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Big Data Analytics for Banking & Finance

Advanced analytics is redefining the banking and financial industry today. Innovators are using big data and analytics to differentiate their firms and gain an edge over the competitors by integrating risk into daily decision making and predicting customer behavior. Analytics as a service helps financial firms drive revenue, improve operational efficiencies and transform businesses at speed and scale.

Analytics for financial markets

Questions Quadratyx can answer

Quadratyx solutions for financial institutions

Analytics for insurance

  • How to make customers adhere to the bank more?
  • How can our loan officers control risk better at various levels?
  • Who are our more profitable customers and how does their worth add up?
  • How can we prevent fraudulent transactions from happening?
  • Which branches are performing better, by what margins and in which ways?
  • How to gain insight about customers from social media data?
  • How to validate identities, transactions and relationships more rapidly?
  • How to better detect, prevent, and prioritize cyber threats?
  • How to push profitability and improve business flexibility?
  • How to optimize employee compensation?
  • Develop personalized offers by analyzing all customer data.
  • Identify and thwart potentially fraudulent transactions by rapidly analyzing all available data and frequently updating risk models.
  • Improve customer service by providing the officers with instant access to valuable insights.
  • Protect themselves against cyber threats by building real-time insights into security to detect threats as they emerge and prevent them from doing any damage.
  • Build a lifetime valuation model for the most profitable customers.
  • How to generate investor insights cross-selling and up-selling?
  • How to enable financial advisors to provide deeper insights and added value to clients?
  • How to better enforce compliance to rules and stop fraudulent trading behavior in real-time?
  • How to guide the clients in portfolio optimization?
  • How to ensure real-time post-trade processing with end-to-end visibility of the process and minimal breakage?
  • How to develop a deeper, more accurate view of counterparty credit risk?
  • How to better monitor credit worthiness and changes in financial stability of the clients?
  • How to capture and maintain more details in credit, market and operational risk analyses?
  • How to improve retention of the customers and determine the most relevant offers for them?
  • How to use data and analytics to reach my customers with the same efficacy over various channels?
  • How to improve the claims processes, embedding advanced fraud detection into the processing?
  • How to leverage the Internet of Things to better service policyholders?
  • How to better underwrite the risk?
  • How to mitigate losses and pro-actively alert policyholders wherever possible?
  • How to gain more insight into the drivers of profitability in the business?
  • How to manage the risks in a better way, while ensuring compliance with regulations?

Our BFSI Clients

Scope of Hadoop and Big Data in finance

Fraud detection:

Fraud, financial crimes and data breaches are some of the most costly vulnerabilities in the industry. Hadoop analytics help financial organizations detect, prevent and eliminate internal and external fraud. A more rigorous analysis of points of sale, authorizations, transactions, and other data points help banks identify and mitigate fraud. For example, big data technology can alert the bank that a credit or debit card has been stolen by picking up on unusual behavior patterns.

Risk management:

Every financial firm strives to assess risk more accurately, and big data solutions can aid them in the effort by enabling them to evaluate credit exposures more effectively. Banks analyze transactional data to determine risk and exposure, scoring customers and potential clients. Hadoop solutions allow for a more comprehensive view of risk and impact, enabling firms to make the most informed decisions.

Contact center efficiency optimization:

Big data can help resolve customer problems quickly by allowing banks to anticipate customer needs ahead of time. Analysis of data at the contact center can offer the concerned managers concise insight in resolving the customer issues.

Customer segmentation for optimized offers:

Big data provides a way to understand customers’ needs at a granular level allowing for better personalization of offers. Detailed information about customers derived from social media and various online transactions can be utilized to serve the customers better.

Customer churn analysis:

Big data and Hadoop technologies can help financial firms retain more of their customers by analyzing behavior and identifying factors and patterns that lead to customer erosion.

Sentiment analysis:

Hadoop and advanced analytics tools help analyze social media data in order to monitor user sentiment of a firm, brand or product. Analytics on the fine-grained details could be insightful.

Customer experience analytics:

Insights garnered from portfolio management, customer relationship management, loan systems, contact center, etc., can help the firm to improve customer relationships on a long-term basis.