Sizing your investments
Industry Challenge

The finTech industry is booming, making it imperative for financial institutions to collect, organize, analyze and make data insightful and action-driven. The sector needs intelligent and innovative data scientists who can make sense of customer and financial data for predictive analysis, forecasting, and analysis for risk and compliance more effectively.

Business Need

Data Science has been a game-changer for the Finance Industry in performing Financial Management; it is primarily used in algorithmic trading, fraud detection, customer management, risk analytics, and risk management.

Advantage Rubiscape

Rubiscape helps FinTech firms in deploying ML and AI for analyzing their financial data across multiple business functions. Today, it is popularly employed for customer portfolio management, identifying unusual transactions, and fraud detection.

Cumulative growth for years to come

Sentiment Analysis

NLP techniques maps customer sentiments by analyzing data from Online Reviews, Social Media Content, and Claims Processing to improve marketing strategies and enable risk management.

Portfolio Management

AI and ML techniques help investment managers analyze fundamental and alternative data sets to identify new investment opportunities.

Automated Risk Management

Automation in risk management helps risk experts detect new risks and assess their impact on customer engagement and the organization’s brand identity.

Algorithmic Training

The amalgamation of AI in algorithmic trading also allows faster and easier order execution to traders and investors. They can quickly book profits on small price changes & enjoy benefits.

Deep Personalization

The data collected from engagement with an individual consumer can help brands accelerate deep personalization to enhance customer retention.

Cross- and Up-Selling

Use ML analytics to identify the target customers, the best offers, and the precise time to leverage cross-selling and up-selling opportunities.

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