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Apache Kafka in Crypto and Finserv for Cybersecurity and Fraud Detection

The insane growth of the crypto and fintech market brings many unknown risks and successful cyberattacks to steal money and crypto coins. This post explores how data streaming with the Apache Kafka ecosystem enables real-time situational awareness and threat intelligence to detect and prevent hacks, money loss, and data breaches. Enterprises stay compliant with the law and keep customers happy in any innovative Fintech or Crypto application.

The Insane Growth of Crypto and Fintech Markets

The crypto and fintech markets are growing like crazy. Not every new crypto coin or blockchain is successful. Only a few fintech like Robinhood in the US or Trade Republic in Europe are successful. In the last months, the crypto market has been a bear market (writing this in April 2022).

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Leveraging Change Data Capture for Fraud Detection using Arcion Cloud

During the height of the business intelligence (BI) craze earlier in my career, I worked with an internal reporting team to expose data for extract, transform, and load (ETL) processes that leveraged data structures inspired by Ralph Kimball. It was a new and exciting time in my life to understand how to optimize data for reporting and analysis. Honestly, the schema looked upside down to me, based on my experience with transaction-driven designs.

In the end, there were many moving parts and even some dependencies for the existence of a flat file to make sure everything worked properly. The reports ran quickly, but one key factor always bothered me: I was always looking at yesterday’s data.

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AI Is More Than Robots: Top Applications of Artificial Intelligence in Insurance

The insurance industry is quite prone to uncertainties as it’s highly dependent on global trends, ever-changing rules and regulations, and dynamic demographics of customers. Leading businesses are leveraging artificial intelligence in insurance for redefining the playing field. AI has undergone a massive transformation in the past few years, to the point that insurers can now capitalize on their AI investments with new applications. Let’s have a look at the market size of artificial intelligence in insurance and its prominent use cases in the industry. 

The Global Market

The market size of artificial intelligence in insurance is expected to reach the value of USD 6.92 billion by the year 2028.  It is estimated to grow at a CAGR of 24.05% in the forecast period of 2021 to 2028. 

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A Secure Online Payment for Cloud Marketing

Introduction

With modern technology and innovations, businesses' have no limits. Even a small shop can go global using an eCommerce platform and third-party payment solutions. The biggest challenge for online businesses is accepting payments. Transferring money requires a FinTech solution, combining multiple solutions into a single website payment processing platform. 

eCommerce payment processing is a modern necessity now. So, when the customers get the facility to buy something with a touch of a finger sitting at home, the business gets tremendous benefits. The transaction volume of the online transaction has multiplied 2000 times in the last couple of years. Volume-wise it has increased more than 41 times. This highlights the growing penetration of online payment across the globe. 

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Do Graph Databases Scale?

Graph Databases are a great solution for many modern use cases: Fraud Detection, Knowledge Graphs, Asset Management, Recommendation Engines, IoT, Permission Management … you name it. 

All such projects benefit from a database technology capable of analyzing highly connected data points and their relations fast – Graph databases are designed for these tasks.

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Payments Architecture – Fraud Detection Example

Cloud technology is changing the way payment services are architectured. In this series we will be presenting insight from our customers on adopting open source and cloud technology to modernize their payment service.So far we've presented research-based architectural blueprints of omnichannel customer experience, integrating with SaaS applications, and cloud-native development solutions.  

In the previous article in this series we walked through the anti-money laundering physical architecture. 

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How Graph Analytics Can Transform Your Business

Introduction

Your business is operating in an ever more connected world where the understanding of complex relationships and interdependencies between different data points is crucial to many decision-making processes. This is the main reason why graph databases have gained a lot of interest in the past few years and have become that fastest-growing database category. They offer powerful data modeling and analysis capabilities your business can use to easily model real-world complex systems and answer challenging questions previously hard to address.

What Is a Graph Database?

You might not be aware of it, but many of the services you use on a daily basis are powered by a graph database. Such examples include Google’s search engine, Linkedin’s connection recommendations, UberEats food recommendations and Gmail’s autocomplete feature. Simply put, a graph database is a data management system specifically engineered and optimized to store and analyze complex networks of connected data where relationships are equally important to individual data points. As a result, they offer a highly efficient, flexible, and overall elegant way to discover connections and patterns within your data that are otherwise very hard to see.

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How SMC Allows You to Perform Advanced Data Collaboration Without Exposing Your Data

Data collaboration is the process of combining datasets together to generate new value from data-driven insights. The datasets being combined can come from different organizations, or they can come from data silos internal to an organization.

A number of use cases are possible through data collaboration: fraud detection, advances in healthcare research, real-world data, cross-selling, churn analysis, etc. However, there are significant blockers in realizing the potential benefits of data collaboration. Some of these blockers are so severe that they can stymie potentially valuable collaborations. The blockers originate from a host of areas — fear of loss of IP (intellectual property), privacy regulations, data residency restrictions, and reputational risk (just to name a few).

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Guidelines to Employ Machine Learning Algorithms for Fighting Fraud

Fraud Prevention

Fraud Prevention isn’t everyone’s cup of tea. By the time financial institutions catch up with the latest criminal tactic, fraudsters come up with a new one to take its place. Because of this obligation to constantly upgrade against scammers, it is always an ongoing challenge for financial institutes to stay neck and neck with criminals. 

At the same time, the finance sector is spending considerable budget, time, and effort to develop or adopt more advanced technologies for fraud prevention. However, one thing they may be lacking is the technology that could adapt and change as hastily as fraud tactics.