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Solution

Amazon FinSpace

Data drives financial services organizations, Amazon FinSpace is a data management and analytics service for financial services industry (FSI) that reduces time to organize, prepare, and analyze large dataset.

Financial services organizations analyze data from internal data stores like portfolio, actuarial, and risk management systems as well as petabytes of data from third-party data feeds, such as historical securities prices from stock exchanges. It can take months to find the right data, get permissions to access the data in a compliant way, and prepare it for analysis.

Amazon FinSpace removes the heavy lift of building and maintaining a data management system for financial analytics. Using Amazon FinSpace, collect data and catalog it by relevant business concepts such as asset class, risk classification, or geographic region. Amazon FinSpace makes it easy to discover and share data across the organization in accordance with compliance requirements. It provides the capability to specify data access policies in one place and it enforces them while keeping audit logs to allow for compliance and activity reporting. Amazon FinSpace also includes a library of 100+ functions, like time bars and Bollinger bands, to prepare data for analysis.

Capgemini as an Amazon FinSpace Launch Partner

As a long-standing APN Consulting Partner and Amazon FinSpace launched partner, Capgemini helps financial services companies build and maintain data management system with Amazon FinSpace to advance and accelerate your data-powered journey. We help companies’ setup petabytes scale data catalogs along with executing ML models for analyzing this data.

Features of the solution

In capital markets, quantitative modeling is the practice of organizing and interpreting data sets with mathematical formulas to identify trends in the broader markets. Because raw data isn’t always decipherable, quantitative analysts will rearrange data into visual representations that communicate meanings and patterns.

Capgemini has built bespoke solution for our capital market clients on Amazon FinSpace – Quant Model Comparator. Capgemini’s Quant Model Comparator powered by Amazon FinSpace provides you with the capability to compare and analyze multiple trading quant models on large data sets. Leveraging rich user interface and reports, helping Portfolio Managers and Trade Analysts to effectively evaluate and select the best quant model for analysis and decision-making.

  • Find data with just a few clicks – Solution based on Amazon FinSpace makes it easy to store, catalog, and manage your data according to concepts common in the financial services industry like asset class and instrument type. 
  • Quant Model Comparison, Outlier Detection, LSTM Rebalancing – Highly effective platform to compare various Quant models used for trading. Additionally perform outlier detection and execute LSTM Rebalancing Model. Rich dashboard shows model comparisons reports. Fully deployed on AWS Infrastructure powered by Amazon FinSpace.
  • Integrations with other enterprise applications and AWS services like Amazon S3 and Lambda.

Common financial services challenges addressed

Multiple organizations use Quant modeling techniques to develop and implement market research and customer centric portfolio that deliver insight and drive business improvement.

The volume of the data needed for research and analytics from multiple sources is elevating from terabyte (TB) to petabyte (PB). Quant models are more effective on large historical datasets and provide better insights to portfolio managers, researchers and trade analysts.

Organizations compare various Quant models using various techniques to find out which model is more effective and better suited on the specific datasets. In today’s world these comparison techniques have many challenges and hinder the progress and results for analytics professionals.

Following are some of the challenges faced by financial service organization which are addressed by our solution:

  • Data Maintenance – Traditional data maintenance was challenging with respect to time and data accuracy.
  • Data Model Selection– Data capacity is growing at tremendous rate and companies are struggling to identify which model is good for Data Analysis before extracting data from multiple tables and queries.
  • Integration – Complex integration is a painful process to store and manage multiple data model comparison.
  • Manual Configurations– Many companies manually extract data software that requires a lot of manual configurations, which makes it a tedious process.  Quant Model provides upfront data schema and current data structure which help the user to make better decisions using Amazon FinSpace .
  • Scalability– Auto scaling facility. Data analysis using large data sets with multiple clusters is a complex and tedious job. Specially, analyzing multiple sophisticated models with historical/live data set. Quant Comparator Neural Network, model help easily to compare Supervised & Unsupervised learning.

Now capital market customers can leverage our Quant Model Comparator platform powered by Amazon FinSpace to examine two or more quant models in conjunction, based on one or more variables/indicators, to assess similarities and differences in data which help in the desion-making process to identify and capitalize on available trading opportunities in capital markets. Amazon FinSpace supports an organization’s compliance requirements by enforcing data access controls and keeping audit logs. 

To know more about the solution, please contact awsleadership.fssbu@capgemini.com.

Meet our experts

Sanjeev Gupta

Global Head, FS AWS COE Solutions
Sanjeev is a Global Head, FS AWS COE Solutions at Capgemini, an AWS Premier APN Partner. Having 18 years of experience in IT industry and worked on Cloud Technologies, Java, J2EE and Blockchain based applications. He likes to work on AWS solutions for various cloud use cases. He architected AWS migration strategies and Amazon Connect based Contact Center as a Service for major financial customers. Sanjeev is also an AWS Certified Solution Architect.

Hitesh Swami

Advisory & Consulting Technology – IT Strategy & Enterprise Architecture
Hitesh Swami, carrying global experience in Advisory & Consulting Technology – IT Strategy & Enterprise Architecture.  His areas of focus are Machine Learning and AI development in Banking Services & Health Care. Known for exceptional technical proficiency in Capital Market and Investment Banking.  He is AWS Certified Solution Architect with hands-on experience on Python, R, AWS and Amibroker trading tools (c++).  Worked on Margin, Derivative and Future for leading US and UK banks.