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Investment Banking - Ms Office Solution

Investment Banking

Today’s disruptions in the technology landscape, along with evolving market dynamics, customer behaviors, and competition are changing the fundamentals of banking, from keeping customers engaged today to making strategic decisions for tomorrow.

For retail banks, new technology doesn’t just affect how you reach customers—it reshapes your core offerings.From powerful mobile apps that can replace branches, to stronger fraud detection and smarter risk management decisions, modern banking is digital-first.

What is investment banking ?

An investment bank (IB) is a financial intermediary that performs a variety of services. Investment banks specialize in large and complex financial transactions, such as underwriting, acting as an intermediary between a securities issuer and the investing public, facilitating mergers and other corporate reorganizations, and acting as a broker and/or financial advisor for institutional clients.

Investment Banks as Financial Advisors

As a financial advisor to large institutional investors, the job of an investment bank is to act as a trusted partner that delivers strategic advice on a variety of financial matters. They accomplish this mission by combining a thorough understanding their clients' objectives, industry and global markets with strategic vision trained to spot.

Mergers and Acquisitions

Handling mergers and acquisitions is a key element of an investment bank's work. The main contribution of an investment bank in a merger or acquisition is evaluating the worth of a possible acquisition and helping parties arrive at a fair price. An investment bank also assists in structuring and facilitating the acquisition in order to make the deal go as smoothly as possible.

  • Stakeholder expectations and Dashboard objectives
  • Business context and constraints
  • Tools and technology choices
  • Internal and external data sources
  • Envisaging outputs/dashboard
  • Exploratory data analysis
  • KPIs used in industry, best practices
  • Analyses and KPIs to address business problems
  • Stakeholder sign-off on KPIs
  • Refresh frequency decisions
  • Map data sources to target database
  • Data cleansing and consistency checks
  • Data transformations and KPI construction
  • Load data into the target database
  • Mock-ups and wireframes
  • Revise with stakeholders
  • Build dashboard prototype
  • Conduct User Acceptance Testing
  • Release dashboard and schedule for updates

Case Studies

 

A Singapore based retail and banking company needed an enterprise data platform to support their top priority programs: transforming their loyalty program, improving store optimization, and meeting new regulatory requirements. Ms Office Solution built and launched a scalable and extensible data platform to meet current and future needs.

Our client operates more than 200 stores, and is comprised of multiple entities. They had an existing data warehouse solution built on Teradata in an on-premise location, that was struggling to meet their Business as Usual (BAU) needs. Any development work for data science or future analytical capabilities was de prioritized or cancelled as there was no additional storage or processing capacity available. In addition, the existing platform was not able to provide access to data in near real-time, as the data ware-house processed its batch jobs in a 24-hour cycle.,

A top-10 global investment management firm needed increased reliability, read/ write access, and usability for risk data. Ms Office Solution signed and tested a more efficient, scalable next-generation architecture to support the needs of future data growth and business demand.

MS OFFICE SOLUTION was engaged by a top-10 investment management firm to assist with migrating from MapR to a Cloudera-based platform, designed to enable increased reliability, read/write access, and usability for risk data. The client generates significant amounts of data each day, which must be stored and made available to a wide variety of tools and downstream systems. Data is being pulled daily into a custom?built in-memory structure that enables users to write user-defined functions (UDFs) and generate their own metrics on demand. Roughly 10,000 additional metrics are generated for users from this system. Legacy workloads are entirely hosted in Oracle, with dedicated specialized hardware. Despite optimized hardware, the legacy systems were no longer scalable. ,

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