Data science is drawing the attention of all the Financial organizations around the globe and 90% of the organization thinks that successful Data Science initiatives will define them as winners in the future. Financial Services and Banking is leading in applying data science techniques. There are multiple internal data sources in banks – inclusive of databases, Data Warehouses, XML data, enterprise applications like ERP and CRM. Banks also have a large amount of social media data of their customers in the form of tweets, Facebook wall posts, website visits, streams, searches videos, etc. Today’s high-end technologies make it possible to realize the value of Data.

Data Analytics helps the retail leaders, financial traders, large banking firms, many funding organizations in terms of

  • Trade Analytics
  • Trading analysis of high-frequency business
  • Decision analysis of pre-trading support
  • Sentiment calculations
  • Predictive analytics
  • Risk analysis for anti-money laundering
  • Enterprise risk demand
  • Fraud mitigation

Fraud detection & Forecasting:
Fraud detection protects customer and enterprise information, assets, accounts, and transactions through the real-time, near-real-time or batch analysis of activities by users and other defined entities (such as kiosks). It uses background server-based processes that examine users’ and other defined entities’ access and behavior patterns and typically compares this information to a profile of what’s expected. Fraud detection is not intrusive to a user unless the user’s activity is suspect.

  • Fraud Expense Plan
  • Credit Risk Analysis
  • Targeted marketing & offer acceptance rate
  • Crypto Currency Prediction
  • Crypto Market Analytics
  • Crypto Exchange Analytics

Industry Coverage:
Banking: Core Banking, Corporate Banking, Retail Banking, Investment banking
Financial Services: Crypto Exchange, Stock Broking, Mutual Funds, Payment Gateways
Insurance: Life Insurance, general Insurance