Saxon Global

  According to  McKinsey’s prediction , generative AI could contribute somewhere between USD 2.6 trillion and 4.4 trillion annually to the global economy by 2040. The potential impact is particularly significant in banking, retail, consumer-packaged goods, pharmaceuticals, and medical products. That apart, McKinsey’s predictions also indicate that many key business functions, such as software engineering, customer operations, research and development, marketing, and sales, will account for  75% of the total annual value  that generative AI can bring. Therefore, generative AI is a versatile tool across sectors and business functions, revolutionizing business. Hence, enterprises aiming to ride the wave of this technology revolution should invest in generative AI and prepare their workforce for the imminent transformation. Transforming business operations So, how is generative AI transforming business operations? It is doing so by truly bringing about unparalleled innovation and creativit

  Data is growing exponentially, and so are the demands of the modern market. More and more enterprises realize that their present infrastructure is inadequate to fulfil the needs of the modern market. Migrating to modern systems, from on-premises set-ups to cloud-based systems, has become the most preferred choice. Cloud systems such as Azure provide a plethora of benefits, such as enhanced flexibility, scalability, cost optimization, streamlined process automation, strong data security, seamless remote collaboration, and much more. The latest  Gartner’s report  further strengthens this notion, as it says that 75% of organizations will embrace a digital transformation approach centered on the cloud as the foundational platform by 2026. When it comes to database migration to Azure, you may know that it is not a straightforward, simple journey. There are several challenges and complexities that you have to cut across to migrate to Azure. You may need to make several strategic decisions

  Remember the 2008 recession? When the   housing bubble burst , it shook the American economy, and the shock waves reverberated in every other economy in the world. 8.7 million Americans lost their jobs, and $19 trillion wealth of U.S. households vaporized. The reasons – financial firms took high risk by mortgaging to people who were a bad credit risk. The consequences of this poor risk assessment were catastrophic. The Great Recession couldn’t emphasize more the importance of risk management and fraud detection in the finance sector. In modern times, the finance sector witnesses new risks and fraud methods. In this blog, let us explore how one can use data analytics in finance. Risk management with data analytics in Finance Incorporating data analytics at each phase of risk management can enhance the processes and mitigate risks. Typically, risk management involves five stages, including risk identification, assessment and prioritization, response and mitigation, monitoring, and repo