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.

 

How Banks can benefit from Blockchain Analytics?

 

 

Blockchain is a digital and decentralized public ledger with a system that records transactions across several computers linked to a peer-to-peer network. It was originally developed for cryptocurrency assets like Bitcoin, Dogecoin, Ethereum, etc., In recent years there are several new use cases have emerged in financial services. (See:  Blockchain for Secure Healthcare Records, How Banks & Financial Institutions can use Blockchain Technology, Blockchain use cases) 

As blockchain’s use cases go beyond cryptocurrency, including for government applications, healthcare, identity management, art, and IPR, the database of all blockchain transactions grow even more bigger, richer, and more valuable for banks – if they can use this data via data analytics and use these insights to build better services.  The benefit of blockchain is its inherent transparency. The blockchain’s decentralized, open network allows banks to collect data from blockchain transactions.  

The Rise of Data Analytics

Aside from all the aforementioned areas, blockchain also huge potential in analytics. Modern businesses have been benefiting from data analytics for several years now.  Currently, the big problem with any data analytics is getting quality data from different sources and correlating them. There is the issue of whether there is enough of the right data.   

This is where blockchain technology helps. Data recorded in a blockchain is irrefutable and can be easily cross verified from any node in the network. Having access to this large network that provides high quality data in a vast number of datasets is invaluable. 

A good potential application will be blockchain analytics – to understand customers of cryptocurrency customers & traders. Bank’s asset & wealth management business and customer banking’s marketing organization can use these valuable analytics for future marketing campaigns and for managing cryptocurrency as an asset class in wealth management. This system can be used to forecast price movements for cryptocurrencies. 

Today there are more than 100 digital assets including Bitcoin, Ethereum, ERC-20 tokens, and other crypto coins, representing over $200 billion worth of transactions per month. 

Other use cases include risk analysis on crypto transactions: uncovering activities related to money laundering, terrorist fundraising, fraud, and other financial crimes. Blockchain analytics can de-anonymize funds flow by actively collecting millions of data points every week, and then implementing machine learning to its huge data pool to track flows to legitimate entities and also criminal activities.

 

How Banks & Financial Institutions can use Blockchain Technology

Apart from cryptocurrencies, there are several other important use cases for Blockchain technologies in the banking & financial sectors.

NFT 

Non-fungible tokens (NFTs) are new digital assets.  In a nutshell, NFT is a crypto block – which encapsulates digital art or record. The digital art/record is tokenized – just like recording a payment transaction in a blockchain, it becomes a certified true copy – whose authenticity & Ownership can be verified by any node in the crypto-chain network. This token (also called NFT) can then be traded on the blockchain network just like any cryptocurrency.

Today, the world is experimenting with NFT for digital art or digital records. For example, Twitter CEO Jack Dorsey sold his first-ever tweet as an NFT for more than $2.9 million!

Ownership & Transfer of Financial Instruments: 

One of the biggest use cases of NFT in the financial world is recording ownership of financial records: Bond/stock certificates, insurance policies, etc can be tokenized to record the ownership of financial assets, and then these NFTs can be traded on the crypto network.  

Payments, Remittance & Reconciliation

Unlike cryptocurrencies, banks can create & issue crypto tokens that are tied to a fixed value – which can then be transferred over the network for instantaneous payments and remittances without the need for a central bank’s approval. JPCoin is a good example of this.

Servicing of Instruments

Once the ownership of financial assets is tokenized, Payments as per financial contracts such as Bond coupons or dividends can be made programmatically to the current owner of the Financial instrument accurately.   

Storing KYC information & Anti-Money Laundering Registers

KYC information is nonfungible data that can be tokenized so that these records cannot be altered by hackers, and also be used for rapid ID identification across the network for rapid transactions. As the pace of transactions increases, the current data warehousing systems impose certain limitations and the use of NFT for KCY AND AML Registers can speed up global financial transactions.

Regulatory Reporting

Regulatory Reporting should be a nonfungible report as it has major implications. Countries and participating Banks can use the crypto network to get, store and use all regulatory reporting data. These reports can then be shared securely in the crypto database with multiple regulators and other governing bodies.