Orbis Financial continues to expand, achieving a growth average of 20 percent year-on-year.
Orbis Financial constantly provides its customers with online reports about their investments and the services the customer is using. These reports are crucial for customers, helping them make important business decisions or meet regulation compliance. Customers can also customize reports by asking Orbis Financial to change the way it presents data in the documents.
For example, one day a customer may want to see performance figures with two values after the decimal and then, another time, the same figures with four values after the decimal point. The main issue was the user-acceptance testing UAT environment, where the Orbis Financial IT team checks that customizations have been successful. Mani Kant Singh R, head of information technology at Orbis Financial, was looking to improve the performance of the Orbis Financial IT infrastructure and, in turn, the experience for customers.
Mani also sought to reduce costs—in particular, the expense of backing up and archiving the data from the UAT to a secondary on-premises site. On top of this, he sought to improve management efficiency and enhance the speed of software development so IT could deliver more value back to the business.
To address these needs and challenges, Mani looked to move the UAT environment and data backups to the cloud. To ensure he received the support of Orbis Financial board members, Mani needed to demonstrate that the cloud would be secure. We ran a month-long proof of concept on the AWS Cloud to make the final tweaks to the AWS infrastructure, and we completed the migration of the UAT and backup processes without any issues.
Therefore, the company must run risk modeling to show the effects on their operations depending on certain economic changes. The company conducts financial risk modeling using the Monte Carlo Method, a statistical sampling technique that approximates solutions to quantitative problems, to simulate sources of uncertainty that affect the value of their various risk portfolios.
Running on premise, these monthly batch jobs would take up to three weeks to run, tying up expensive computing resources and limiting the ability to run other jobs. The company was faced with a major data center expansion and infrastructure upgrades to accommodate the growing batch computing requirements necessary to process all the scheduled jobs as well as what was required to run adequate risk modeling scenarios.
That planning paid off, as Peixe Urbano is now Brazil's largest collective discount website. Amazon moved more than 10 billion records from Oracle to Amazon DynamoDB to reduce latency by 50 percent. The private cloud offers a dramatically different approach and cost structure, in a variety of ways. Amazon EC2. They understood that these architectural deficiencies were due an AWS skills gap, rather than gaps in the AWS services themselves. Before launching Enfuce, the five cofounders designed, built, and maintained financial products for banks and financial institutions.
Rather than investing in another data center or co-location contract, the company began exploring running batch jobs using High Performance Computing HPC on the public cloud to meet this growing demand. The company avoided the traditional provision of technology by leveraging the 2nd Watch Risk Modeling solution on AWS and capitalizing on the elasticity of the public cloud, enabling workloads to spin up thousands of cores in hours and days versus weeks and months.
The hybrid solution has increased the visibility for the business and provided data to meet the Federal Reserve regulatory requirements and make different business decisions. Additionally, 2nd Watch created an AMI factory utilizing vetted images, root volume encryption to satisfy security audit requirements and pre-staged essential services. The insurance firm is now able to run upwards of , cores while validating liquidity with no CAPEX investment.
The company has gained weeks and months by leveraging the 2nd Watch batch computing solution. Additionally, the company has gained valuable agility to rerun the modeling application if it times out or there is an error, while still meeting regulatory and client-agreed upon timelines. The valuable gain in flexibility, security and elasticity from the new environment has saved the company from having to invest in additional datacenters or colocation facilities.
We took the risk out of Risk Modeling.