaigns they participated in, and the total number of interactions.
• WHERE: Filters for leads engaging with more than two channels and having more than five interactions to focus on highly active leads.
• MATCH & WITH (again): Matches the leads to their engagement again to calculate the engagement score by summing the scores from engagements with different channels.
• ORDER BY: Sorts the leads based on their engagement score to prioritize those with higher engagement.
• RETURN: Returns the lead ID, the channels they engaged with, the campaigns they participated in, their total number of interactions, and their overall engagement score.
• LIMIT: Limits the results to the top 10 leads to focus on those with the highest potential ROI for targeted marketing efforts.
• This query helps the insurance company identify and prioritize leads most receptive to their marketing efforts across multiple channels. By analyzing engagement patterns and scoring leads based on their interactions, the company can optimize its marketing spend and customize follow-up strategies to increase conversion rates effectively.

To Read Blog Visit :- https://insurnest.com/blog/using-graph-database-to-uncover-fraud-networks