Financial Services


Globalization and digitization have significantly impacted all industries, forcing them to adopt newer business models. The financial services industry is no exception and digitization – omni-channel usage, mobile-friendly payment services, and mCommerce – has resulted in a tremendous increase in data volumes and variety. Coupled with this, financial institutions are also faced with the rising costs of regulatory compliance, threat of cybercrimes, customer churn, and competition from new entrants. Having said that, banks are also enjoying the many benefits of this data deluge - accurate customer insights for effective operations, increased connectivity with other enterprises, and opportunity to monetize data. This data evolution has not only made them competitive, but has also helped them to keep new entrants at bay – for now. Here are focus areas that financial service enterprises must invest in to win in the competitive market:

  • Upgrade systems and processes to report data accurately and in a timely manner to regulators. Non-compliance can lead to monetary fines, enforcement of supervisory actions, and reputational risks
  • Build systems that not only calculate risk at the transaction level, but also integrate them at the organization level. Scenario analysis and stress testing are other functions that use organization-wide risk data and are integral to the functioning of a modern financial institution
  • Provide personalized and real-time customer experience. This is why banks are now focusing on emerging areas such as social media to gauge customer reactions and accordingly manage their reputation and client satisfaction 
  • Implement increased measures for fraud detection and prevention.  This involves managing complex data and analytics
  • Derive deeper insights into portfolio performance, liquidity positions, and risks. To achieve this, banks must also build self-service capabilities to analyze payments, liquidity patterns, and working capital requirements
  • Develop new business models that connect with enterprises for better business results. For instance, banks could analyze the financial spend data of the client, provide real-time offers or next-best offers, integrate it with offers from retailers and media companies, and communicate it to the client in real-time by collaborating with telcos
  • Implement data integration capability to connect multi-bank relationships, trade, supply chains, and lending data, for a consolidated view across asset classes, currencies, risks, and cash positions
  • Optimize delinquency models that can predict the probability of loan default over the next three to six months, so that they can initiate proactive steps to minimize risk
  • Renew focus on optimization and operational efficiency using analytics. This requires a robust data management and risk management framework to improve oversight and compliance
  • Invest in big data analytics, supported by cloud-based platform solutions for more reliable predictive analytics on customer spending behavior, enabling organizations to design more suitable loyalty programs

Service Offerings

We have a wide range of solutions that address different challenges across the financial services ecosystem. Our High Performance Analytics solution manages voluminous data and provides insights to improve customer experience and operational efficiency, while also generating additional revenue. Our visual analytics capabilities analyze financial transactions trends and discover new patterns. The master data management (MDM) solution creates a 360-degree view of customers and enables the creation of personalized offers. At the same time, our next-gen automation solution reverses the change-the-bank to run-the-bank ratio by driving down the run-the-bank cost.

Success Stories

Enabled compliance with regulatory laws in the areas of AML, FATCA, Dodd-Frank, Basel-II, and Basel-III by offering a global and standardized solution with High Performance Analytics services for multiple financial services firms. Our solutions are based on big data and traditional data warehouses

High Performance Analytics Services for Multiple Financial Services Firms

Reduced next-best-offer execution time from 12 weeks to one through High Performance Analytics solution for a major financial services firm in the USA

Analytics Solution for Major Financial Services Firm in the USA

Improved analysis time by 20% by automating the anti-money laundering (AML) engine of a global bank dealing with approximately 1 billion transactions. Also filed a joint patent with the client for improving historical transaction in enterprise data warehouse (EDW)

Improved Analysis Time by Automating AML Engine of a Global Bank

Created a 360-degree view of customers to derive insights by implementing a customer MDM solution for multiple financial services clients and sourcing data from 50+ source systems and 4000+ entities. It has helped a client reduce operational costs by US $720,000 annually

Big Data Analytics in Financial Services
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