Types of Graph Analysis

 

Graph Analysis has become a groundbreaking way for organizations to look at their data and understand the relationships between them. For two years running, Gartner selected graphs as one of their top analytics and data trends because of the significant potential for value creation. 

Graphs capture relationships and connections between entities. The relationships and connections between the entities are used in data analysis. Knowing how the data is connected, and building a graph to understand the relationships are becoming increasingly important because they make it easier to explore those connections and made new insights.  For example, understanding how a person’s buying pattern is influenced by all the entities the person is connected with. 

Centrality analysis: Estimates how important a node is for the connectivity of the network. It helps to estimate the most influential people in a social network or most frequently accessed web pages by using the PageRank algorithm.

Community detection: Distance and density of relationships can be used to find groups of people interacting frequently with each other in a social network. Community analytics also deals with the detection and behaviour patterns of communities.

Connectivity analysis: Determine how strongly or weakly connected two nodes are.

Path analysis: Examines the relationships between nodes. Mostly used in shortest distance problems.

Fintech Use Case for Graph Analytics

In my previous blog, I had written about high-level use cases for Graph Analytics. In today’s blog, lets' dive in deeper and take a look at how Fintech companies can use graph analytics to allocate credit to customers & manage risks.

Today, banks and other Fintech companies have access to tonnes of information – but their current databases and IT solutions do not have the ability to make the best use of it. Customer information such as KYC data, and other demographic data are often storied in traditional RDBMS database, while transactional data is stored in a separate database, customer interactions on web/mobile apps, customer interactions data are stored in Big Data HDFS stores, while the data from Social network or other network data about customers are often not even used. 

This is where graph databases such as Neo4J or Oracle Autonomous database etc come into play.  

A graph database can connect the dots between different sources of information and one can build a really cool, intelligent AI solution to make predictions on future purchases, credit needs, and risks. Prediction data can then be validated with actual transactional data to iterate and build better models. 

Graph databases are essentially built for high scalability and performance. There are several open-source algorithms and libraries that can detect components and make connections within the data, you can evaluate these connections and start making predictions, which over time will only get better. 

BFSI Use cases for Graph Analytics

 

Graph analytics is used to analyze relations among entities such as customers, products, operations, and devices. Businesses run on these relationships between customers, customers to products, how/where/when customer’s use products, and how business operations affect the relationships. In a nutshell, it’s like analyzing social networks and financial companies can gain immensely by using Graph Analytics.

Let’s see the four biggest use case of Graph Analytics in the world of finance.

 

1. Stay Agile in Risk & Compliance
   Financial services firms today face increased regulations when it comes to risk and compliance reporting. Rather than update data manually across silos, today's leading financial organizations use Neo4j to unite data silos into a federated metadata model, enabling them to trace and analyze their data across the enterprise as well as update their models in real-time.
   
   
2. Fraud Protection
   Dirty money is passed around to blend it with legitimate funds and then turned into hard assets. Detect circular money transfers to prevent money laundering via money mules. Graph Analytics discovers the network of individuals or common patterns of transfers in real-time to prevent common frauds – to detect illegal ATM transactions. Data like IP addresses, cards used, branch locations, the timing of transfers can be instantly tied to individuals to prevent fraudulent transactions.
   
   
3. Leverage data across teams
   Data is the lifeblood of finance. Companies strive to actively collect, store and use data. At the same time, financial companies are governed by laws, regulations, and standards around data. The burden of being compliant and ensuring data privacy has become ever more complex and expensive.
   Graph Analytics allows tracking data lineage through the data lifecycle. Data can be tracked and navigated, vertex by vertex, by following the edges. With graph analytics, it is possible to follow the path and find where the information originated, where it was copied, and where it was utilized. This makes it easier to remain compliant and use data for its full value.
   
   
4. Capture 360-degree view of customers
   Marketing is all about understanding relationships of their customers and their products. Knowing the relationships between customers, customers’ transactions, and products will build a 360-degree view of customers – which can be used for better marketing and more effectively provide customers with what they want.