Contact Request Demo
Digital Business

Using the cloud in the financial services sector

• By Naveen Joshi • 1:01 am

What should (or shouldn’t) a financial services organization put on the cloud? We find out what companies are doing currently and what the future seems to hold.

The challenges

Firms are apprehensive about their data being compromised on a public cloud computing service or the monetization of their customer data by cloud vendors. A company’s willingness to adopt the cloud in financial services has much to do with whether the cloud services offer specific functions and features critical to users. In a 2015 global study by Cloud Security Alliance, 43% companies surveyed considered public breach notification as one of the top obstacles in adopting cloud. There is a myth that public and private clouds are a hacker’s dream, and that we should not move to the cloud due to security vulnerabilities. The reality is that if financial services firms do just a bit of planning, most public or private cloud computing service systems should provide better security than your applications and data currently enjoy. At the same time, you upgrade security, you can install a data and service governance approach and system that will provide better managed and controlled IT infrastructure. It boils down to four things:

  • Awareness of your regulatory and data privacy obligations
  • Clear policy and governance to meet those obligations
  • The right technology to protect your data
  • The right systems to control data use and enforce policies.

With these four things in place, cloud computing can prove a major win for the financial services organizations that adopt it.

Current Scenario

Cloud computing has caused more debate than many other recent technological advancements. Regardless, there has been a tremendous rise in its adoption by financial services firms over the last couple of years. Some of the prominent examples are given below.

NYSE Euronext Capital Markets Community Platform

Recently, NYSE Euronext launched a PaaS community cloud in financial services industry, aimed at brokers, dealers, hedge funds and other market makers. The platform has been set up to host customer applications and services. Electronic trading, market data analysis, algorithmic testing and regulatory reporting were included in it. The infrastructure consists mainly of storage and virtualization tools from EMC and VMware, running on Xeon-powered blade servers.

NASDAQ OMX Data on-demand

This SaaS cloud service, built with the support of Xignite, provides easy and flexible access to massive amounts of historical level 1 tick data. It’s a web application that allows users to purchase data online and access it using an application programming interface (API) or as plain text files.

CME Clearport OTC Data on-demand

This on-demand SaaS web service is also built on top of the Xignite platform and offers access to end-of-day OTC settlement, volume and open interest data to support markets available through CME clearport.

I-Banks using cloud for risk analysis and non-core processes

Now a part of Bank of America, Merrill Lynch used IBM iDataPlex servers as part of an IaaS strategy to build and evaluate risk analysis programs. The servers turn many separate computers into a pool of shared resources, i.e., a cloud. Morgan Stanley uses PaaS cloud vendor Force.com for its recruiting applications and has extensive cloud penetration in analytics and strategy.

Related posts:

  1. The different ways of using pricing algorithms The reality of our world is that the pricing for almost all products, services and experiences has already become, or is in the process of becoming, real-time and dynamic. Pricing algorithms are helping organizations maximize their revenue and identify the optimal price of a product or service.
  2. When bankers go tech The Internet of Things seems to be all encompassing. Startups are cropping up in all parts of the world. Curiosity is driving market game globally. Digital transformation is replacing humans with computers. Innovation labs and venture funds are creating the right environment for risk taking.
  3. Machine learning for anomaly detection In data mining, anomaly detection is referred to the identification of items or events that do not conform to an expected pattern or to other items present in a dataset. Typically, these anomalous items have the potential of getting translated into some kind of problems such as structural defects, errors or frauds. Using machine learning for anomaly detection helps in enhancing the speed of detection.
  4. 3 ways technologies are helping us save the environment Saving environment with technology is helping us build better ecosystems or remedy the damages done to the environment by employing both organic and inorganic techniques for a cleaner planet.

Related Articles

Leave a Comment

Your email address will not be published. Required fields are marked *

CAPTCHA

*